We study a dynamic equilibrium model in which investors disagree on future volatility and trade volatility derivatives to hedge stock positions and speculate. On average, volatility disagreement makes the variance risk premium more negative. However, volatility trading enables a risk transfer among investors that turns the variance risk premium positive when the market underestimates future volatility. Under higher volatility, investors trade fewer volatility derivatives as these become too risky. These economic mechanisms shed light on empirical regularities during market turmoil. Volatility disagreement also lowers the stock market valuation, increases market volatility, and generates time-variation in the leverage effect.

We argue for the introduction of firm-level emission futures contracts as a novel way of assessing the real impact of ESG initiatives. Our measure is based on the forward-looking market-based valuation of firm-level CO2 emissions. We establish both theoretically and empirically that backward-looking subjective ratings are limited to the extent that they fail to capture future reductions in emissions. We show evidence that although lower emissions have predicted higher E ratings, higher E ratings have predicted higher, not lower, emissions. As such, by following these subjective ratings, investors may have inadvertently allocated their money to firms that pollute more, not less. We discuss several applications of our new measure, including executive pay and investment management.

The rapid advancement in artificial intelligence (AI) and computing power has significantly elevated the value of data, making it a critical asset for firms. I measure firms' value of data and consumers' privacy preferences by analyzing the supply and demand-side reactions to the EU’s General Data Protection Regulation (GDPR). After GDPR limits firms’ access to data, the EU sales share of US data-intensive firms declines by 8%. EU consumers, who can choose to share less data, suffer a 6% deterioration in user experience as measured by app ratings. I develop a general equilibrium model to map these empirical findings and estimate the value of data and privacy. Privacy-conscious consumers gain from privacy protection. However, the quantitative model reveals that the digital welfare of other consumers declines because firms also use data to enhance productivity and customize digital products. In aggregate, EU digital welfare declines by 4%, and US digital welfare declines by 1.4%.

I study whether companies strategically utilize captive financing, a form of providing funding to consumers, to manage product demand. Using detailed data on auto loans, I show that captive lenders alter the financing terms and lending standards throughout the product life cycle. They reduce interest rates, allow longer maturity, charge lower down payments, and relax loan standards (1) when the underlying car models become outdated; (2) when competitors release new models; and (3) when they experience exogenous shocks such as recalls. While the lower interest rates offered by captive lenders reduce the likelihood of consumer default in the short term, the average default rate eventually increases in the long horizon because captive lenders’ willingness to dispense higher-risk loans allows more subprime borrowers to access credit. For consumers who cannot find a loan from non-captive lenders, borrowing from captive lenders helps them in purchasing a car, but they could potentially be approved for a loan they cannot afford. These findings collectively suggest that captive financing is a tool manufacturers use to boost car sales throughout the product life cycle, while this tool could induce overleveraging by consumers.

We find that the flightiness of depositors displays pronounced fluctuations over time, reaching unprecedentedly high levels after the Covid-19 crisis. Elevated deposit flightiness coincides with low interest rate environments, expansions in central bank reserves, and a disproportionate increase in corporate deposits. Our dynamic model rationalizes these trends based on heterogeneity in investors’ convenience value, where those in the banking system value the convenience benefits of deposits more. Following deposit inflows from outside investors, e.g., due to QE's reserve expansions, the marginal depositor becomes more rate-sensitive and the risk of panic runs increases. Our findings imply that the risk of panic runs triggered by policy rate hikes is amplified when the Fed’s balance sheet size is larger, highlighting a novel linkage between conventional and unconventional monetary policy.

We study the real impacts of capital regulation caused by the reallocation of mortgage servicing rights (MSRs). Using U.S. credit registry data, we show that Basel III’s stricter MSR regulation induced banks to transfer riskier MSRs, leading to a market- wide shift toward non-bank servicers. We develop a model showing that the privately optimal allocation of MSRs may not minimize agency conflict in a non-integrated mortgage market. Comparing foreclosure rates, a sufficient statistic for welfare in the model, we show that the reallocation of MSRs decreased agency conflicts and enhanced investor welfare at the expense of borrowers.

This study investigates how U.S. firms respond to geopolitical tensions by reorganizing their global supply chains and how CEO partisanship shapes such responses. Firms reduce import from foreign countries with diverging ideologies from the U.S., more so by firms whose CEOs are politically aligned with the U.S. administration. Following foreign elections that increase ideological distances, aligned CEOs cut imports from election countries by 40% more than misaligned ones. Potential mechanisms include aligned CEOs having heightened concerns for geopolitical risk and national security, and demonstrating support for the administration. These politically driven import decisions significantly reduce firm value and performance.

I explore how venture capital firms (VCs) become more influential in their co-investment networks. While we know that well-connected VCs perform better, we do not fully understand how less-connected VCs can improve their position. Using data from US VC investments (2010-2021, 4.5 million connections), I study three ways VCs can become more central: joining deals with influential VCs, investing in their portfolio companies, or getting them to invest in your portfolio companies. The results show that getting top VCs to invest in your portfolio companies is most effective way to increase network influence. This boost happens whether or not the company connecting the VCs is successful, suggesting that connections to prominent VCs matter more than immediate financial returns. I employ k-shell decomposition and dynamic network analysis to track changes in investors’ positions and quantify influence, Granular Instrumental Vari- ables and Triple Difference analyses to study the different connections’ impact.

What is the role of belief heterogeneity in shaping the equity term structure? We address this question by developing a general equilibrium model with habit formation in consumption and heterogeneity in both risk aversion and beliefs about the expected growth rate of the aggregate endowment. We demonstrate that the effects of belief heterogeneity are countercyclical, increasing equity yields during recessions and reducing them during expansions. These effects are also more pronounced in recessions than in expansions and are stronger for short-term assets than for long-term assets. Our findings underscore the significant role of belief heterogeneity in determining equity yields.

We study who owns coal power plants -- the largest single source of carbon emissions -- in Europe. A sharp increase in private firms' ownership was met by a large decline in public equity ownership. This decline was not driven by public equity investors selling plants, but by their scaling down of plants quickly. State investors played a crucial role, selling to private firms and slowly scaling down their plants. We calibrate a model in which asset owners vary in how they value externalities. Nationalization by state investors that value social factors (jobs, ``energy security'') can hinder ``green finance'' in decreasing emissions.

This paper studies the impact of employee output information disclosure through GitHub on labor reallocation towards large firms. GitHub, which is the world’s largest software management platform, tracks and publicly displays real-time individual contributions. In 2016, a policy change enabled GitHub users to display their contributions more accurately on their profiles. Following this update, employees with 1 standard deviation higher GitHub contributions witnessed a 5.7% increase in job transitions to large firms, predominantly at the expense of smaller companies. While productive individuals left small firms for senior roles in larger companies, the latter retained them through internal promotions. The departure of productive workers led to an overall reduction in employment growth and productivity for small firms with more productive employees prior to the shock. Our findings highlight the role of labor-related big data in amplifying the dominance of large firms in recent years.

Using data from TransUnion, a large U.S. credit bureau, we analyze whether and how cuts in bank income taxation are passed through to the interest rates and size of consumer loans. Exploiting the change in bank corporate income taxation from the Tax Cuts and Jobs Act and utilizing tax-exempt credit unions as a control group, we find that corporate tax cuts lead to lower interest rates for consumers obtaining auto loans from banks. We also find greater pass-through for individuals with higher credit quality. We develop a parsimonious model to identify the economic mechanisms influencing the pass-through of corporate tax cuts to interest rates. Our empirical tests reveal that pass-through declines with banks' market power and leverage, while we find only a limited role for selection in consumer credit markets.

This paper examines deposit stickiness using account-level data from over 10 million accounts across 152 U.S. credit unions. We find significant skewness in deposit distributions, with 10% of depositors controlling 70% of total deposits. Aggregate deposit stickiness is driven by high-balance depositors. Using unexpected changes in Fed Funds rates as exogenous variation in the opportunity cost of holding deposits, we show that low-balance depositors are sensitive to changes in interest rates, but high-balance depositors are not. High-balance depositors are also relatively insensitive to discontinuous interest rate jumps at specific balance thresholds and are more likely to experience periods of prolonged inactivity followed by large reductions in account balances. Our evidence suggests that deposit stickiness is driven by relatively few high-balance accounts that are used as liquidity pools rather than for long-term savings.

Mortgage markets are central to monetary policy transmission. We show that this is because monetary policy impacts the supply of mortgage credit by the two largest mortgage holders: banks and the Federal Reserve. The Fed's supply of mortgage credit consists of buying or selling mortgage-backed securities (MBS) under its quantitative easing and tightening (QE and QT) programs. Banks' supply of mortgage credit is driven by the deposits channel of monetary policy. Under the deposits channel, when the Fed lowers rates, banks receive large inflows of deposits. They invest these deposits in long-term fixed-rate assets, in particular MBS, to match the interest-rate sensitivity of their income and expenses. The deposits channel reverses when the Fed raises rates: deposits flow out and banks sell MBS. Through the combined effect of QE/QT and the deposits channel, monetary policy drives mortgage rates, mortgage originations, and residential investment. We show that QE/QT and the deposits channel played a large role in the expansion and contraction of mortgage credit during the 2020--24 monetary policy cycle. Our results imply that monetary policy will continue to operate through these channels in future cycles.

We study the extent to which individuals’ consumption decisions are influenced by firms' stances on controversial social issues and the implied incentives for firms to take such stances. We use transactions from a major payment card company to predict cardholders' likely social alignment with firm stances and to quantify effects on consumption. The social stances taken by firms increase revenue on average, with significant heterogeneity across consumers and firm stances. Consumers most aligned with a firm’s social stance increase their consumption at the firm by 19 percent in the month following widely known social stance events, and consumers most opposed to the firm's stance decrease their consumption by 11 percent. These diverging consumption responses attenuate over time but persist even a year later. Firms tend to take stances that align with their consumers' and employees' social preferences and that correlate with the firm’s ownership structure. Together our results show that consumers meaningfully respond to their social alignment with firms, and that this consumer response can incentivize profit-maximizing firms to engage with social issues.

The central tension in securities regulation is between protecting investors and enabling broad capital formation. Focusing on VC fund managers, we study key tools of investor protection in private markets: enforcing relationship-based fundraising and restricting eligible investors. A new policy permitting public advertising is disproportionately used by less well-networked, underrepresented fund managers and is less sensitive to local conditions. Yet it has limited take-up because track record matters at arm’s length while strong networks matter in relationship financing; underrepresented managers more often have neither. Arm’s length fundraising also imposes costs to accessing the “crowd” and verifying investors, inducing negative signaling.

This paper studies the implications of a simple theorem, which states that for arbitrary underlying dynamics and diffusive information flows, the cumulants of Bayesian beliefs have a recursive structure: the sensitivity of the mean to news is proportional to the variance; the sensitivity of the $n$th cumulant to news is proportional to the $n+1$th. The specific application is the US aggregate stock market, because it has a long time series of high-frequency data along with option-implied higher moments. The model qualitatively and quantitatively generates a range of observed features of the data: negative skewness and positive excess kurtosis in stock returns, positive skewness and kurtosis and long memory in volatility, a negative relationship between returns and volatility changes, and predictable variation in the strength of that relationship. Those results have a simple necessary and sufficient condition, which is model-free: beliefs must be negatively skewed in all states of the world.

This paper examines the impact of climate-driven disasters on commodity prices. Using extensive archival sources including census data and declassified CIA intelligence reports, I develop a global geospatial dataset to identify the locations of key commodity-producing sites at subnational level since the 1970s. By linking these regions to climate disaster events, I find that, over time, production has become increasingly concentrated in high-risk areas. Leveraging this dataset, I analyze how commodity futures respond to climate-driven shocks and uncover significant cross-sectional differences. Specifically, a long-only portfolio of vulnerable commodities yields a significant monthly alpha of 0.90%, whereas that of resilient commodities, albeit still significant, is negative at –0.43%, reflecting a premium paid for protection against climate shocks. Furthermore, I find that vulnerable commodities experience slower recoveries from past shocks.

How do inaccurate beliefs about the distribution of interest rates affect search and outcomes in consumer credit markets? In collaboration with Chile's financial regulator, we conducted a randomized controlled trial with 112,063 loan seekers where we showed treated participants a price comparison tool that we built using administrative data on the universe of consumer loans merged with borrower characteristics. The tool shows loan seekers a conditional distribution of interest rates based on similar loans obtained recently by similar borrowers. We find that consumers thought interest rates were lower than they actually were, and the price comparison tool caused them to increase their expectations about the interest rate they would obtain by 56%. Consumers also underestimated price dispersion, and our price comparison tool caused them to increase their estimates of dispersion by 69%. The price comparison tool did not cause people to search or apply at more institutions, but it did cause them to be 39% more likely to negotiate with their lender, to receive 14% more offers and 11% lower interest rates, and to be 5% more likely to take out a loan. We also cross-randomized whether we asked participants their beliefs about the distribution of interest rates, and find that merely asking these questions led them to search at 4% more institutions and obtain 9% lower interest rates.

We study the dynamic effects of interest rate shocks on small business lending by banks

and non-banks, through the lens of three channels affecting banks at different horizons:

core deposits, time deposits, and bank profitability. Using a shift-share design within a

dynamic panel setting, we show that rate cuts stimulate bank lending relative to non-

bank lending in the first year, in part through an inflow of core deposits, but migration

effects towards non-banks arise after three years of net interest income compression and

time deposit outflows induced by low rates, and get stronger thereafter. Our results

bridge the gap between the expansionary short-run effects and contractionary long-run

effects of low interest rates on bank lending. We highlight that substitution towards

non-banks can take place at business cycle frequencies, and should thus be taken into

account in the conduct of monetary policy.

Mortgage structure matters not only for monetary policy transmission, but also for financial stability. Adjustable-rate mortgages (ARMs) expose households to rising rates, increasing default risk through higher payments, while fixed-rate mortgages (FRMs) protect households but potentially expose banks to greater interest rate risk. To evaluate these competing forces, we develop a quantitative model with flexible mortgage contracts, liquidity- and net worth-driven household default, and a banking sector with sticky deposits and occasionally binding constraints. We find financial stability risks exhibit a U-shaped relationship with mortgage fixation length. FRMs benefit from deposit rate stickiness, reducing volatility, whereas ARMs provide net worth hedging by concentrating defaults when intermediary net worth is high, thus lowering risk premia. An intermediate fixation length balances these effects, minimizing banking sector volatility and improving aggregate risk-sharing. Our model explains observed differences in delinquencies, house prices, and bank equity prices between ARM and FRM countries during 2022–2023, with implications for mortgage design, macroprudential regulation, and monetary policy.

Using rich hand-collected data, we examine how corporations use carbon offset credits issued by third-party developers to claim emission reductions. Larger firms with higher institutional ownership and net-zero commitments tend to use offsets. However, offsets are used intensively in low-emission industries. After an exogenous ESG rating downgrade, triggered by a leading ESG rating agency’s methodology change, low-emission firms retire larger quantities of cheap, low-quality offsets while heavy emitters decarbonize more in-house. Our findings are consistent with a separating equilibrium where firms choose whether to outsource their transition efforts, but also with firms using offsets strategically for certification and ranking benefits.

Venture capital firms (VCs) and underwriters form crucial connections that guide startups to public markets. This paper provides causal evidence supporting this claim and quantifies the value of these connections. Using plausibly exogenous variation from underwriter mergers and closures, we show that disrupting established VC-underwriter relationships impacts VCs' exit activity. After such relationship losses, VCs experience a 9.5% decrease in IPO exits and weaker fund performance. This suggests these horizontal relationships constitute valuable, relationship-specific organizational capital that generates value beyond the independent reputations of the intermediaries involved. Consistent with these relationships heavily relying on human capital, we find high post-merger employee turnover at underwriters strongly predicts relationship discontinuation. On average, only 23.4% of relevant personnel remain after mergers. Our findings demonstrate how specific intermediary networks shape capital formation and reveal important spillover effects of financial sector consolidation on entrepreneurial finance outcomes.

We propose a statistical model of heterogeneous beliefs where investors are represented as different machine learning model specifications. Investors form return forecasts from their individual models using common data inputs. We measure disagreement as forecast dispersion across investor-models (MFD). Our measure aligns with analyst forecast disagreement but more powerfully predicts returns. We document a large and robust association between belief disagreement and future returns. A decile spread portfolio that sells stocks with high disagreement and buys stocks with low disagreement earns a value-weighted return of 14% per year. Further analyses suggest MFD-alpha is mispricing induced by short-sale costs and limits-to-arbitrage.

Residential Property Assessed Clean Energy (PACE) loans allow homeowners to fund investments in green residential projects through their property tax payments. We collect new PACE loan-level data and develop a novel approach to recover households’ home improvement investment decisions from permit descriptions. PACE projects are capitalized into home values, but expansions of the property tax base are partially offset by an uptick in tax delinquency rates among borrowers. Lenders in PACE-enabled counties expand mortgage credit access, indicating improved recovery values despite a PACE lien’s super seniority. Overall, PACE adoption increases local fiscal income while improving climate-proofing of the housing stock.

We investigate the technical and ethical standardization of artificial intelligence (AI) and its corporate implications around the globe. We show that AI standards not only fuel AI investments but also broader capital and R&D spending. Standardization of machine learning methods, programming languages, protocols for big data, guidelines for data interchange, and interoperability of industrial data encourage investment. Conversely, frameworks for societal and privacy considerations in AI discourage investment. AI standards have a positive ripple effect on firm value, gradually amplifying as the standards mature and their impact on firms deepens.

The rapid rise in student loan balances has raised concerns among economists and policymakers. Using administrative credit bureau data, we find that nearly half of the increase in balances from 2010 to 2020 is due to deferred payments, largely driven by the expansion of income-driven repayment (IDR) plans, which link payments to income. These plans help borrowers by smoothing consumption, insuring against labor income risk, and reducing the present value of future payments. We build a life-cycle model to quantify the welfare gains from this payment deferment and the channels through which borrower welfare increases. New, more generous IDR rules increase these transfers from taxpayers to borrowers without yielding net welfare gains. By lowering the average marginal cost of undergraduate debt to less than 50 cents per dollar, these rules may also incentivize excessive borrowing. We demonstrate that an optimally calibrated IDR plan can achieve similar welfare gains for borrowers at a much lower cost to taxpayers and without encouraging additional borrowing, primarily through maturity extension.

I show that bank capital regulation lowers long term interest rates by increasing banks’ holdings of long term government bonds, effectively acting as an unconventional monetary policy. I study the implementation of bank capital requirements, and their subsequent relaxation in 2018, to show that stricter capital requirements caused banks to shift their portfolios toward long term government bonds. I develop a quantitative bank portfolio choice model where capital requirements modify banks’ interest rate risk management problem to make long term government bonds a more valuable hedge. The model features costly bank deposit franchises, countercyclical loan losses, and inelastic asset markets to study equilibrium effects on long term interest rates. Using the model, I find that stricter capital requirements caused long term interest rates to fall by 47 basis points. I demonstrate that countercyclical central bank policies dampen the baseline effect on long term rates and crowd-in bank lending.

Over the past decade, the share of commercial real estate (CRE) loans held by regional banks increased significantly. Today, these banks are the largest holders of commercial mortgages with more than one third of U.S. commercial mortgage dollars on regional bank balance sheets. Recent declines in commercial property valuations have raised concerns that regional banks' substantial loan exposure to this asset class may cause fractures in the banking system and spill over to the wider economy. Despite the risk of such externalities, the role of regional banks in this market has received little attention so far. We characterize bank's expansion over the last ten years and argue that regional banks' expansion into markets outside their local expertise face heightened risks.

We propose that public investors react differently to patent issuance depending on its novelty, and these misreactions exert real impacts on the firms' future innovations. First, using textual analyses of patent documents to measure patent novelty, we find that investors underreact to the issuance of path-breaking innovations while overreact to the trend-following ones. Novel patent issuance predicts lower risk but positive forecast errors, consistent with a non-risk-based novelty mispricing mechanism. A bounded-rationality model, where investors cannot figure out the true novelty of a patent at issuance due to cognitive limits, explains the empirical patterns well. Second, using exogenous distraction shocks, such as sensational news, we present causal evidence that after disappointing returns, novel firms shift from creating and following up on novel innovations to copycatting. The findings highlight that investors' misreactions to patent novelty impact firms' future innovation directions by steering them away from higher-valued, groundbreaking research.

We hypothesize and find evidence that banks use venture investments in fintech startups as a strategic approach to navigate fintech competition. We show that banks’ venture investments have increasingly focused on fintech firms in systematic ways. We find that banks facing greater fintech competition are more likely to make venture investments in fintech startups. Banks target fintech firms that exhibit higher levels of asset complementarities with their own business. Finally, instrumental variable analyses show that venture investments increase the likelihood of operational collaborations and knowledge transfer between the bank investor and the fintech investee.

We combine a customized survey and randomized controlled trial (RCT) to study the effect of higher-order beliefs on U.S. retail investors’ portfolio allocations. We find that investors’ higher-order beliefs about stock market returns are correlated with but distinct from their first-order beliefs. Furthermore, the differences between the two vary systematically according to investor characteristics. We use information treatments in the RCT to create exogenous differential variations in first- and higher-order beliefs. We find that an exogenous increase in first-order beliefs increases the portfolio share allocated to the stock market (risky assets), while an exogenous increase in higher-order beliefs reduces it

This study investigates the prevalence and severity of under-insurance among U.S. households using comprehensive new microdata from BlackKnight McDash that links homeowners insurance and mortgage information from 2013-2023 nationwide for 100 million borrowers. We document widespread under-insurance on the intensive margin, particularly among borrowers in high climate risk states, with low credit scores, and high loan-to-value ratios. We examine the role played by household credit constraints by showing that households respond to quasi-exogenous premium increases by both dropping coverage as well as increasing mortgage debt, showing that mortgage credit is used to finance insurance purchases. These results imply that rising premiums increase risks in the mortgage market in two ways, first by inducing households to drop coverage, and second by increasing household leverage. Lastly, we study the broader impacts of rising premiums under-insurance on household financial resilience and default risk.

How good is AI at persuading humans to perform costly actions? We study calls made to get delinquent consumer borrowers to repay. Regression discontinuity and a randomized experiment reveal that AI is substantially less effective than human callers. Replacing AI with humans six days into delinquency closes much of the gap. But borrowers initially contacted by AI have repaid 1% less of the initial late payment one year later and are more likely to miss subsequent payments than borrowers who were always called by humans. AI’s lesser ability to extract promises that feel binding may contribute to the performance gap.

Student loan forgiveness has been proposed as a means to alleviate soaring student

loan burdens. Who benefits from loan forgiveness, and how does it affect borrowers? This paper uses administrative credit bureau data to study the distributional, consumption, borrowing, and employment effects of the largest event of student loan forgiveness in history. Beginning in March 2021, the United States federal government ordered $132 billion in student loans cancelled, or 7.8% of the total $1.7 trillion in outstanding student debt. We find that student loan forgiveness led to increases in mortgage, auto, and credit card debt by 9 cents for every dollar forgiven. Borrowers’ monthly earnings and employment fall. The implied Marginal Propensities for Consumption (MPC) and Earnings (MPE) are 0.27 and 0.53, respectively.

Exploiting large cross-sectional variation in exposure to securities losses across euro area banks, we show that banks more negatively exposed to the July 2022 monetary policy tightening lost access to the interbank market and cut lending, irrespective of whether the securities were booked at market or historical value. Importantly, banking groups provide liquidity to domestic subsidiaries, reducing the impact of their security losses on corporate lending, but foreign subsidiaries of banking groups and stand-alone banks with more significant securities losses reduced lending to corporate borrowers to a larger extent. Our results show how differences in banking structure can contribute to an uneven transmission of monetary policy.

We show that banks “extended-and-pretended” their impaired CRE mortgages in the post-pandemic period to avoid writing off their capital, leading to credit misallocation and a buildup of financial fragility. We detect this behavior using loan-level supervisory data on maturity extensions, bank assessment of credit risk, and realized defaults for loans to property owners and REITs. Extend-and-pretend crowds out new credit provision, leading to a 4.8–5.3% drop in CRE mortgage origination since 2022:Q1 and fuels the amount of CRE mortgages maturing in the near term. As of 2023:Q4, this “maturity wall” represents 27% of bank capital.

We study how stakeholder orientation impacts firm management and performance. We exploit

state-level law changes governing the conversion of hospitals from nonprofit to for-profit and find that for-profit orientation reduces hospital spending on emergency rooms and Medicaid patients, while increasing focus on revenue and a↵ecting investment decisions. Consistent with spillovers, nonprofit hospitals located near converting hospitals experience increased emergency room visits and expenditures. We investigate governance channels that align corporate behavior with stake-holders and find that converted for-profit hospitals adjust their boards by replacing MDs with MBAs, and that the tax code is a major source of governance for nonprofits.

We develop a model to examine how aggregate discount rates affect the nature and composition of innovation within an industry. Challenging conventional wisdom, higher discount rates do not discourage innovation when accounting for the industry equilibrium. Higher discount rates deter entry---effectively acting as entry barriers---but encourage innovation through the intensive margin, which can lead to a higher industry innovation on net. Simultaneously, high discount rates foster explorative over exploitative innovation. Our predictions strengthen in industries with higher exposure to systematic risk, for which the negative impact of discount rates on entry is stronger.

We study the equity, Treasury bond, and corporate bond risk premium expectations of asset managers, investment consultants, wealth advisors, public pension funds, and professional forecasters. Consistent with conventional rational expectations asset pricing models, subjective risk premia vary one-to-one with objective risk premia that are available in real time and countercyclical (i.e., high in recessions and low in expansions). Despite their significant time-series variation, several subjective equity premia vary more in the cross-section than in the time series. We tie this heterogeneity in subjective equity premia to heterogeneous priors about long-term valuations. Overall, the results are consistent with the notion that time-varying risk premia are the key drivers of asset prices.

Firms increase climate-friendly investments if they perceive that the cost of green capital is lower than that of brown capital. We show that green and brown firms perceived their cost of capital to be the same before 2016, but after the post-2016 surge in sustainable investing, green firms perceived their cost of capital to be on average 1 percentage point lower. This difference has widened as sustainable investing and preferential capital allocation to green firms by governments have intensified. Within some of the largest energy and utility firms, managers have started applying a lower cost of capital to greener divisions. The observed changes in the perceived cost of green capital incentivize cross-firm and within-firm reallocation of capital toward greener investments.

Nearly half of decentralized autonomous organizations (DAOs) allow vote delegation to facilitate user participation in governance and decision making. Yet, how well this mechanism works is largely unknown. We evaluate the efficacy of the vote delegation scheme by examining token holders’ vote delegation decisions and delegates’ voting behavior in MakerDAO, a pioneering and foundational DAO protocol. We find that token holders are able to discern delegates’ actions and reward delegates acting in their best interest with more delegated votes. Delegates vary in their incentives and expertise, which influence how they vote on proposals. Delegates whose interests are more aligned with token holders and who possess more expertise related to the proposals are more likely to vote correctly, whereas those with potential conflicts of interest tend to vote against token holders’ interest. Finally, we find that how well the vote delegation scheme works is positively related to future performance of the governance tokens. Overall, our evidence suggests that vote delegation can contribute to the performance and growth of DAOs so long as delegates have the requisite incentives and expertise.

Using bankruptcy filing information on parents matched with administrative data on their children, along with judicial leniency as an instrumental variable, we examine the effect of parental bankruptcy protection on children’s income, intergenerational mobility, and homeownership. We find that children whose parents receive Chapter 13 bankruptcy protection experience a significant increase in lifetime income. For every dollar of debt relief granted, these children gain two dollars in adjusted present value of lifetime earnings. Furthermore, they are more likely to rank in the top tercile of the income distribution, driven by increased intergenerational upward mobility, and are over five percentage points more likely to own a home by age thirty. Our findings suggest that bankruptcy protection and debt relief play an important role in fostering intergenerational mobility for low-income distressed households. Our results are most consistent with three mechanisms: protection of assets (e.g., house), higher investment in children's education and skill-development, and avoiding forced geographic mobility. We do not find support for neighborhood effects driving our estimates.

This paper investigates the financial stability consequences of banks' interest rate risk exposure and uninsured deposit funding share. We develop a model incorporating insured and uninsured deposits, interest rate-sensitive securities, and credit-risky loans to understand how banks respond to interest rate risk and the potential for deposit runs. The model delivers the concentration of uninsured deposits in larger banks and examines how banks' portfolio- and funding choices impact financial stability. When banks anticipate volatile bond returns, they seek exposure to this interest rate risk. We study the effects of recent Federal Reserve rate hikes on banks and analyze micro-prudential policy tools to enhance the banking sector's resilience. Higher liquidity requirements that target uninsured deposits are effective at curbing run risk of large banks but cause misallocation in the lending market. Size-dependent capital requirements are equally effective at mitigating run risk, with minimal unintended consequences.

This paper reconstructs house price and return dynamics in Germany over the very long-run, from the 15th century to the present, taking advantage of recent leaps in primary data. Contrary to existing consensus, I find that the ongoing contemporary "housing boom" in fact can be traced back four centuries ago, rather than originating in the mid-20th century. Similarly, the 1998-2024 era that saw house price growth outstrip income growth appears to be consistent with the historical norm, rather than an outlier driven by "bubble conditions". Housing excess returns appear to be driven by credit and demographic factors over time, and characterized by a "U-shape" trajectory since the Renaissance. A major inflection point in housing markets appears to have taken place around the year 1650, when mortgage rates began their secular decline: indeed, with idiosyncratic risk in housing being remarkably stable since this point in time, the data presented adds to evidence that it was this particular period that first witnessed the emergence of sovereign debt as the "safe asset", which around then began its divergence from the rest of the asset universe.

We study the impact of political disagreement on investor disagreement. Using continuous, time-varying measures of ideological leanings of U.S. state legislators on the liberal-conservative scale, we show that greater political polarization in a state leads to greater dispersion in earnings forecasts of analysts located in that state. This effect is stronger for firms in politically sensitive industries and firms that commit significant resources to social issues. We document the importance of our finding for both asset pricing and corporate investments. Looking at the cross-section of returns, we show that stocks covered by more politically polarized analysts earn lower future returns. This finding is consistent with Miller’s (1977) idea that in the presence of belief heterogeneity and short-sale constraints, prices reflect more optimistic valuations. In an M&A setting, we show that acquirers covered by more polarized analysts earn significantly lower announcement returns for equity offers but not for all-cash offers. These findings are consistent with models in which greater investor disagreement leads to steeper demand curves for stocks.

This study demonstrates that banking organizations with higher investments in AI technologies are exposed to more operational risk. Using comprehensive supervisory data on operational losses from large U.S. bank holding companies (BHCs) combined with detailed company-level data on AI-skilled human capital, we show that BHCs with more AI investments suffer higher operational losses per dollar of total assets. This relationship remains robust when using an instrumental variables (IV) approach based on BHCs' historical connections to universities with strong AI research programs. The impact of AI investment on operational losses significantly varies by loss type, being particularly driven by external fraud, client-related issues, and system failures. These losses stem not only from small, frequent incidents but also from severe, tail-risk events. Moreover, the risk-enhancing effect of AI is more pronounced for BHCs with weaker risk management practices. Our findings have important implications for banking performance, risk, and supervision.

Regulating externalities is particularly challenging in the presence of uncertainty and disagreement among economic agents. Traditional Pigouvian and Coasean approaches often fail because they require either precise knowledge of externality costs or frictionless bargaining. We propose an “uncertainty-based regulation” (UBR) mechanism that harnesses heterogeneous information and disagreement among firms to achieve socially efficient outcomes without requiring explicit information revelation. UBR modifies firms’ payoffs based on their deviation from the aggregate action, weighted by observable outcomes, effectively creating a synthetic market that internalizes externalities. This mechanism implicitly defines property rights, aligns incentives, and elicits private information without direct negotiation. We show that UBR achieves team efficiency, dominates conventional regulation, incentivizes information acquisition, and remains robust even when firms distrust each others’ signals. Moreover, if brought to a vote, it would receive unanimous support, making it politically viable.

Trillions of dollars in pension funds and other institutional investors engage in regular portfolio

rebalancing. This rebalancing often occurs based on a calendar date or a deviation from a threshold.

We show that such rebalancing has a market impact and induces predictability. When stocks are

overweight, funds sell stocks and buy bonds, leading to a decrease in equity returns of 17 basis points

over the next day. We find our results are robust to including controls for momentum, reversals,

and macroeconomic information. Importantly, mechanical rebalancing offers certain investors the

opportunity to front-run the predictable trades of these large funds. We estimate that the cost of

current rebalancing policies is approximately $16 billion per year—or $200 per U.S. household.

We develop a methodology to determine which days are “equity premium events”: events with significantly elevated equity premia relative to the daily equity term structure. To do so, we use recently available daily S&P 500 option expirations and forward analogs of option-implied ex ante measures of the equity premium. We use a data-driven approach to identify events that are significantly priced without taking a stance on what those events are. A variety of individual events are associated with significantly elevated equity premia. Among macroeconomic releases, FOMC, CPI, and nonfarm payrolls have the largest abnormal equity premia, which increase substantially between June 2022 and June 2023. However, the elevated equity premia on macroeconomic release days account for a significantly smaller share of total expected returns compared to previous estimates using realized excess returns. To provide intuition for the significant variation in equity premia across announcement types and time, we propose an asset pricing framework that decomposes the equity premium for a given macroeconomic release into components due to news variance and the sensitivities of the stock market and the SDF to the news released.

Integrating national accounting with financial accounting, we measure firm-specific current-cost capital stocks and flows for the entire Compustat universe. The firm-level current-cost investment rate distribution is heavily right-skewed, with a small fraction of negative investment rates, 5.51%, but a huge fraction of positive investment rates, 91.64%. Firm-level investment is also lumpy, featuring a fraction of 32.66% for positive spikes (investment rates higher than 20%). For a typical firm, 39% of total investment is completed within 20% of the sample years. Our data infrastructure facilitates empirical investment research and guides the calibration of theoretical models on firm investment.

Using firm-level administrative tax data, we document dramatic reductions in private leverage since the Global Financial Crisis, while leverage among public firms rose during this period. Changing firm characteristics are unable to account for this pattern. Younger and smaller private firms experience large declines in leverage. Reduced leverage among private firms is correlated with lower investment. The decline in private firm leverage and investment is strongly related to plausibly exogenous increases in local area bank capital requirements. Our findings suggest that banks' credit supply plays a prominent role in explaining the leverage pattern of private firms.

Does the housing market lead the financial cycle? We address this question by creating a new hand-collected database spanning a century of monthly building permit quantities and valuations for all U.S. states and the 60 largest MSAs. We show that the option to build embedded in permits renders volatility in residential building permit growth (BPG) a strong predictor of aggregate and cross-sectional stock and corporate bond return volatility. This predictability remains even after conditioning on a battery of factors, including corporate and household leverage and firms’ exposure through their network of plants to other localized physical risks like natural disasters. Cities and states with more elastic housing supply consistently predict financial market downturns at 12-month horizons, resulting in new trading strategies to hedge against overbuilding risk. A noisy rational expectations framework in which local building permits serve as a quasi-public signal for dividends explains these empirical patterns.

According to a no-arbitrage condition, risk-adjusted returns should be unpredictable. Using several

prominent factor models and a large cross-section of anomalies, we find that past cumulative riskadjusted

returns predict future anomaly returns with a negative sign. We interpret these cumulative

returns as deviations of an anomaly price from the mean-variance efficient portfolio, introducing a

novel anomaly-specific predictor endogenous to both the anomaly portfolio and the factor model.

The observed reversal pattern in risk-adjusted returns is consistent with a transitory component in

prices, indicating excess volatility in anomaly prices beyond what discount rate adjustments can

explain.

I show that venture capital market shocks have real consequences for high-skill knowledge workers. Plausibly exogenous shocks to local VC increase local startup hiring but also increase startup labor turnover. Workers that join startups in hotter VC markets are less likely to remain at the firm and more likely to leave the universe of VC-backed firms within two years. While job duration in hot markets falls across occupations, effects on career advancement differ by role: STEM workers who enter booming VC markets advance slower in seniority in the following two to five years, while Business workers are less affected. I show that differences in technology-skill specificity across occupations can explain this heterogeneity. The results indicate that shocks to risk capital can have lasting effects on knowledge worker careers.

Data is non-rival: a firm's data can be used simultaneously by others, and information about its customers benefits other firms even across industries. How is data being shared? Using granular information on mobile app usage, functionalities, and connections with data analytics platforms, we uncover a network of inter-firm data flows. Data sharing generates comovements in operational, financial, and stock-market performances among data-connected firms, beyond what traditional economic linkages can explain, and induces strategic complementarity in firms' product-design choices. Apple’s App Tracking Transparency policy, which restricts inter-firm data flows, weakens these patterns, providing causal evidence of the role of data sharing. To explain these findings, we develop a dynamic network model of data economy, where firm growth becomes interconnected through data sharing. The model introduces a network-augmented Gordon growth formula to value data-generated cash flows, capturing direct and indirect network externalities over multiple time horizons. Our metrics of valuation centrality identify systemically important firms that disproportionately influence the data economy due to their pivotal positions within the data-sharing network.

We identify a novel exogenous demand shock caused by passive funds in corporate bond markets. Passive fund demand for corporate bonds displays discontinuity around the maturity cutoffs separating long-term, intermediate-term, and short-term bonds. Using a novel identification strategy, we show that these non-fundamental passive demand shifts i) lead to predictable upward price pressure, and ii) spill over to primary markets, causing lower issuing yield spreads, and firms engaging in debt market timing by substituting bank debt with bond financing. We show how SEC regulations and provisions affect the execution of passive strategies and their transmission to the real economy.

This work proposes a framework to study the risk-benefit trade-off of quantitative easing (QE) for the consolidated government, integrating the central bank and treasury department. In a simple model with distortionary taxes, nominal frictions, and a zero lower bound, we characterize the optimal size of a QE program as equalizing the marginal benefit from stimulating output to the marginal cost of induced rollover risk for taxpayers. A conservative quantification of this trade-off suggests that QE programs in the US made a positive net present contribution to welfare.

This paper provides causal evidence that benchmarking-induced asset price distortions have real effects on corporate investment. We document that increased benchmarking over the past 20 years has fundamentally changed the cross-section of stocks' CAPM βs. We establish causality using exogenous variation in stocks' benchmarking intensity around Russell index reconstitutions. Stock's CAPM β increase upon index inclusion with larger effects for stocks which experience larger benchmarking intensity increases. Firm managers perceive this as an increase in their cost of capital and reduce investment. Treated firms have 7.1% less physical and 8.4% less intangible capital after six years. We find consistent results at the industry level using long-differences from 2000 to 2016. At the aggregate level, the increase in CAPM βs caused by benchmarking largely offset the decline in the risk-free rate over the past 20 years and can explain 57% of the missing investment puzzle.

We examine the interaction between private debt and bank debt in corporate borrowing.

Combining administrative bank loan-level data with private debt deals, we document

that many U.S. private debt borrowers also borrow from banks. When co-financing

these dual borrowers, private debt lenders provide larger, relatively junior, and riskier

term loans with higher spreads, while banks offer credit lines. After accessing private

debt, dual borrowers obtain additional bank credit line commitments, reflecting greater

demand for liquidity insurance and imposing drawdown risks on banks. Our findings

suggest that private debt substitutes for banks’ term loans while complementing their

liquidity provision through credit lines.

We construct a novel physical risk factor by forming a portfolio of REITs, long on those with properties more exposed to climate risk and short on those less exposed. Combined with a transition risk factor, we assess the climate risk exposure of P&C and life insurance companies in the U.S. Insurers can be exposed to climate-related physical risk through their operations and transition risk through their $12 trillion of financial asset holdings. We estimate insurers’ dynamic physical and transition climate beta, i.e. their stock return sensitivity to the physical and transition risk factors. Validating our approach, we find that insurers with larger exposures to risky states have a higher sensitivity to physical risk, while insurers holding more brown assets have a higher sensitivity to transition risk. Using the estimated betas, we calculate the expected capital shortfall of insurers under various climate stress scenarios.

We decompose conditional variance into the sum of two components: one driven by uncertainty about cash-flow growth, or bad variance, and a second driven by uncertainty about discount rates, or good variance. We develop a simple theoretical model with time-varying second conditional moments and document empirical evidence that suggests bad variance earns a risk premium that is statistically and economically significant, whereas good variance does not. In out-of-sample tests we find that bad variance dominates other predictors of market returns and can help identify the gap between the lower bound of Martin (2016) and the actual market risk premium.

Despite managing assets totaling $23 trillion, little research has been conducted on the investment activities and impact of Development Finance Institutions (DFIs). We document that over the past three decades, DFIs have increasingly invested in venture capital (VC), participating as limited partners in one out of every six deals. When investing in VC, DFIs pursue not only financial returns but also aim to address market failures such as externalities, information frictions, and coordination challenges. Based on their mandates, we identify four objectives DFIs seek to achieve through VC investments: building a VC ecosystem, supporting entrepreneurship and SMEs, fostering innovation, and promoting sustainable business practices. Our analysis of DFI investments in developing countries yields mixed results. On the positive side, DFIs are more likely to target industries that generate positive externalities for society and provide more capital to underrepresented fund managers. However, they are less likely than conventional VC investors to support early-stage deals, and their investments have no significant effect on firms’ success and innovation. In developed countries, we find little evidence that DFIs address market failures through their investments. Overall, our results suggest that DFIs have significant room to enhance their impact by reallocating capital toward developing countries, where they also achieve better financial performance than in developed countries. Additionally, DFIs could more directly address market failures and accept higher risks in their portfolios to fulfill their developmental objectives.

We develop a credit market competition model that distinguishes between the information span (breadth) and signal precision (quality), capturing the emerging trend in fintech/non-bank lending where traditionally subjective (``soft'') information becomes more objective and concrete (``hard''). In a model with multidimensional fundamentals, two banks equipped with similar data processing systems possess hard signals about the borrower's hard fundamentals, and the specialized bank, who further interacts with the borrower, can also assess the borrower's soft fundamentals. Increasing the span of the hard information hardens soft information, enabling the data processing systems of both lenders to evaluate some of the borrower's soft fundamentals. We show that hardening soft information levels the playing field for the non-specialized bank by reducing its winner's curse. In contrast, increasing the precision or correlation of hard signals often strengthens the informational advantage of the specialized bank.

We find that index funds incur adverse selection costs from changes in the composition of the stock market. This is because indices rebalance in response to composition changes, both on the extensive margin (IPOS/delistings or additions/deletions) and intensive margin (issuance/buybacks). This rebalancing approach successfully captures the market as it evolves, but effectively buys high and sells low. A long-short portfolio capturing only intensive margin rebalancing has an average alpha of nearly -4% per year. Despite being less than 10% of AUM, this rebalancing portfolio does poorly enough to drag down overall index fund returns. We estimate that a “sleepy” strategy that rebalances once a year improves fund returns by 50bps per year by avoiding the short- and medium-term adverse selection associated with a firm’s own primary and secondary market activity.

We argue that quantitative easing (QE) and tightening policies constitute a dynamic state-contingent plan instead of a succession of independent interventions.

This view changes the main reason QE is effective by adding an insurance channel

to the static effect of absorbing bond supply in a given period. QE purchases occur in bad economic states (e.g., 2008-2009 or 2020) when the supply of government

debt increases. Increasing long-term bond prices in bad economic states increases

their safety, driving up their value and thus lowering ex-ante yields. We estimate

that this insurance channel alone lowers long-term bond yields by 75-100 bps. This

channel explains the prevalence of low long-term yields, low term premia, and low

yield volatility since the introduction of QE, despite the sharp increase in net government debt supply. Consistent with a state-contingent channel, implied volatilities

of long-duration risk-free securities fall substantially on QE announcements, even for

options with maturities out to 10 years. We calibrate a policy rule for asset purchases

to their historical path and include it in a quantitative term structure model. In the

model, state-contingent QE offsets term premia fluctuations in long-term bonds. The

insurance effect from this channel lowers long-term Treasury yields by 75bps ex-ante,

which explains about 75% of the total effect of QE on yields. The calibrated model

matches both broad patterns in bond yields and the response to QE announcements.

Since firms time equity net issuance based on perceived misvaluation of their shares, these actions reveal their net present value calculations and perceived cost of equity. Building on this insight, we propose a test to identify the asset pricing model that best aligns with firms’ perceived cost of equity. We find that the CAPM explains net issuance decisions better than alternative factor models or market multiples. Our findings suggest that firms may overlook variation in cost of equity associated with characteristics such as size and value.

Using a newly comprehensive dataset that merges firm-level information with corporate bond

issuance and holdings, we show that firms strategically use bond issuance not only to minimize

their cost of capital but also to diversify their investor base. Investor specialization in certain

bond characteristics allows firms to effectively shape their bondholder composition through

issuance decisions. We find that firms with more diversified bondholder exhibit increased resilience

to credit market shocks. Our analysis underscores the dual function of market timing

in corporate bond issuance: it serves both to reduce capital costs and as a strategy for credit

supply diversification. These findings emphasize the pivotal role of financially sophisticated

firms in strategically issuing assets in a market increasingly reliant on non-bank intermediaries.

We compile a dataset comprising seven million residential real estate transactions in the United Kingdom to examine homeowner valuation of dwelling sustainability. Homeowners pay a premium for more energy-efficient dwellings. Exploitation of the spatial, temporal, tenurial, and vintage heterogeneity in the premium shows that homeowners price the energy efficiency of their dwellings following economic principles. We propose a simple valuation model to recover the discount rates used by homeowners, which provide direct measures for rates used to discount investments in sustainable development and climate change mitigation. The rates demonstrate that homeowners accept lower returns for greener dwellings, indicating non-pecuniary incentives.

This paper provides a guide for using causal inference with asset prices and quantities. Our framework revolves around two simple assumptions: homogenous substitution conditional on observables and constant relative elasticity. Under these assumptions, standard cross-sectional instrumental variable or difference-in-difference regressions identify the relative demand elasticity between assets, the difference between own-price and cross-price elasticity. In contrast, identifying aggregate elasticities and substitution along specific characteristics necessarily relies jointly on exogenous sources of time-series variation alone. The same principles also apply to the estimation of multipliers measuring the price impact of supply or demand shocks. The two assumptions map to familiar restrictions on covariance matrices in classical asset pricing models, encompass models from the industrial organization literature such as logit, and accommodate rich substitution patterns even outside of these models. We discuss how to design experiments satisfying these conditions and offer diagnostics to validate them.

Is there a gender gap in the serial founding of VC-backed startups? Despite robust evidence linking serial entrepreneurship to startup success, women comprise only 13.3% of VC-backed founders, declining to just 4% among those who found three or more startups. We introduce a novel empirical design that exploits within-startup variation to study this gap, comparing future funding outcomes for men and women who co-founded the same startup. This approach, akin to twin studies, controls for unobservable differences in startup quality and founder ability. We document substantial gender gaps, both on average and following failure or success of the current startup. Following failure, women are 22.5% less likely to found another VC-backed startup compared to their cofounders who are men. Among those who do found another VC-backed firm, women raise 53.3% less capital following failure and 24.6% less following success. Using founder LinkedIn data and pension fund supply shocks, we investigate potential demand- and supply-side drivers of these gaps. We rule out lack of interest by women in founding new firms and find no evidence of gender differences in founder quality. In fact, subsequent startups founded by women and men have higher success probabilities, despite the large funding gap. The gender gaps appear driven by unequal treatment, particularly from new investors rather than existing ones. Our analysis reveals striking negative spillovers following investors' experiences with other women-founded startup failures, but no positive spillovers following their successes, consistent with theories of stereotyping. These findings suggest potential efficiency gains from reducing frictions faced by experienced women founders in accessing venture capital.

We study a noisy general rational expectations equilibrium in an economy with big trad- ing data, populated by asymmetrically informed logarithmic investors. We show that the equilibrium can be either fully or partially revealing about macroeconomic shocks privately observed by informed investors, depending on economic parameters and the extent of en- dogenous information overlap across data sources. We find that trades in derivative securities reveal substantial information about the shocks; interest rates signal impending economic downturns; and asset prices jump when output volatility transitions across thresholds sepa- rating fully and partially revealing equilibria in the parameter space. In contrast to markets with logarithmic investors, in markets with CARA investors derivatives data reveals little information and the quantity of this information does not depend on the data set’s dimension.

We use machine learning to capture nonlinearities and interactions in the relation between trades and holdings of multiple market participants and future stock returns. Our predictor yields a long-short portfolio with significant out-of-sample alpha, forecasts firm fundamentals, and assigns stocks on the right side of most anomalies. Predictability is stronger for smaller or illiquid stocks and stocks with lower analyst coverage or higher idiosyncratic volatility. A factor model based on our predictor achieves higher Sharpe ratio than existing models. Our findings suggest that incorporating nonlinear interactions between trades and holdings of various participants reveals valuable information for price discovery.

We show that the debt ceiling significantly impacts the duration of government liabilities through an unintended interaction of the Treasury’s issuance rules and the debt ceiling constraint. During debt ceiling episodes, the Treasury systematically allows more bills to mature than it issues. In recent years this force has induced fluctuations in bill supply greater than one percent of GDP. Exploiting this, we devise an instrument for the supply of bills and show that the debt ceiling has distorted the price of short-term investment-grade corporate credit in both the primary and secondary markets. Our preferred IV specifications imply that a one hundred billion dollar decline in bill supply depresses corporate yields on the order of ten basis points.

This study uses a novel dataset of over 11,000 foreign suppliers to U.S. manufacturers to investigate the impact of supply network fragility on corporate policies. The scarcity of suppliers offering specialized inputs emerges as a key driver of fragility. Both theoretical and empirical evidence indicate that firms with fragile supply networks maintain more input inventories, less cash, and higher leverage. Moreover, plausible exogenous variation in fragility from technology adoption and disruptions supports a causal interpretation of the results. My findings indicate that because specialized inputs lack a spot market post-disruptions, firms with fragile supply networks favor operational over financial hedging.

Leveraging unique data on internal capital flows between Bank Holding Companies (BHCs) and their subsidiaries, we investigate the role of internal loans to commercial banks over the past three decades. These loans serve as asset-like liabilities, providing banks with a stable, low-cost funding alternative to traditional demand deposits. Banks with access to internal loans engage more aggressively in the syndicated loan market, issuing loans with larger amounts, longer maturities, and lower interest rates. Additionally, they are more likely to establish relationships with new borrowers. Despite instances of underperformance, the aggregate impact of loan-funded loans on bank performance is positive. We further characterize how bank and nonbank subsidiaries compete for internal loans from their BHC. We show that nonbanks have received increasingly preferential treatment over time. Our findings suggest that the expansion of nonbanks inside BHCs can negatively affect credit access in the broader economy.

Since the Great Recession, the rise of single-family rental companies has changed the investor ownership landscape in the U.S. Using housing transaction data, we document the rise of Long Term Rental (LTR) companies, defined as inclusive of single-family rental, rent-to-own, and real estate private equity firms, by constructing a panel of national single-family housing portfolios between 2010 and 2022. We show that LTR growth outstripped all other investor types, such as builders, iBuyers, and small investors, over the last decade. These companies geographically concentrate their holdings in select census tracts and expand their local market shares over time. To estimate LTRs’ impacts on local housing markets, we construct a novel instrument predicting LTR entry, which we name the “suitability index.” In the cross-section, this instrument leverages differential revealed preferences in product characteristics across landlord types. In the time-series, we interact these differential product preferences with a proxy for falling property management costs over time. In the first stage, more suitable locations for LTRs experience higher growth in LTR shares: a one-standard-deviation increase in the instrument implies a 54.4% higher annual growth in LTR share relative to the baseline mean. We use this instrument for LTR market entry to estimate the causal impact of LTR market share on local house prices. We find that a one-standard-deviation above the mean increase in LTR share growth leads to an annual additional house price growth of 1.24pp and additional rent growth of 2.51pp. Finally, we discuss how the reallocation of homeownership across small and large landlords, as well as owner-occupants and investors, contribute to these price increases.

We introduce discounted alphas, a novel framework for equity valuation. Our approach circumvents the need for stock-level cost-of-equity estimates required in discounted cash flow (DCF) valuation and identifies economically important variation in fundamental value not captured by best-in-class DCF methods. We find that discretionary buy-and-hold funds tilt toward characteristics that predict underpricing but not short-term alphas and that private equity funds appear to capture substantial CAPM misvaluation, both initially at buyout and subsequently at exit. However, despite these pockets of misvaluation, we find that firm equity values are "almost efficient" by Black's (1986) definition.

We use the staggered roll-out of a mentorship program within a superstar technology firm to test whether small-scale, targeted mentoring can address organizational frictions that influence innovation and productivity. Analyzing novel data capturing the complete innovation pipeline—from initial idea disclosures through patent applications—we study seven cohorts with 633 mentees and track outcomes for over 20,000 employees. First, we find a positive, significant relation between participating in the mentorship program and innovation outcomes in the short- and long-term, with underrepresented innovators realizing the largest gains. Second, we document significant indirect spillovers through team formation, with unmentored peers experiencing productivity gains through collaboration. Survey evidence points to three mechanisms: knowledge transfer about patent processes, expanded professional networks, and increased confidence in identifying patentable ideas. The mentorship program is also associated with broader organizational improvements: engineers at the implementing firm perceive the culture to be better, report higher job satisfaction, and are less likely to leave than peers. These results suggest that small-scale mentoring programs can provide a cost-effective approach to fostering innovation and inclusivity.

Short sellers are widely known to be informed, which would typically suggest that they demand liquidity. We obtain comprehensive transaction-level data to decompose daily short volume into liquidity-demanding and liquidity-supplying components. Contrary to conventional wisdom, we show that the most informed short sellers are actually liquidity suppliers, not liquidity demanders. They are particularly informative about future returns on news days and trade on prominent cross-sectional return anomalies. Our analysis suggests that market making and opportunistic risk-bearing are unlikely to explain these findings. Instead, our results align with recent market microstructure theory, pointing to strategic liquidity provision by informed traders.

Existing high-frequency monetary policy shocks explain surprisingly little variation in stock prices and exchange rates around FOMC announcements. Further, both of these asset classes display heightened volatility relative to non-announcement times. We use a heteroskedasticity-\allowbreak based procedure to estimate a "Fed non-yield shock'', which is orthogonal to yield changes and is identified from excess volatility in the S&P 500 and various dollar exchange rates. A positive non-yield shock raises stock prices in the U.S. and around the globe, depreciates the dollar against all major currencies, increases global commodity prices, and leads to net capital inflows into emerging market economies. The non-yield shock is essentially uncorrelated with previous monetary policy shocks and its effects are large in comparison. Its strong effects on the VIX and other risk-related measures point towards a dominant risk premium channel. We show that the non-yield shock can be related to Fed communications and that its existence has implications for the identification of structural monetary policy shocks.

We find that firms adjust their AI/green investments upward (downward) in response to favorable (unfavorable) market reactions to announcements of such emerging-technology investment plans. This positive investment-price association is more likely due to managerial learning from the market than alternative explanations based on omitted variables or growth expectations. It is more pronounced when market reactions are unfavorable, when investors have more domain knowledge, and when managers are more uncertain about their plans. Our paper illustrates the usefulness of market feedback in guiding emerging corporate policies and sheds new light on what type of information managers learn from the stock market.

This paper examines the dual role of Business Development Companies (BDCs) as creditors and shareholders in the private direct lending market. Utilizing a comprehensive deal-level database, our analysis shows that dual-holder BDCs are more effective monitors than sole lenders, benefiting from enhanced tools for information access and governance. This effectiveness allows them to charge higher loan spreads, while simultaneously reducing credit risk and lowering the borrowing cost of portfolio firms from other lenders. We rule out alternative explanations attributing higher loan spreads to mere compensation for capital injection or to hold-up by a dominant financier. Our findings highlight a critical mechanism through which BDCs serve a market segment --- mid-sized firms with low (or even negative) cash flows and a lack of collateral but high growth potentials --- that is typically undesired by traditional bank lenders.

Mortgage borrowers are "locked in": forgoing moves to keep low mortgage rates. We study the general equilibrium effects of mortgage lock-in on housing markets. We provide causal evidence that lock-in increases prices, particularly in expensive areas, because locked-in borrowers would otherwise demand less housing. We design a spatial housing ladder model with long-term mortgages, generating a distribution of locked-in rates and equilibrium effects on mobility and prices consistent with the data. A temporary rate hike causes lock-in, increasing housing demand and prices, especially in expensive areas. A $10k tax credit to starter-home sellers modestly unlocks mobility while increasing trade-up home prices.

We propose a new method for decomposing realized stock market capital gains into contributions from changes to the real yield curve, equity premia, and expected dividends. The method can be implemented period by period. It requires no regressions and is instead based on calculating the effects of changes to the various inputs of the present value formula. We provide two versions of the decomposition, one which uses financial markets data alone: the real yield curve, near-term options-based equity premia, and dividend futures (to assess the weight of dividend strips of different maturities in the market), and one supplementing this with data on growth in analyst earnings forecasts as a guide to growth in dividend expectations.

We implement our method in US data for the S\&P500 for 2005-2023. Changes to expected dividends played the dominant role for the cumulative capital gain in the market over this period, while changes to the real yield curve and equity premia contributed more to capital gain fluctuations, with real yield curve shifts driving a large positive capital gain in 2020 but a large negative capital gain in 2022. Changing near-term equity premia contribute to returns mostly in crisis. In general, the roles of the real yield curve, equity premia, and expected dividends vary dramatically across periods and we highlight the heterogeneous drivers of the 2008, 2020, and 2022 market drawdowns.

This paper shows that exposure to entrepreneurs during adolescence increases women's entry and performance in entrepreneurship and improves the allocation of talent in the economy. Using population-wide registry data from Denmark, we track nearly one million individuals from adolescence to adulthood and exploit idiosyncratic within-school, cross-cohort variation in exposure to entrepreneurs, as measured by the share of an adolescent's peers whose parents are entrepreneurs at the end of compulsory school. Early exposure, and in particular exposure to the entrepreneur parents of female peers, encourages girls’ entry and tenure into this profession, while it has no effect on boys. The increase in female entrepreneurship is associated with the creation of successful and female-friendly firms. Furthermore, early exposure reduces women's probability to discontinue education at the end of compulsory school and to hold low wage jobs through their lives. Together these results challenge the view that the most successful female entrepreneurs would enter this profession regardless of early exposure.

There is increasing awareness of how local media affects financial markets, but also of the endogeneity of media coverage. We separate the causal impact of local media on financial markets from selection effects using a new, hand-collected database of newspaper closures. We find that at least 29% of local newspaper closures are driven by distress, and thus, likely endogenous to local economic conditions. Return volatility and idiosyncratic risk decrease significantly after non-distress-driven newspaper closures, but increase after distress-driven closures, suggesting the presence of substantial selection effects. We find similar patterns for liquidity and trading. Once we account for selection, the estimated impact of local newspapers on volatility increases by over 40%. The reduction in volatility after non-distress-driven newspaper closures is larger for stocks subject to greater information frictions, lower national media coverage, firms located in remote areas, and during recessions. All these tests suggest that investor information processing is the main channel driving our results. Our findings highlight that the effect of media on financial markets may be larger than previously documented.

We show that three traded risk factors---Dollar, Carry, and Euro-Yen---propagate demand shocks across currencies. Identified using a novel approach that combines trading and return data, these factors explain 90% of the non-diversifiable risk intermediaries bear in currency trading. IV estimates show that factor prices rise by 5–30 basis points per $1 billion of demand. A demand shock to one currency changes demand for these risk factors, affecting their prices and prices of other currencies with shared exposures. Non-currency assets are also exposed to these risks, allowing demand shocks to propagate across markets through shared currency risk.

We study the trading behavior of financial intermediaries around Federal Open Market Committee (FOMC) announcements in the S\&P 500 options market using daily and high-frequency data. Proprietary trading firms are net sellers of options on FOMC days as opposed to non-FOMC days. This liquidity provision is particularly pronounced when monetary policy is unexpectedly accommodative and market interest rates fall. Strikingly, the morning trades of proprietary trading firms predict monetary policy shocks and option price movements later in the day, suggesting that some financial institutions may have preferential access to the information release by the Fed regarding the future path of policy actions. Our results show that monetary policy not only boosts intermediaries' risk taking and liquidity provision through option trades but also grants them a trading edge due to their informational advantage.

We examine the response of investment to real estate prices among French firms from 1994 to 2015. Using newly introduced methods and specifications, we find that investment sensitivity to real estate prices decreases with firm size: The smallest firms are at least three times more responsive to changes in collateral value compared to the largest firms. We impute these estimates onto other countries where available data lack firm-level detail. This approach allows us to assess the aggregate sensitivity of investment to real estate prices across different countries. Our results indicate significant variation in the sensitivity of aggregate investment to real estate shocks, driven by cross-country differences in the size distribution of firms.

Private debt funds are the fastest growing segment of the private capital market. We evaluate

their risk-adjusted returns, applying cash-flow-based methods to form a replicating

portfolio that mimics their risk profiles. Accounting for both equity and debt factors, a typical

private debt fund produces an insignificant abnormal return to its investors. However,

gross-of-fee abnormal returns are positive, and using only debt benchmarks also leads to

positive abnormal returns as funds contain equity risks. The rates at which private debt

funds lend appear to be high enough to offset the funds’ fees and risks, but not high enough

to exceed both their fees and investors’ risk-adjusted required rates of return.

Most homeowners do not have enough insurance coverage to rebuild their house after a total loss. Using contract-level data from 24 homeowners insurance companies in Colorado, we show wide differences in average underinsurance across insurers that persist conditional on policyholder characteristics. Underinsurance matters for disaster recovery. Across households that lost homes to a major wildfire, each 10 p.p. increase in underinsurance reduces the likelihood of filing a rebuilding permit within a year by 4 p.p.. To understand why consumers purchase underinsured policies, we build a discrete choice insurance demand model. The results suggest that policyholders treat insurers that write less coverage as if they set lower premiums, forgoing options to get more coverage at the same premium from other insurers -- a pattern we call coverage neglect. Our findings suggest that coverage limits are either not salient to consumers or they are difficult to estimate without the input of insurance agents. Under a counterfactual without coverage neglect, consumer surplus increases by $290 per year, or 10% of annual premiums, on average.

We propose a new model of expected stock returns that incorporates quantity information from market trading activities into the factor pricing framework. We posit that the expected return of a stock is determined by not only its factor risk exposures (beta) but also the factor's quantity fluctuations (q) induced by noise trading flows, and hence term the model beta times quantity (BTQ). The rationale is that a factor's premium should be higher when sophisticated investors have absorbed flows of stocks with high exposure to that factor. The BTQ model provides a compelling risk-based explanation for stock returns, which is otherwise obscured without considering the quantity information. The cross-sectional risk-return association, which is nearly flat unconditionally, strongly depends on the quantity variable. The structured BTQ model reliably predicts monthly stock returns out of sample, and addresses the factor zoo problem by selecting a small number of factors.

Science-based R&D can deter venture capitalists due to high technical uncertainty. We study whether mission-oriented public funding, which supplies basic science as a public good, fosters VC investments. Our quasi-natural experiment is the BRAIN Initiative (BI), a government-funded program with the goal of mapping the human brain. Using a large language model, we first show the large spillover effects of BI in neurotech. In a difference-in-differences analysis, we find an increase in VC investments in neurotech startups accompanied by higher valuations and more successful VC exits following the BI. The channels driving these results suggest reduced technical uncertainty: 1) increased supply of high-skilled academic labor; 2) more innovation, including breakthrough patents; 3) enhanced integration with complementary technologies, especially AI and big data, which aligns with the BI's data-driven mission. Our results suggest the supply of government-backed science and scientists can spur follow-on private investments in emerging technologies.

Tax incentives on financial activities have a major impact on the U.S. government budget and currently amount to $1 trillion in foregone tax revenue per year. Motivated by this fact, this paper uses a novel empirical framework to study the role of fiscal policy as a driver of capital flows in financial markets, the cost and supply of financial products, and asset prices. Using fiscal reforms that shifted household demand for financial products, I document a large and persistent response of capital flows to tax incentives, accounting for up to 53% of the long-term growth of aggregate financial sectors. These shifts in capital allocation have persistent effects on the cross-section of asset prices, with securities that are held relatively more by subsidized institutions outperforming by 13 percentage points in the three years post-reform. The tax savings introduced by fiscal reforms, however, do not entirely accrue to households, as financial institutions retain up to 21% of these savings by increasing the cost of their products when market entry is limited. Given the large response of capital flows to tax incentives, I then investigate whether this response differs across individual U.S. households. I find that fiscal policy carries substantial distributional effects, which tend to benefit wealthier households. Having access to sophisticated advisors, ultra-high-net-worth households are significantly more responsive to tax incentives and earn a tax alpha of 50 bps annually relative to less-wealthy households. Overall, these findings highlight the importance of fiscal policy in shaping financial markets, with wealthier households benefiting from tax incentives more than less-wealthy households.

Cumulative abnormal returns (CAR) computed around acquisition announcements are widely considered market-based assessments of expected value creation. We show that announcement returns do not correlate with commonly used and new measures of ex-post acquisition outcomes. A simple characteristics model using standard information known at announcement can predict outcomes reasonably well, and CAR fails even to capture the prediction from this model. We present evidence suggesting that information about the standalone acquirer dominates CAR and, therefore, makes it virtually impossible to extract deal-related information. We conclude that CAR is an unreliable measure of expected value creation.

We develop a theory of blockholder activism, in which a blockholder can expend costly effort to improve firm productivity, contract with management to limit agency costs, and influence investment and financing decisions. Starting with an initial toehold, the blockholder may also adjust its stake over time, shaping engagement and firm policies and potentially leading to a takeover bid or an exit. We obtain the equilibrium in closed-form, i.e. the blockholder's dynamic trading strategy, its entry/exit decisions, and the firm’s compensation, financing, and investment policies. A key feature of the model is the endogenous and dynamic nature of the blockholder’s stake, which implies that the allocation of control rights over policies has contrasting implications in static versus dynamic contexts.

Interest rate caps are regulations that place an upper limit on the interest rate that can be charged on loans. We estimate the impact of rate cap laws on consumers’ financial well-being with a difference-in-difference analysis based on staggered implementation of rate cap laws. We combine a credit bureau’s detailed data for almost 600,000 consumers between 2016 and 2019 with a database of alternative credit that tracks credit accounts of consumers with primarily subprime alternative credit providers. We find that rate cap laws result in a credit score increase of 0.153 points every quarter. However, consumers with initial credit score in the range 300-660 experience a decline in credit score of about 1.7 points per quarter. Following rate cap laws, unbanked consumers and alternative financing users experience credit score declines of 0.54 points per quarter and 1.545 points per quarter, respectively. We do not find evidence of a decline in credit usage, suggesting there may be substitution across different forms of credit. However, chargeoffs, delinquencies, foreclosures, and payment due increase for consumers in the alternative credit database.

A rent guarantee insurance (RGI) policy makes a limited number of rent payments to the landlord on behalf of an insured tenant unable to pay rent due to a negative income or health expenditure shock. We introduce RGI in a rich quantitative equilibrium model of housing insecurity and show it increases welfare by improving risk sharing across idiosyncratic and aggregate states of the world, reducing the need for a large security deposits, and reducing homelessness which imposes large costs on society. While unrestricted access to RGI is not financially viable for either private or public insurance providers due to moral hazard and adverse selection, restricting access can restore viability. Private insurers must target better off renters to break even, while public insurers focus on households most at-risk of homelessness. Stronger tenant protections increase the effectiveness of RGI.

The dollar intermediation channel of foreign exchange interventions (FXI), a

form of the portfolio balance channel, arises from the imperfect substitutability

between domestic currency and USD, the dominant global currency, along with

financial frictions limiting USD liquidity access. This channel plays a key role

in Banco Central do Brasil’s FXI. High-frequency data on over 8,000 FXI events

(1999–2023) show that unanticipated spot sales appreciate the domestic currency,

reduce covered interest parity deviations, and crowd out private intermediation,

especially when intermediaries are constrained. Our results support an extended

Gabaix and Maggiori (2015) model, in which constrained intermediaries amplify

intervention effectiveness.

Private information production in financial markets enhances asset price informativeness, aiding efficient decision-making. Investors pay for information to profit from trading, creating a trade-off between market informativeness and illiquidity costs. Using a mechanism design approach, we show price informativeness can be achieved without illiquidity, at a cost equal to producing information. This mechanism incentivizes efficient information production, avoiding the inefficiency of profits obtained at the expense of less informed investors.

Macro-finance models featuring an infinitely-lived, representative agent typically imply that (a) the equity premium reflects compensation for aggregate risk and (b) the long-run, risk-adjusted growth rate of consumption is smaller than the risk-free rate (``transversality condition''). The international historical experience with growth-indexed bonds suggests that these bonds, which isolate the risk premium of aggregate fluctuations, command only a moderate risk premium. Equity investments that that are hedged against aggregate fluctuations still command a sizable equity premium, suggesting that the equity premium is not just compensation for aggregate risk. In addition, the risk-adjusted GDP-growth rate is roughly the same (and slightly higher) than the risk-free rate, which calls into question one of the basic tenets of standard macro-finance models. The findings have potential implications for some recent puzzles pertaining to the pricing of government debt.

This study presents a novel Bayesian approach incorporating financial frictions into a panel structural break model, utilizing economically informed priors from intermediary asset pricing theories. Our data-driven prior selection method, adept at handling unbalanced panels, enhances the identification of regime shifts and the selection of return predictors, thereby improving equity return forecasts. Validated through simulations and empirical analysis, our approach boosts out-of-sample cumulative returns and Sharpe ratios. Leveraging asset holdings data and intermediary-induced priors, the framework facilitates precise real-time regime change detection and provides Bayesian insights into the inconsistencies of risk prices associated with intermediary risks.

How do acquisitions of young, innovative, venture capital-funded firms (startups) affect firms’ incentives to enter a market? I create a product-level dataset of enterprise software, and use textual analysis to identify competing firms. Motivated by new stylized facts on startup acquisitions in software, I build and estimate a dynamic model of startups’ entry decisions in the face of these acquisitions. In the model, acquisitions can affect returns to entry (1) by affecting market structure, and (2) by providing an entry-for-buyout incentive to potential entrants. Using the parameter estimates, I simulate how startup entry would evolve over time if merger control was tightened. The simulations reveal that, if all startup acquisitions were blocked, entry would decline on the order of 8-20% in some markets. In contrast, I find suggestive evidence that blocking mergers between established industry players and more mature startups might increase entry. These findings indicate that case-by-case merger review can best foster sustained startup entry.

We model the trade-offs of an investor who builds positions and exerts governance in competing firms. The implicit cost of doing traditional governance is under-diversification, since her incentives to cut slack and improve efficiency are low when she has similar exposure to all firms in an industry. The benefit is to escape the incentive to push firms to compete less aggressively, and avoid the potential litigation and reputational costs. We study how these trade-offs shape the equilibrium interactions of ownership, governance, and competition, and the role of competition policy in a world where investors influence the objectives of competing firms.

Credit card interest rates average 23%, far exceeding rates on other major loan or bond types. To understand the drivers of these high rates and the economics of credit card banking, we analyze regulatory account-level data on 330 million monthly accounts, representing 90% of the US credit card market. While high default rates partially explain high card interest rates, netting these out still leaves an average interest spread of 10%. Non-interest expenses, including rewards, are more than offset by corresponding non-interest income. Operating costs are large, suggesting a significant role for market power, and explain a sizable part of rate spreads. Yet even after subtracting them, banks’ return on credit card lending is over four times their average return on assets. Using the cross section of accounts by default risk, we estimate a price of default risk. We find it is similar to that in high-yield bonds, and implies the average card borrower pays a 4.2% risk premium. Yet, there remains a substantial zero-beta rate of 3% that is unexplained by risk and is specific to credit card lending.

Twenty-two percent of U.S. households partially prepay their mortgage each month, a practice known as curtailment in the mortgage industry. For mortgages with interest rates below the risk-free rate, curtailment has negative net present value. We show that interest rate increases in 2022 led to $1.2 billion in curtailment losses from January 2022-February 2023, due to the rising share of mortgages with a negative rate spread. Curtailment is more frequent among households with less credit card debt or those who have no credit card at all and is correlated to the availability of disposable income. Interest rate increases reduce curtailment for adjustable-rate but not fixed-rate mortgages. Our findings suggest that curtailment is explained by both an aversion to debt and by household responses to changes in current cash flows.

We study the feedback loop between dollar dominance, currency mismatch, and the U.S.’s role as a global dollar lender of last resort. Using new administrative data, we find that central bank swap lines act as substitutes for dollar reserves held by foreign central banks in helping fill global banks’ dollar funding gaps, particularly when market-based synthetic dollar funding is scarce. U.S. dollar lending of last resort, however, incentivizes global banks to engage in greater currency mismatches while encouraging foreign central banks to hold relatively fewer dollar reserves, exacerbating dollar funding gaps during crises. We develop a model that incorporates swap lines into a framework of global banking and central banking, highlighting the intermediation chain in emergency dollar liquidity provision. Swap lines stabilize dollar funding markets during crises yet lead to ex-ante over-dependence on the dollar due to pecuniary externalities between global banks and foreign central banks. This dependence introduces unintended long-term risks, not only for foreign countries but also, perhaps surprisingly, for the U.S., as it alters Treasury holder composition and the heterogeneous exposure to fire sale risks. Finally, we examine how post-crisis regulations have inadvertently amplified the demand for a global dollar lender of last resort, creating a policy ratchet effect that entrenches systemic reliance on U.S. dollar liquidity backstop.

We analyze a model of a monopolistic informed investor who receives private information

sequentially and faces a post-trading disclosure requirement. We show that this trading

model can be transformed into a fictitious consumption-saving model with a borrowing constraint.

Hence, insights from the consumption-saving literature can be adapted for the trading

model. For example, analogous to the insights from the permanent income hypothesis,

the informed investor “saves” more of his current information when expecting less future information

advantage (“saving for rainy days”) or more uncertainty about it (“precautionary

saving”) and smooths his information “usage” over time (“consumption smoothing”).

Models of learning about economic crises generate risk premia that rise at the onset of a crisis, but then fall as belief uncertainty fades. In contrast, empirical risk premia remain elevated during crises. We resolve this tension via leverage dynamics generated by the impact of learning on optimal default and capital structure decisions within a representative agent consumption-based model. Endogenously time-varying leverage creates a feedback loop: the learning-induced slow recovery in equity prices raises leverage, thereby further depressing equity values and keeping the equity premium and credit spreads persistently high as the crisis unfolds. We structurally estimate the model and show it closely matches the joint dynamics of consumption, equity risk premia, credit risk, and leverage, especially during crises, together with the term structure of credit risk and default probabilities.

We uncover financial professionals’ mental models—the reasoning they use to explain their quantitative forecasts. We organize our analysis around a framework of top-down attention, where analysts endogenously choose both a valuation method and how to allocate attention across variables, based on each variable’s relevance for valuation and the cost of acquiring information about it. Using the near-universe of 1.6 million equity analyst reports, we collect the valuation methods analysts adopt to compute their price targets. We then prompt large language models (LLMs) on a subset of 110,000 reports to extract 4.8 million lines of reasoning—each combining a topic, valuation channel, time horizon, and sentiment. To validate the reliability of our output, we introduce a multi-step LLM prompting strategy and new diagnostic tools. We document four main findings. (1) Analysts exhibit sparse mental representations, focusing on a limited set of topics, that are primarily related to top-line items, and forward-looking. (2) The choice of valuation methods and topic focus is closely linked. (3) There is substantial disagreement among analysts, and differences in attention weights to firm-specific variables are a bigger source of disagreement than differences in valuation weights on those variables. (4) Lastly, variation in mental models aligns with key asset pricing patterns both in the time-series and in the cross-section.

We examine how upstream technology vulnerability affects downstream firms' reliance on supply chain relationships and their subsequent strategic responses. Using patent litigation as an idiosyncratic shock to technology vulnerability, we find that upstream patent litigation reduces downstream supply chain reliance by 6.46% in the following year (fragility). We identify three underlying mechanisms: legal risks associated with technology, delivery uncertainties, and reputation risks driven by market pressures. Our results remain robust when exploiting the staggered adoption of the U.S. anti-troll laws as a quasi-natural experiment. Moreover, we find that when upstream technology supply is disrupted, downstream firms shift toward in-house development. By increasing R&D investments and recruiting external inventors, these affected customer firms enhance their innovation capabilities, especially in the disrupted technological domains (flexibility). This enables them to better integrate the technological supply chain and achieve greater product diversification.

The equity lending and option market both allow investors to decouple voting and cash flow rights of common shares. We provide a theory of this decoupling. While either market enables investors to acquire voting rights without cash flow exposure, empirical studies demonstrate a substantial difference in implied vote prices. Our model explains this surprising difference by uncovering the mechanism by which vote prices in the equity lending market are endogenously lower than those implied by the option market. We show that even though votes are cheaper in the equity lending market, activists endogenously choose to purchase votes in both markets.

Depositors maintain substantial, rate-insensitive account balances to facilitate payments and meet liquidity needs. Analyzing over 50 billion transactions from more than 1,400 U.S. banks, we find that, contrary to conventional wisdom, depositors actively manage their deposits and value payment convenience: faster settlement in deposit transfers increases gross deposit transfers between bank accounts while reducing total deposit balances. We extend the Baumol-Tobin framework with transfer delays to align with new empirical findings and quantify the impact of fast payments on depositor behavior. If banks uniformly adopt next-day settlement, ceteris paribus, low-interest precautionary and transactional deposits would decrease by approximately 30%, and related deposit transfer activities would increase by 40%. Our findings indicate that payment convenience significantly influences the sticky demand for deposits, which has important implications for deposit franchise value and monetary transmission.

We survey public employees across the United States about their preferences regarding retirement plan options, and in particular at what employer contribution rate public employees would agree to switch to a defined contribution (DC) plan on a forward-looking basis. Overall, 89.2% of respondents are willing to accept a hard freeze of their defined benefit (DB) plan and the introduction of a DC plan at some contribution level. Conditional on acceptance, the median minimum contribution rate that respondents would require—if no additional retirement benefits would accumulate under their existing plan—is 10.0% of payroll, while the mean is 18.2% of payroll. The perceived and actual financial generosity of the pension plan relates negatively to the acceptance rate and positively to the minimum required contribution. More senior employees are somewhat less likely to accept the DC option, but there is over 80% acceptance even among long-tenured employees. Consistent with typical DB accrual patterns in the presence of early retirement options, employees with around 20 years of service require the largest DC contributions to switch. Employees who perceive the financial stability of their current plan as weaker are, on average, more likely to accept a DC plan and at lower contribution levels. We find no statistically significant heterogeneity with respect to educational attainment or financial literacy, making an explanation of the results based on cognitive ability less likely. In comparison to the economic cost of prevailing DB plans, introducing DC options that are acceptable to employees could potentially improve the sustainability of pension systems across the United States without compromising employees’ satisfaction with their pension plan options.

We show that US monetary policy is transmitted internationally through the factor

structure of exchange rates. Following an unexpected easing, investment funds sell

safe and buy risky currencies. Global US banks, similarly, tilt their distribution of

foreign loan origination toward currencies of greater systematic currency risk. The

effects of monetary policy on currency flows and loans persist for several months and

feed into the leverage and real investment decisions of firms and, in particular, those

that operate using a high-risk currency. We argue that currencies’ factor exposures

are a lens through which we can understand the international transmission of US

monetary policy.

I analyze the welfare consequences of segmentation in financial markets. Venues vary in their mixture of information-motivated versus liquidity-motivated order flow. In a simple model, I consider the combinations of information-motivated investor welfare and liquidity-motivated investor welfare that can be achieved by some segmentation. This set’s Pareto frontier can (under certain conditions) be implemented by a simple class of segmentations, in which a fraction of information-motivated flow is segregated, while the remainder pools with liquidity-motivated flow. These results call into question the wisdom of the current regulatory framework as it applies to segmentation, e.g., in U.S. equities.

Using US administrative data on worker earnings, we show that increases in risk premia lead to lower earnings for lower-paid workers. These declines in earnings are primarily driven by job separations. We build an equilibrium model of labor market search that quantitatively replicates the observed heterogeneity in labor market dynamics across worker income levels. Our findings lend further support to the idea that fluctuations in risk premia are a key driver of unemployment and labor market dynamics. Importantly, our work illustrates the importance of the job destruction margin for understanding the heterogeneous dynamics of worker earnings over the business cycle.

We identify a common misconception that expected future changes in short-term interest rates predict corresponding future changes in long-term interest rates. People forecast similar shapes for the paths of short and long rates over the next four quarters. This is a mistake because long rates should already incorporate public information about future short rates and do not positively comove with expected changes in short rates. We hypothesize that people group short- and long-term interest rates into the coarse category of “interest rates,” leading to overestimation of their comovement. We show that this categorical thinking persists even among professional forecasters and distorts the real behavior of borrowers and investors. Expectations of rising short rates drive households and firms to rush to lock in long-term debt before further increases in long rates, reducing the effectiveness of forward guidance in monetary policy. Investors sell long-term bonds because they anticipate future increases in long rates. The resulting increase in supply and decrease in demand for long-term debt cause long rates to overreact to expected changes in short rates, and can help explain the excess volatility puzzle in long rates.

We find that the identity of the shareholder matters as much as, if not more than, the number of shares they hold: firms are twice as responsive to the votes of active funds as to those of passive funds. We provide suggestive evidence that this discrepancy arises not because active funds are better informed, but because they pose a greater threat of future action. Despite the significantly larger holdings of Vanguard, BlackRock, and State Street -- the so-called “Big Three” funds -- their votes carry no more weight than those of an average active fund. These findings suggest that concerns over the influence of ever-larger index funds may be overstated.

We take a mechanism design approach to dynamic capital allocation and risk-sharing between an investor (the principal) and asset managers (the agents).

Incentive-compatibility implies that managers with better idiosyncratic performance get larger fees, capital, and continuation utilities.

This generates an endogenous distribution of utilities across managers, which is a state variable of the optimal control problem of the principal.

With a continuum of agents, this gives rise to a Bellman equation in an infinite-dimensional space, which we solve with mean-field techniques.

With CRRA utilities, optimal compensation is proportional to assets-under-management and

costly exposure to idiosyncratic risk lowers risky investment.

Small businesses in the US are frequently excluded from borrowing through traditional term loans or lines of credit and rely instead on highly standardized, high-interest rate business credit cards to meet their financing needs. Are rates high because this credit is costly to provide or because lenders charge high markups? We document that average credit card utilization is almost 30% and is higher for firms facing significant cashflow volatility. While the unconditional delinquency rate is low, it is strongly correlated with utilization, potentially making cards expensive to provide because borrowers make interest-generating draws when they are least able to repay. We develop and estimate a structural model of firms’ card demand, utilization, and default choice, accounting for imperfect competition and the correlation between utilization and default. We find that while the correlation between utilization and delinquency leads to modestly higher rates, they are primarily explained by markups rather than lender costs, making business card provision highly profitable. In counterfactual analyses we show that under systematic stress scenarios, absent large shocks to lender funding costs, lender profits tend to rise in times of borrower stress, as higher revenue from utilization more than offsets increases in delinquency. Finally, we evaluate proposed capital regulations that add a portion of undrawn credit limits to bank risk-weighted assets. Such rules reduce bank credit provision and push some lending outside the regulated banking sector, while modestly reducing firm surplus. Because credit card lending tends to be more profitable in times of stress, such regulations may be counterproductive for bank stability.

Insufficient rental supply in American suburbs limits mobility for financially constrained households unable to afford homeownership. I find that reallocating suburban single-family homes to rentals reduces spatial inequality by increasing access to desirable neighborhoods for non-White and younger households. In my reduced-form analyses, I exploit the entry of large-scale corporate landlords and leverage property-level data on home prices, rents, and tenant characteristics. Corporate landlords pay a 9% premium to acquire owner-occupied homes, increasing rental supply in suburbs where it is scarce and expensive. This expansion of rental supply lowers rents while raising home prices. To assess the distributional consequences, I develop a quantitative spatial equilibrium model with segmented housing markets. Converting ownership homes to rentals benefits down payment-constrained households by reducing barriers to high-amenity neighborhoods. However, the estimated non-pecuniary benefits of homeownership suggest that households who can marginally afford a home lose out.

Abstract: We study the impact of the last five decades of financial globalization on world GDP and income distribution, employing a novel multi-country dynamic general equilibrium model that embeds a demand system for international assets. We introduce, estimate and validate new country-level measures of inward and outward Revealed Capital Account Openness (RKO), which are derived from wedge accounting. The implementation of our framework requires only minimal data, which is available as early as 1970 (national income accounts, external assets and liabilities positions). Our RKO wedges reveal enormous heterogeneity in the pace of capital account liberalization, with richer countries liberalizing much faster than poorer ones. We call this pattern Unbalanced Financial Globalization. We then utilize our model to simulate a counterfactual trajectory of the global economy, where the RKO wedges are fixed at their pre-globalization levels. We find that unbalanced financial globalization led to a worsening of capital allocation (lowering world GDP by 1.4%), a 10% rise in the cross-country dispersion of GDP per capita, lower wages in poorer countries and lower cost of capital in high-income countries. These findings stand in sharp contrast to the predictions of standard models of financial markets integration, where capital account barriers decline symmetrically across countries. We also study counterfactual globalization patterns where countries open their capital account in a symmetric or convergent fashion, and find that these scenarios produce diametrically opposite effects (significant improvements in capital allocation efficiency and lower cross-country inequality, higher wages in poor countries, etc..). These findings underscore the pivotal role played by country heterogeneity in shaping the real effects of capital markets integration.

Abnormal returns for stocks added to or removed from the S&P 500 index (known as index effect) have been declining, despite a sharp rise in demand shifts of passive funds. I explore (i) whether these abnormal returns during index reconstitutions stem from passive demand shifts or information content, and (ii) why they have decreased over time. My study isolates pure demand-based price impact using a novel identification strategy that analyzes incumbent stocks–index constituents whose portfolio weights adjust due to the differing sizes of added and deleted firms. I find that (i) the index effect was primarily driven by information prior to 2000 while passive demand became predominant thereafter, and (ii) the declining index effect is a direct result of the flattening of stocks’ demand curves in the context of index reconstitutions, independent of the informativeness of the index committees decisions. This flattening is associated with decreased arbitrage risk and enhanced cross-stock substitution.

We estimate the evolution of permanent and transitory income risk across the income

distribution and over time using newly-digitized, longitudinally-linked Census-IRS tax return data, which cover the universe of US tax returns from 1969 to 2019. Over our sample window, the variance of permanent income shocks rises by over 65% for those in the 95th percentile or higher of the income distribution — a group which collectively owns a sizable fraction of financial wealth. Because our Kalman filter yields a panel of permanent and transitory shocks for every individual, we validate that top earners indeed face “risk” by documenting that negative permanent shocks coincide with observable proxies for adverse life events, changes in asset positions, and financial distress. Using capitalized interest and dividend income from 1040s, we also show that high earners save significantly more in response to greater permanent income risk. We then integrate our income process into a Bewley-Huggett-Aiyagari model in order to quantify the effects of rising permanent income

risk on interest rates. Rising permanent income risk at the top of the income distribution pushes interest rates down by 0.6% (from 3.5% to 2.9%), explaining roughly 25% of the decline in interest rates observed since the 1970s. Moreover, rising permanent income risk at the top of the income distribution provides a distinct and complementary rationale to non-homothetic preferences for increased savings rates among the richest U.S. households.

Are belief dynamics or risks and risk attitudes more important for asset pricing? Allowing both, I use survey data combined with subjective-belief versions of stochastic discount factor (SDF) volatility bounds to shed new light on this classic question. I estimate lower bounds for the volatility of the SDF attributable to (i) risks relevant for investor marginal utility, versus (ii) subjective belief dynamics. The estimates suggest that risks, particularly long-term risks, make up at least half of SDF volatility. An example extrapolation model with a modest direct contribution of beliefs to SDF volatility (about 25%) can account for my estimates. This example also highlights the potential for a novel mechanism, subjective risk, which is the indirect impact of beliefs on asset prices through induced marginal utility volatility.

This paper shows that remote work increases wage workers' transition into entrepreneurship. Using big data on Internet activities, we create a novel firm-level measure of remote work. We show that firms with greater increases in remote work during the pandemic are more likely to see their employees subsequently becoming entrepreneurs. This holds both unconditionally and relative to other types of job turnovers. We establish causality using instrumental variables and panel event study. The spawning response is stronger among younger and more educated employees, and the marginally created businesses are not of low quality. The effect is not driven by employee selection, preference change, or forced turnover. Rather, remote work increases spawning by providing the time and downside protection needed for entrepreneurial experimentation. We calibrate that at least 13.4% of the post-pandemic increase in new firm entry can be attributed to spawning from remote work.

Firms are increasingly adopting predictive artificial intelligence (AI) to improve decision-making by combining advanced data analysis with human judgment. While AI enhances managers' ability to predict project success probabilities, we show that its adoption can unintentionally backfire, leading to suboptimal decisions that diminish shareholder profits. Specifically, the additional information provided by AI raises the likelihood that the manager's estimate of the project's success probability falls within a range where the sensitivity of managerial compensation to their decision decreases significantly, incentivizing the pursuit of private benefits over shareholder interests. To address this, we propose AI-contingent contracts that align managerial compensation with AI predictions, thereby mitigating AI-induced agency conflicts. Our study highlights the necessity of updated corporate governance structures to effectively navigate the challenges posed by AI-augmented decision-making.

We study how bankers' expertise in identifying productive projects impacts the economic structure. We examine the U.S. 1980s interstate banking deregulation as a quasi-natural experiment on the structural transformation of non-financial sectors across states. The economic structure of those states with deregulated banking sectors became closer to each other because bankers brought their expertise to find productive projects across state borders. We build a two-state, two-sector search-and-matching model where bankers' expertise determines sector-specific matching efficiency between bankers and producers. As deregulation makes bankers' expertise more accessible across states, the economic structure converges across states, especially when firms depend on external funding. Outputs are concave in the level of bank penetration. The relationship between output and bank penetration is concave: moderate levels of penetration reduce credit misallocation and boost output, but excessive penetration results in inefficiencies as guest banks crowd out local banks, diverting resources from more productive sectors.

We conduct the first study exploring the application of large language models (LLMs) to mortgage underwriting, using an audit study design that combines real loan application data with experimentally manipulated race and credit scores. First, we find that LLMs systematically recommend more denials and higher interest rates for Black applicants than otherwise-identical white applicants. These racial disparities are largest for lower-credit-score applicants and riskier loans, and exist across multiple generations of LLMs developed by three leading firms. Second, we identify a straightforward and effective mitigation strategy: Simply instructing the LLM to make unbiased decisions. Doing so eliminates the racial approval gap and significantly reduces interest rate disparities. Finally, we show LLM recommendations correlate strongly with real-world lender decisions, even without fine-tuning, specialized training, macroeconomic context, or extensive application data. Our findings have important implications for financial firms exploring LLM applications and regulators overseeing AI's rapidly expanding role in finance.

In their income statements, firms report their foreign exchange (FX) transaction income, i.e., the overall effect of exchange rate-induced revaluations of their monetary items, net of any financial hedging. Using such publicly available data, we find a strong comovement between exchange rate shocks and FX transaction income at the firm, industry, and aggregate levels, implying that financial hedging is limited. The FX exposure increases with international trade and foreign currency debt. The FX transaction income passes through to the firms' profits, payouts and subsequent investments, implying that operational hedging is also limited, and that exchange rate changes affect firms.

We study incentives for post-trade information disclosure in the over-the-counter financial markets. In a setting where execution prices alone do not fully capture the value of the traded assets, we model decisions of investors to hide or reveal information embedded in unexecuted offers. Our model explains why investors, requesting quotes from multiple dealers in the corporate bond market, might choose to conceal the runner-up offer — the cover — from the winning dealer even though the increased informational opacity decreases dealers’ incentives to win the trade and worsens their quotes. Investors conceal covers if they trade frequently, gains from trade are high, or uncertainty about bond values is low. We discuss the implications for market liquidity, fragmentation, and the design of electronic RFQ platforms.

Over the last half-century, economic growth stagnated but stock-market wealth boomed. I present evidence that declining innovation productivity reconciles these trends. At the macro level, I document that R&D spending has fallen relative to value, while M&A spending has doubled relative to R&D. At the micro level, most of the increase in aggregate valuation ratios is explained by a reallocation of sales shares toward high-valuation firms. Using a Schumpeterian model of growth and asset prices, I find that declining innovation productivity explains these facts. When innovation productivity falls, R&D falls and M&A rises. This concentrates production into the hands of the most efficient (high-valuation) incumbents, causing aggregate value to boom. Quantitatively, this explains most of the decline in growth and the rise in valuations. It also helps explain other salient trends, including declining firm entry, rising concentration, and falling interest rates. While stock-market wealth boomed, the present value of consumption (consumer welfare) stagnated with output.

This paper develops a theory of the elasticity of preference for early resolution of uncertainty (PER) that parallels the Arrow-Pratt measure of risk aversion in expected utility theory. We demonstrate that the local welfare gain of early resolution of uncertainty is equal to the product of the elasticity of PER and the conditional variance of continuation utility. We illustrate how asset market data can be used to estimate the elasticity of PER and how this measure can be used to compute the welfare gain for various experiments of early resolution of uncertainty.

Using resume data on over 20 million U.S. workers, we find that the flow of employees between a pair of firms sharply drops by about 20% when the firms start to share a director on their boards. We find no trend prior to initiation, and the reduced flow persists throughout the overlapping period. This relationship is stronger in settings where firms are more likely to benefit from lower competition for each other’s employees and is most pronounced for higher-skilled employees. The results suggest that shared directors facilitate cooperative behavior in the labor market.

Banks are made of contracts. For a bank to finance productive investment by issuing riskless, money-like claims, its organizational structure (e.g., sole proprietorship, partnership, or public ownership), capital structure, and its bankers' compensation contracts must be jointly designed to induce banker effort and discourage risk-taking. Our model explains why bankers receive high pay for producing mediocre outcomes, and why pure charter value (or market value of equity) is insufficient to prevent banker risk-taking. Outside shareholders, contributing book equity, are useful despite introducing another layer of agency problems. It is efficient for shareholders to create a `big' bank with multiple bankers and their respective projects and finance those projects with joint liabilities. When bankers' incentive contracts are opaque, each banker's pay should depend on the entire bank's performance even though he exerts control only on his own project.

This paper examines the comparative advantages of shadow banks using novel credit bureau data on 653 million formal retail loans in India. Proxying credit demand shocks with weather variation, we show that Fintechs respond more than other lenders in uncollateralized markets. Conversely, non-Fintech shadow banks are more responsive in collateralized markets. Both show stronger responses for borrowers with low credit scores or no credit history. Exploiting the geographic heterogeneity in the adoption of digital payments technology we document the importance of technology for Fintechs. Leveraging four natural experiments across lenders, time, and products, we establish the importance of lax regulation and physical presence for non-Fintech shadow banks. Our results suggest that the dominant comparative advantages of shadow banks differ across market segments.

A variety of distortions, such as financial constraints and behavioral biases, have been proposed to explain deviations from canonical consumption-savings models. We develop a new sufficient statistics approach to measure the impact of such distortions on consumption as a wedge between actual consumption and a counterfactual "frictionless" consumption. We calculate these wedges for a population of predominantly low-income US consumers using a new survey of economic beliefs linked to bank account transactions data. We find that consumption choices are significantly distorted both upwards and downwards. The median wedge is 40% of frictionless consumption in absolute value, with 51% having negative wedges (under consuming) and 49% having positive wedges (over-consuming). Because alternative models of distortions imply different properties of wedges, estimates of wedges can be used as a diagnostic to distinguish between models. Notably, financial constraints only generate negative wedges, indicating that additional or alternative distortions (such as present bias or consumer inertia) are necessary to rationalize the consumption decisions of low-income households.

Using comprehensive new contract level data (EMIR) for the period 2019-2023, we explore how the FX derivative trading by European funds compares to a feasible theoretical benchmark of optimal hedging. We find that hedging behavior by all fund types is often partial, unitary (i.e., with a single currency focus), and sub-optimal. Overall, the observed FX derivative trading does not significantly reduce the return risk of the average European investment funds, even though optimal hedging strategies could do so without incurring substantial trading costs.

This paper investigates hidden liquidity on U.S. equity exchanges and how to find it. Despite the National Market System’s (NMS) goal of transparent trading, we show that orders hidden from the NMS provide liquidity for 40% of the trading volume in our sample, rising to 75% of the dollar volume traded for high priced stocks. We demonstrate how price improvement on exchanges depends on interacting with hidden liquidity, especially with non-displayed orders. Leveraging big data and machine learning, we develop an algorithm that dynamically predicts where price-improving non-displayed orders are likely to appear, illustrating how AI-driven models can allow broker-dealers to more effectively meet their best execution obligations.

Financial wealth inequality and long-term real interest rates track each other closely over the post-war period. We investigate how much of the increase in measured financial wealth inequality can be accounted for by the decline in rates, and study the implications for inequality in total wealth (lifetime consumption). To do so, we measure the exposure of householdlevel financial portfolios to interest rates. We find enough heterogeneity in household portfolio revaluations to explain the entire rise in financial wealth inequality since the 1980s. A standard incomplete markets model calibrated to these data implies that the low-wealth young lose when rates decline, while the high-wealth old gain.