We recover a stochastic discount factor (SDF) for asset returns from a firm’s investment Euler equation. Given a parametric statistical specification of the SDF and profitability process, we solve for the firms’ optimal investment decision with approximate analytical solutions and provide a dissection of the determinants of real investment. We estimate a specification of the model to discipline the free parameters of the SDF by matching moments of both aggregate real quantities and asset prices. We use the estimated parameters to recover the latent SDF from data on aggregate investment rates, risk-free rates, and profitability growth rates. Innovations in the recovered SDF are driven dominantly by innovations in investment rates and marginally by innovations in risk-free rates and profitability growth rates. The recovered SDF exhibits strong counter-cyclicality with large jumps in recessions and prices standard Fama-French portfolios out of sample reasonably well. Our model allows us to explicitly characterize the risk-free rate, the equity premium, the term structure of interest rates, and the term structure of equity risk premia. The framework we propose here is general and can be extended to accommodate several additional aggregate shocks and frictions that have been proposed in the literature.

The recent linear factor models (e.g., Fama and French (2015) and Hou, Xue, and Zhang (2015)) use total asset growth as the measure of investment, largely due to its stronger return predictive power than its components such as the long-term and current asset growths. We offer an explanation of the latter finding by extending the standard q theory of investment into a two-capital setup in which firms use both long-term and current asset as production inputs. We uncover a novel asset imbalance channel which creates negative comovement between current and long-term asset growths that are unrelated to discount rate. This comovement is muted in the total asset growth, giving rise to its stronger return prediction. Once controlling for this comovement, the return predictive power of current and long-term asset growths substantially improves. Furthermore, we document strong evidences for the model's prediction that the asset growth effects are more prominent among firms with low asset imbalance. Our results support the q theory based explanation for the asset growth effect.

We argue that leasing is an important mitigation channel of credit constraint-induced capital misallocation. However, the existing literature neither includes leased capital in empirically measuring capital allocation efficiency, nor recognizes its mitigation role economically. Empirically, we show that neglecting leased capital and overlooking its mitigation effect leads to significant overestimations of capital misallocation and the cyclicality of capital misallocation. Theoretically, we develop a dynamic general equilibrium model with heterogeneous firms, collateral constraint, and an explicit buy versus lease decision to demonstrate and quantify the role of leasing in mitigating capital misallocation. We provide strong empirical evidence to support our theory.

This paper provides a general framework for analyzing the stability of stablecoins, cryptocurrencies pegged to a traditional currency. We study the problem of a monopolist platform that can earn seigniorage revenues from issuing stablecoins. We characterize stablecoin issuance-redemption and pegging dynamics under various degrees of commitment to policies. Even under full commitment to issuance, the stablecoin peg is vulnerable to large demand shocks. Backing stablecoins with collateral helps to stabilize the platform but does not provide commitment. Decentralization of issuance, combined with collateral, can act as a substitute for commitment.

We estimate the run risk of fiat-backed stablecoins. A run on stablecoins would lead to the fire sales of US dollar assets like bank deposits, Treasuries, commercial papers, and corporate bonds. Our model shows that the possibility of panic runs persists even though general investors only trade stablecoins in competitive secondary markets with flexible prices. This is because stablecoins engage in liquidity transformation and the fixed price at which a set of authorized participants (APs) redeem stablecoins for cash from the issuer reinstates run incentives among secondary-market investors. A more concentrated AP sector acts as a firewall between secondary and primary markets to mitigate runs but gives rise to larger secondary market price dislocations, implying a tradeoff between run risk and price stability. We collect a novel dataset on stablecoin redemptions, trading, and reserve assets to calibrate our model. For the largest stablecoin, Tether (USDT), our estimates imply a run probability of 17.04% in September 2021 which decreases to 3.45% in March 2022 as reserve assets became more liquid.

I show that the growing trend in financial services digitalization has introduced a novel dimension along which commercial banks compete. Based on the analysis of newly hand-collected data on the launch date of banks' smartphone apps, I show that small community banks (SCBs) have been slow to provide mobile banking services to their customers. As a consequence, when the local mobile infrastructure improves--a positive shock to smartphone apps' usage and value--they lose deposits to larger, better-digitalized banks. Further, this dynamic negatively affects their small business lending, for these institutions have historically relied on information and liquidity synergies with deposits to maintain their competitive advantage in such markets. Larger banks and FinTech firms prove to be imperfect substitutes in this setting, and the local economy benefits less from digitalization in areas where SCBs had an important presence before its advent.

Asset pricing theory predicts high expected returns are a compensation for risk. However, high expected returns might also represent anomalies due to frictions or behavioral biases. We propose two complementary tests to assess whether risk alone can explain differences in expected returns, provide general-equilibrium foundations, and study their properties in simulations. The tests account for any risk disliked by risk-averse individuals, including high-order moments and tail risks. The tests do not rely on the validity of a factor model or other parametric statistical models. Empirically, we find risk cannot explain a large majority of differences in expected returns of characteristic-sorted portfolios.

We propose a method to estimate latent asset-pricing factors that incorporates economically motivated targets for both cross-sectional and time-series properties of the factors. Cross-sectional targets may capture the shape of loadings (monotonicity of expected returns across characteristic-sorted portfolios) or the pricing span of exogenous state variables (macroeconomic innovations or intermediary-based risk factors). Time-series targets may capture overall expected returns or mispricing relative to a benchmark reduced-form model. Using a large-scale set of assets, we show that these targets nudge risk factors to better span the pricing kernel, leading to substantially higher Sharpe ratios and lower pricing errors than conventional approaches.

U.S. stocks' response to inflation surprises is, on average, robustly negative. Stocks' response to positive inflation surprises shows much more pronounced time-series variability than their response to negative inflation surprises. In our sample, stocks react significantly to positive inflation surprises only when there is a contemporaneous change in monetary policy expectations. In the cross-section, firms with low net leverage, large market capitalization, high market beta, low book-to-market, and low market power (i.e. low markups) are especially susceptible to inflation surprises.

I study the effects of private equity (PE) buyouts on labor productivity using a novel micro-data on investments in PE funds and PE buyout deals, combined with confidential Census data. I show that while PE increased productivity at target firms until 2011, it substantially decreased productivity post 2011. In the time series, the decrease in labor productivity is correlated with an increase in capital from the most underfunded public pensions. In the cross-section, I show that firms financed predominantly by the most underfunded public pensions experience a -5.2% annual change in labor productivity, as compared to firms financed by other investors which experience a +5.2% annual change. Firms supported by low quality PE funds face productivity decreases. The key mechanism is the notion of desperate capital, where the most underfunded public pensions allocate capital to low quality GPs, and realize lower PE returns. I introduce a novel instrument of public unionization rates to establish support for underfunded positions causing selection into low quality GPs, which ultimately leads to capital misallocation within private markets.

This paper demonstrates that private equity buyouts relax firms' financing constraints by enabling them to borrow against cash flows. Unlike comparable non PE-backed firms that primarily use asset-based debt, PE-backed firms rely extensively on cash flow-based debt subject to earnings-based borrowing constraints, and their borrowing and investments exhibit high sensitivity to earnings. Thus, private equity raises both the level and cash flow sensitivity of leverage. Documenting that PE sponsors inject equity and stabilize earnings in distress, we highlight how PE sponsors' involvement in distress resolution serves as a key mechanism that enables cash flow-based borrowing.

This paper studies the interactions between corporate law and venture capital (VC) exits by acquisitions, an increasingly common source of VC-related litigation. We find that transactions by VC funds under liquidity pressure are characterized by (i) a substantially lower sale price; (ii) a greater probability of industry outsiders as acquirers; (iii) a positive abnormal return for acquirers. These features indicate the existence of fire sales, which often satisfy VCs' liquidation preferences but hurt common shareholders, leaving board members with conflicting fiduciary duties and litigation risks. Exploiting an important court ruling that establishes the board’s fiduciary duties to common shareholders as a priority, we find that after the ruling maturing VCs become less likely to exit by fire sales and they distribute cash to their investors less timely. However, VCs experience more difficult fundraising ex-ante, highlighting the potential cost of a common-favoring regime. Overall, the evidence has important implications for optimal fiduciary duty design in VC-backed start-ups.

We study the optimal execution problem in a principal-agent setting. A client (e.g., a pension fund, endowment, or other institution) contracts to purchase a large position from a dealer at a future point in time. In the interim, the dealer acquires the position from the market, choosing how to divide his trading across time. Price impact may have temporary and permanent components. There is hidden action in that the client cannot directly dictate the dealer’s trades. Rather, she chooses a contract with the goal of minimizing her expected payment, given the price process and an understanding of the dealer’s incentives. Many contracts used in practice prescribe a payment equal to some weighted average of the market prices within the execution window. We explicitly characterize the optimal such weights: they are symmetric and generally U-shaped over time. This U-shape is strengthened by permanent price impact and weakened by both temporary price impact and dealer risk aversion. In contrast, the first-best solution (which reduces to a classical optimal execution problem) is invariant to these parameters. Back-of-the- envelope calculations suggest that switching to our optimal contract could save clients billions of dollars per year.

We show that the trading-fee breakdown (fee pricing) depends on the distribution of investor gains-from-trade relative to the tick size. Absent price discreteness, an increase in investor gains-from-trade increases the total fee proportionally, but the fee breakdown has no effect. With price discreteness, the fee breakdown can mitigate the loss of welfare due to difficulty in trading when gains-from-trade are small relative to the tick size. However, when gains-from-trade are large, the exchange fee breakdown plays only a small role and exchanges extract rents from investors gains-from-trade by increasing total fee. The resulting gap between welfare relative to fees set by a Social Planner can be large. Consequently, a regulator can improve welfare substantially by imposing a cap on exchange fees.

I study a futures market model with imperfectly competitive traders, some precluded to trade spot (financial traders), some not (physical traders). I first show that, suprisingly, introducing futures makes physical traders worse off without financial traders, because physical traders seek to influence futures payoff by trading spot, and choose negative hedging ratios. Financial traders improve futures market liquidity, so that physical traders adopt positive hedging ratios when liquidity is sufficiently improved. However financial traders also raise prices when they are long, which benefits high-inventory physical traders at the expense of low-inventory physical traders. Overall, physical traders with high or very low inventory are better off with financial traders than without futures, while traders with intermediate inventory and trading in the same direction as financial traders lose. I also show that imperfect competition makes futures and spot market imperfect substitutes, implying a spot-futures basis.

Assessing the Paycheck Protection Program (PPP) --- a financial rescue program designed to cover firms' payroll expenses during the Covid-19 pandemic --- I document that the decision of managers whether to reveal the bailout loan details to the public dominates the disclosure strategy of firms that engage in relationship lending, especially for longer and more intense relationship. Examining potential economic channels, I find that strategic disclosure is unlikely to be driven by habit formation or liquidation concerns. Instead, the evidence suggests that strategic disclosure is driven by relationship capital considerations, where firms incur the costs of disclosing unfavorable news to reduce lenders' monitoring concerns in exchange for future lending benefits. Overall, the findings highlight a novel economic channel for releasing unfavorable information in which relationship lending has a disciplinary effect on firms' strategic disclosure, especially during times of crisis when debt monitoring becomes more relevant.

While banks historically offer a litany of different credit and depository products to their local customers, technological innovation and consumer preferences have spurred growth of online banks specializing in particular activities across broad geographic areas. This paper analyzes the unintended consequences of online banks' specialized lending model on small business lending. We use loans in the SBA program, which provides guarantees to motivate partner-lenders to lend to higher-risk borrowers. We find that online banks expand credit access, lending in disadvantaged and underserved geographies. While providing credit, online banks target higher guarantees, generating a cross-subsidy from traditional lenders, borrowers, and the government to online lenders.

We analyze the effect of corporate investments in fintech startups on startup performance and on the future performance of investing firms. Corporate investment in fintech startups is associated with greater successful exit likelihood; more and higher quality innovation; and higher inflow of high-quality inventors. We establish causality using an IV analysis. A stacked difference-in-differences analysis shows that such investments enhance the product market performance and equity market valuation of corporate investors belonging to the financial services sector, but not those in the non-financial sector. We show that formation of strategic alliances between investors and fintech startups drive these performance improvements.

Virtual-only shareholder meetings have become dramatically more common following Covid-19. By creating a unique dataset documenting questions shareholders submitted at virtual-only shareholder meetings, I document that precisely when shareholders vote against the directors proposed by management, indicating contention with management, firms are likely to ignore shareholders’ questions. Similarly, I show that when such low-support votes prevail, transcripts of virtual-only shareholder meetings reveal that firms are likely to explicitly limit the scope of questions they are willing to address at the meeting, and not reveal at the meeting precise vote outcomes. Companies that use such methods have significantly more limited communication at virtual-only shareholder meetings, and these methods are significantly more common at virtual-only meetings relative to inperson meetings. Overall, relative to in-person meetings, virtual-only meetings are shorter and dedicate less time to addressing shareholders’ concerns

This paper examines racial preferences of shareholders in the context of corporate director elections. We document a higher propensity of mutual fund managers to vote for director nominees who match their racial/ethnic identity. The same-race preference is more prevalent in elections involving nominees receiving negative recommendations from the dominant proxy advisor ISS. We rule out various potential channels --statistical discrimination, value maximization, conflicts of interest, and social networks-- using high-dimensional fixed effect models along with heterogeneity tests. Additional evidence indicates the documented same-race preference aligns with taste-based biases, and has important consequences for labor market outcomes of director candidates.

This paper examines the voting behavior of women-led mutual funds. We find that women-led mutual funds are more likely to support environmental and social (ES) proposals, but not governance ones, and their voting support is more pronounced for proposals explicitly related to ES risks. Women-led mutual funds are more likely to vote with management in firms headed by female CEOs. They are also more likely to support female candidates in director elections, especially so when there is a shortage of female directors. Finally, women-led mutual funds are not more likely to follow ISS recommendations than other funds. Our results suggest that gender differences in fund management teams influence their voting behavior.

We examine an emerging phenomenon that talented employees leave successful entrepreneurial firms to join less mature start-ups. Using proprietary person-level data and private firm data, we find that the presence of these “serial venture employees” positively predicts their new employers’ future success in terms of exit likelihoods, size growth, venture capital financing, and innovation productivity. Such predictive power is more likely driven by a screening/matching channel rather than venture labor’s nurturing role. Our paper sheds light on an underexplored pattern of inter-firm labor flow, which provides a nonfinancial yet value-relevant signal about private firms for investors and stakeholders.

How do venture capitalists (VCs) make investment decisions? Using a large administrative data set on French entrepreneurs that contains VC-backed as well as non-VC-backed firms, we use algorithmic predictions of new ventures’ performance to identify the most promising ventures. We find that VCs invest in some firms that perform predictably poorly and pass on others that perform predictably well. Consistent with models of stereotypical thinking, we show that VCs select entrepreneurs whose characteristics are representative of the most successful entrepreneurs (i.e., characteristics that occur more frequently among the best performing entrepreneurs relative to the other ones). Although VCs rely on accurate stereotypes, they make prediction errors as they exaggerate some representative features of success in their selection of entrepreneurs (e.g., male, highly educated, Paris-based, and high-tech entrepreneurs). Overall, algorithmic decision aids show promise to broaden the scope of VCs’ investments and founder diversity.

We estimate the unobservable effort that firms put into boosting their efficiency. Identification comes from a model in which firms accumulate capital but also choose a flow of effort that controls efficiency period by period. Model estimates show that, for all cohorts and industries, young firms choose relatively more growth and old firms choose more efficiency. Amongst young firms, higher capital growth predicts higher markups in the long-term, but increases the risk of not surviving into maturity. Our model estimates help explain the priced firms’ exposures to the profitability and in- vestment risk factors of the investment CAPM.

We show that U.S. firms cut imports by 11.1% and are 4.2% more likely to terminate a trade relationship when their international suppliers are hit by environmental and social (E&S) scandals. These trade cuts are larger for publicly-listed U.S. importers facing high E&S investor pressure and lead to cross-country supplier reallocation, suggesting that E&S preferences in capital markets can have real effects in far-flung economies. Larger trade cuts around the scandal result in higher supplier E&S scores in subsequent years, and in the eventual resumption of trade. Our results highlight the role of customers’ exit in ensuring suppliers’ E&S compliance along global supply chains.

We study how institutional investors that join climate-related investor initiatives are actively decarbonizing their equity portfolios. Decarbonization could be achieved by re-weighting portfolios towards lower carbon emitting firms or alternatively via targeted engagements with portfolio companies to reduce their emissions. Our analysis suggests that portfolio re-weighting is the predominant strategy to green their portfolios, in particular by investors based in countries with carbon emissions pricing schemes. We do not uncover much evidence of engagement even after the 2015 Paris Agreement. Furthermore, we find no evidence that climate-conscious investors allocate capital towards firms developing climate patents, but they do re-weight towards firms starting to generate green revenues. Overall, our analysis raises doubts about the effectiveness of investor-led initiatives in reducing corporate emissions and helping an all-economy transition to “green the planet”.

This paper studies the systematic effect of environmental regulations on firms by constructing a time-varying and industry-specific measure of EPA regulatory restrictions over 1974-2021. Identifying industries in years that experience significant changes in regulation restrictions, we find that stricter EPA regulations are associated with an improvement in firm value. Investigating the potential underlying mechanism, we find that the positive valuation effect is more pronounced for firms with myopic managers or weak shareholder monitoring, where agency frictions hinder value-enhancing investments. We further find that stricter EPA regulations induce green innovations and increase the marginal performance of R&D in regulated firms, reflecting an increase in innovation incentives. Collectively, our findings suggest an unintended benefit of stricter environmental regulations: serving as an external governance mechanism by holding managers accountable for their decisions, and in turn, reducing agency frictions to induce value-enhancing investments.

We study sources and implications of undiversified portfolios in a production-based asset pricing model with financial frictions. Households take concentrated positions in a single firm exposed to idiosyncratic shocks because managerial effort requires equity stakes, and because investors gain private benefits from concentrated holdings. Matching data on returns and portfolios, we find that the marginal investor optimally holds 45% of their portfolio in a single firm, incentivizing managerial effort that accounts for 4% of aggregate output. Investors derive control benefits equivalent to 3% points of excess return, rationalizing low observed returns on undiversified holdings in the data. A counterfactual world of full diversification would feature higher risk free rates, lower risk premiums on fully diversified and concentrated assets, less capital accumulation, yet higher consumption and welfare. Exposure to undiversified firm risk can explain approximately 40% of the level and 20% of the volatility of the equity premium. A targeted subsidy that decreases diversification improves welfare by increasing managerial effort and reducing financial frictions.

Young firms' contribution to aggregate employment has been underwhelming. However, we show that a similar trend is not apparent in their contribution to aggregate sales or stock-market capitalization, suggesting that these firms have exhibited a high ratio of average-to-marginal revenue-product-of-labor. We study the implications of the arrival of such firms in a standard model of dynamic firm heterogeneity, and show that their arrival provides a unified explanation for a large number of facts related to the decline in ``business dynamism''. We provide an analytical framework to gauge the quantitative impact of the decline in business dynamism on aggregate economic activity.

Business cycles are typically associated with lower firm cashflows and higher discount rates. We show that these two components have very different implications for labor income growth. Higher discount rates lead to lower worker earnings for workers at the bottom of the income distribution; these declines are primarily driven by job separations. By contrast, lower cashflow (or productivity) news is followed by declines in earnings for workers in the top of the income distribution, with most of the effect coming from the intensive margin. We build an equilibrium model of labor market search that quantitatively replicates these facts. The model has several implications regarding the role of discount rates in generating unemployment fluctuations; the drivers of the low level of cyclicality of aggregate worker earnings; and the redistributive effects of business cycle fluctuations and monetary policy. These implications are consistent with the data.

Since the 1980s many countries have reformed the institutional framework governing their central banks to increase operational independence. Collecting systematic biographical information, international press coverage, and independent expert opinions, we find that over the same period appointments of central bank governors have become more politically motivated, especially after significant legislative reforms aiming to insulate central banks and their governors from political interference. We also show that politically-motivated appointments reflect lower de facto independence, and are associated with worse inflation and financial stability outcomes. Given the increase in central banks' powers worldwide, our findings inform the debate about their political accountability and credibility.

When the Federal Reserve (Fed) expanded its balance sheet via quantitative easing (QE), commercial banks financed reserve holdings with deposits and reduced their average maturity. They also issued lines of credit to corporations. However, when the Fed halted its balance-sheet expansion in 2014 and even reversed it during quantitative tightening (QT) starting in 2017, there was no commensurate shrinkage of these claims on liquidity. Consequently, the past expansion of the Fed’s balance sheet appears to have left the financial sector more sensitive to potential liquidity shocks when the Fed started shrinking its balance sheet, necessitating Fed liquidity provision in September 2019 and again in March 2020. The banks most exposed to liquidity claims suffered the most drawdowns and the largest stock price declines in March 2020. This evidence suggests that the shrinkage of central bank balance sheets must be handled with utmost care as it may involve tradeoffs between monetary policy and financial stability.

Exploiting the introduction of the ECB’s tiering system for remunerating excess reserve holdings, we document the importance of money market access for bank lending. We show that the two-tier system produced positive wealth effects for banks with excess reserves and encouraged banks with unused exemptions to increase their participation in the money market to obtain liquidity. This ultimately decreased money market fragmentation and enhanced the transmission of monetary policy. We provide evidence that stronger money market relationships reduce the precautionary behavior of financially constrained banks with unused allowances, which consequently extend more credit than other banks, including those with excess liquidity whose valuations increased the most.

This paper studies the desirability of programmable payments where transfers are automatically executed conditional upon preset objective criteria. We do so by studying optimal payment arrangements in a framework that captures a wide range of economic relationships between two parties. Our framework stacks the cards in favor of programmable payments by considering an environment without legal recourse. The results show that the optimal payment arrangements for long-term economic relationships consist predominantly of simple direct payments. Direct payments increase the surplus by avoiding the liquidity cost of locking-up funds from the moment where the payer commits the funds in a programmable payment until the moment where the conditions are satisfied to release those funds to the payee. Programmable payments will be desirable, and may in fact be the only viable payment arrangement, in situations where economic relationships are of a short duration. Our results identify a limit to the growth in the demand for payments as their cost decreases: While the number of feasible trading relationships will increase, existing trading relationships will optimally rely on fewer payments.

Stablecoins - a category of crypto-assets designed to keep their value stable - have grown rapidly since 2020. The largest stablecoins hold short-term dollar-denominated assets to manage their peg against the dollar. This paper documents one implication of this pegging mechanism for the short-term funding markets. To this aim, we identify changes in the stablecoin demand for commercial papers (CP) by tracking the stablecoin tokens in circulation and by exploiting cross-sectional and time-varying heterogeneity in reserve assets policy of the main stablecoin issuers. We show that CP issuers catered to the additional demand from stablecoins by issuing more, highlighting a new connection between crypto-assets and conventional markets.

We show that by lending to merchants and monitoring them, an e-commerce platform can price-discriminate between merchants with high and low financial constraints: the platform offers credit priced below market rates and designed to select merchants with lower capital or collateral while simultaneously increasing the platform's access fees. The credit market then becomes endogenously segmented with banks focusing on less financially constrained borrowers. Lending by the platform expands with its monitoring efficiency but can arise even when the platform is less efficient than banks at monitoring. Platform credit benefits more financially constrained merchants as well as buyers, but can hurt less financially constrained merchants if cross-side network effects with buyers are too small. The platform's propensity to offer credit and the financial inclusion of more constrained merchants depends on the platform's market power in its core business.

Open banking is a policy initiative that enables borrowers to share data with any financial institutions. This paper explores impact of open banking on lending market competition and its resulting consequences. In our model, banks compete for underbanked borrowers in common-value auctions and engage in maturity transformation. Under closed banking, the bank with borrower data is an informational monopolist. Under open banking, banks with good signals may refrain from lending. Open banking reduces resource allocation efficiency, narrows bank spread, and enhances financial inclusion. Maturity transformation affects the impact of open banking by preventing banks from transferring risks to their creditors.

We study the transmission channels of quantitative tightening (QT). We develop a structural model where reducing the size of the Federal Reserve’s balance sheet affects the demand for reserves by banks and demand for liquidity by non-banks, and calibrate our model to the data of the current monetary tightening cycle. Rather than the demand for reserves by banks which is typically considered in the existing academic literature, we find that the demand for liquidity by non-banks is the binding constraint for the size of the Federal Reserve’s balance sheet. We show that the Federal Reserve can reduce the size of its balance sheet by more if it sets interest rates higher, documenting a novel complimentarity between both monetary policy tools.

We develop a two-layer asset pricing framework to analyze fragility in the corporate bond market. Households allocate wealth to institutions, which allocate funds to specific assets. The framework generates tractable joint dynamics of flows and asset values, featuring amplification and contagion, by combining a flow-performance relationship for fund flows with a logit model of institutional asset demand. The framework can be estimated using micro-data on bond prices, investors holdings, and fund flows, allowing for rich parameter heterogeneity across assets and institutions. We use the estimated model to quantify the equilibrium effects of unconventional monetary and liquidity policies on bond prices.

We compare the market quality of centralized crypto exchanges (CEXs) such as Binance and Kraken to decentralized blockchain-based venues (DEXs) such as Uniswap v2 and v3.

After discussing the microstructure of such exchanges, we analyze two key aspects of market quality: transaction costs and deviations from the no-arbitrage condition.

We find that CEXs and DEXs operate on roughly equal footing in terms of transaction costs, particularly in light of recent innovations in DEX protocols.

Moreover, while CEXs provide superior price efficiency, DEXs eliminate custodian risk.

These complementary advantages may explain why both market structures coexist.

In contrast to centralized exchanges (CEXs) which match orders continuously following a price-time priority rule, decentralized exchanges (DEXs) process orders in discrete time and require traders to bid a blockchain priority fee to determine the execution priority of their orders. We employ a structural vector-autoregressive (structural VAR) model to provide evidence that blockchain fees attached to DEX trades reveal their private information, contributing to price discovery. We show that informed traders bid higher fees not only to avoid execution risk resulting from blockchain congestion but also to compete with each other. Using a unique dataset of Ethereum mempool orders, we further demonstrate that informed traders mostly compete on DEXs through a jump bidding strategy.

How do cryptocurrency exchanges compete with each other? We show that small and large crypto exchanges appear to be complements, rather than substitutes, as traditional oligopoly theory would predict. When large exchanges list new tokens, trade volumes on small exchanges increase, and small exchanges become more likely to list. We rationalize these facts in a model where small exchanges have captive customer bases, and rely on arbitrage trade with large exchanges for liquidity provision. Our results imply that large exchanges’ listing decisions play a systemically important “leader” role in determining trade volumes and listings on other exchanges.

Does household leverage matter for workers' job search, matching in the labor market, and wages? Theoretically, household leverage has been shown to have opposing effects on the labor market through, among others, a debt-overhang and a liquidity constraint channel. To test which channels dominate empirically, we exploit the introduction of a macroprudential borrowing restriction that exogenously reduces household leverage in Norway. We study home-owners who lose their job and find that a reduction in leverage raises wages by 3.3 percentage points after unemployment. The mandated restriction of leverage enables workers to search longer for jobs, and thereby find positions in firms that pay higher wage premia and switch to new occupations and industries. We observe no evidence that greater use of credit during unemployment drives the extended job search. The positive effect on wages is persistent and more pronounced for workers who are more likely to benefit from improved job search, such as young people. Our findings contribute to the debate on the costs and benefits of policies that constrain household leverage and show that such policies, while primarily aiming at enhancing financial stability, have other positive effects such as improved labor market outcomes.

I study asset-side runs on credit lines in an infinite-horizon banking model. Strategic complementarity between bankers and credit line borrowers arises: borrowers' panic drawdowns and bankers' liquidity rationing reinforce each other, leading to a vicious cycle. Using data from U.S. banks, I estimate the model and quantify the amplification effect due to the strategic complementarity. This amplification effect accounts for two-thirds of the overall credit shortfalls during the 2008-09 crisis. My estimation also suggests policies targeting banks and borrowers simultaneously to support bank credit.

This paper studies the link between government guarantees for banks and bank asset concentration. We show theoretically that these guarantees, when combined with high leverage, incentivize banks to further invest in asset classes they are already heavily exposed to. We confirm these predictions using U.S. panel data, exploiting exogenous changes in banks' political connections for variation in bailout expectations. At the bank level, we find that higher bailout probabilities are associated with higher portfolio concentration. At the bank-loan class level, we find that banks respond to an increase in their bailout expectations by further loading up on loan classes that already have a high weight in their portfolio.

In extant work on information acquisition and governance, the value of information is related only to the impact of a shareholder’s vote on corporate policies. We consider a setting where shareholders can vote and trade. The incentives to acquire information are higher, but the opportunity to generate trading rents distorts voting incentives and reduces the quality of governance for any fixed level of information acquisition. These negative incentives are stronger when more voters are informed and eventually dominate the gains from more information acquisition by shareholders. As a result, the quality of firm governance is eventually decreasing in the fraction of shareholders that become informed. One takeaway is that concerns that proxy advisors may crowd out information acquisition and reduce governance quality seem overstated. Turning to the role of transparency, we show that if the market can only learn whether a motion passed as opposed to the exact voting tally, then opportunities to trade do not cause these distortions, and governance is dramatically improved. Accordingly, the analysis provides a rationale for reducing transparency in governance.

The equity lending and option markets 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 showing that vote prices in the equity lending market are endogenously lower than those implied by the option market. Nonetheless, we show that even though votes are cheaper in the equity lending market, activists endogenously choose to decouple using both markets.

This paper develops a unified theory of blockholder governance and the voting premium. It explains how a voting premium emerges when a minority blockholder tries to influence the composition of the shareholder base, in a setting without takeovers and controlling shareholders. The model shows that empirical measures of the voting premium generally do not reflect the value of voting rights, and that the voting premium can be negligible even when the allocation of voting rights is important. Moreover, the model can explain a negative voting premium, which has been documented in several studies. It arises because of free-riding by dispersed shareholders on the blockholder's trades, which increases the price impact of trading voting shares and makes them less liquid than non-voting shares. The model also has novel implications for the relationship between the voting premium and the severity of conflicts of interest between shareholders, the price of a separately traded vote, and competition for control among blockholders.

This paper investigates how consumers and investors react to the standardized disclosure of data privacy practices. Since December 2020, Apple has required all apps to disclose their data collection practices by filling out privacy “nutrition” labels that are standardized and easy to read. We web-scrape these privacy labels and first document several stylized facts regarding the supply of privacy. Second, augmenting privacy labels with weekly app downloads and revenues, we examine how this disclosure affects consumer behavior. Exploiting the staggered release of privacy labels and using the nonexposed Android version of each app to construct the control group, we find that after privacy label release, an average iOS app experiences a 14% (15%) drop in weekly downloads (revenue) when compared to its Android counterpart. The effect is stronger for more privacy-invasive and substitutable apps. Moreover, we observe negative stock market reactions, especially among firms that harvest more data, corroborating the adverse impact on product markets. Our findings highlight data as a key asset for firms in the digital era.

We investigate how excess commitment to R&D activities impacts innovation outcomes and consumer welfare. Using project-level data on clinical trials by pharmaceutical firms, we document that trials which faced unanticipated delays in the previous trial stage, relative to initial firm expectations or induced by plausibly-exogenous trial site congestion, are significantly less likely to be subsequently suspended. These effects are amplified when the firm’s CEO has a greater wealth exposure to stock price changes and is personally responsible for the project initiation. Consumers may, in some ways, benefit from firms’ excess commitment in new drug development. Marginally-launched drugs because of commitment distortions are not significantly associated with more adverse events, but are significantly more likely to target diseases with no or only few existing medications in the marketplace (orphan drugs).

Capital ages and must eventually be replaced. We propose a theory of financing in which firms finance new capital with debt and optimally deleverage to free up debt capacity as their capital ages, thereby generating debt cycles. Concurrently, firms shorten the maturity of their debt to match the remaining life of their capital, generating maturity cycles. We provide time series and cross-sectional evidence that strongly supports these predictions and highlights the key roles of capital age and asset life for both debt dynamics and debt maturity choices.

We study the conditions under which debt securities that make the cost of debt contingent on the issuer's carbon emissions, similar to sustainability-linked loans and bonds, can be equivalent to a carbon tax. We propose a model in which standard and environmentally-oriented agents can adopt polluting and non-polluting technologies, with the latter being less profitable than the former. A carbon tax can correct the laissez-faire economy in which the polluting technology is adopted by standard agents, but requires sufficient political support. Carbon-contingent securities provide an alternative price incentive for standard agents to adopt the non-polluting technology, but require sufficient funds to fully substitute the regulatory tool. Absent political support for the tax, carbon-contingent securities can only improve welfare, but the same is not true when some support for a carbon tax exists. Understanding the conditions under which the regulatory and capital market tool are substitutes or complements within one economy is an important stepping stone in thinking about carbon pricing globally. It sheds light, for instance, on how developed economies can deploy finance to curb carbon emissions in developing economies where support for a carbon tax does not exist.

This paper investigates how the environmental liability of lenders affects debtors’ behavior. I use U.S. Census Bureau micro-data and the passage of the Lender Liability Act as a novel identification strategy to answer this question. Firms increase on-site pollution, cut investment in abatement technology, and incur 17.54% more environmental regulatory violations when secured lenders become less responsible for the cleanup cost of their collateral. This lower environmental compliance slightly benefits employment, but does not change wages or production. Overall, reduced lender liability lessens banks’ incentives to influence the environmental practices of their debtors with limited benefit on economic growth.

We investigate the impact of three non-climate environmental criteria: biodiversity, water, and pollution prevention, on infrastructure firms' credit risk term structure from the perspective of double materiality. Our findings show that firms that effectively manage these three environmental risks to which they are materially exposed have up to 93bps better long-term refinancing conditions compared to the worst-performing firms. While the results are less significant for the firm's material impact on the environment, investors still reward the management of these criteria beyond climate with improved long-term financing conditions for infrastructure investments. Overall, we find that financial markets respond positively to the prospect of more stringent regulations related to these criteria, which are currently used by the EU Taxonomy to assess the sustainability of investments.

Using a comprehensive database of corporate news, we examine how bond mutual funds trade on the sentiment of news releases. We find that bond funds trade against the direction of news sentiment (e.g., selling after good news about a firm). The results are more pronounced in bonds that lie within a fund’s investment objective sector, and in bonds with high turnover and low information asymmetry, and in credit-rating news and news with positive sentiment. Funds that most frequently trade against news sentiment produce a higher alpha, and a source of such alpha is bond price reversals post news events. Fixed income mutual funds, dealers, and insurance companies complement each other in news trading, with mutual funds trading against news largely in the absence of dealers. Our study indicates that bond mutual funds represent a significant liquidity provider, upon corporate news events, in the market for corporate bonds.

Using a dataset of over a million local government procurement contracts in China, we study the social and economic costs of local government debt. Firms contracting with local governments with high maturing debt-to-fiscal income see an increase in accounts receivables that do not reverse, a decrease in cash balances, and an increase in the probability of litigation by creditors. These effects (1) are not driven by local economic conditions, endogenous government indebtedness, or self-selection into becoming government suppliers, (2) do not apply for government-linked firms, and (3) are larger for firms in areas with weaker labor and property rights. Affected firms are less likely to be repeat government contractors and more likely to see protests relating to non-payment of wages or pension contributions, suggesting that suppliers bear the costs involuntarily.

We use a model of a limit order book to determine the optimal tick size set by a social planner who maximizes the welfare of market participants. Our results show that when investors arrive sequentially and supply liquidity by undercutting or queuing behind existing orders, the optimal tick size is a positive function of the asset value and a negative function of stock liquidity. Intuitively, the tick size is a strategic tool a social planner uses to optimally affect investors' choice between liquidity demand and supply, thus mitigating the inefficiencies created by excessive undercutting and queuing. The policy implication of such findings is that both the European tick size regime and the 2022 SEC proposal dominate Reg. NMS Rule 612 that formalizes the tick size regime for the U.S. markets. Using data from the U.S. and the European markets we test our model's predictions.

We test for skewness preferences in a large set of observational panel data on online

poker games (n=4,450,585). Each observation refers to a choice between a safe option and

a binary risk of winning or losing the game. Our setting offers a real-world choice situation

with substantial incentives where probability distributions are simple, transparent, and

known to the individuals. Individuals reveal a strong and robust preference for idiosyncratic

skewness, which has important implications for asset pricing. The effect of skewness

is most pronounced among experienced and losing players but remains highly significant

for winning players, in contrast to the variance effect.

We find that active mutual funds owning product market competitors have superior risk-adjusted returns that are not driven by industry concentration, common selection, or stock-picking ability. These funds charge higher fees but also generate persistent net-of-fee returns for investors. Funds with higher common ownership are more active voters who are more likely to vote against executive incentives compensation and for directors with existing directorships in competitors. Our findings suggest some actively- managed mutual funds have an incentive to soften product market competition and that proxy voting could serve as one mechanism for influencing corporate policy.

Using a novel representative sample of digital payment data, we observe a robust U-shaped relationship between individual investors’ monthly entertainment-related consumption and stock market returns in the previous month. Contrary to the prediction of the wealth effect, individuals increase their entertainment-related consumption after experiencing large positive and negative stock market shocks. We show that the latter effect, termed “financial retail therapy,” is consistent with a dynamic model of Prospect Theory, and provide further evidence for it in a controlled laboratory experiment. Finally, we show that our results are not driven by income effects or wealth shock measurement errors.

Households exhibit "return chasing" behavior, so through asset reallocation channel, good stock market performance induces contractions in deposit supply. Using stock market performance as a shock to deposit supply, we trace out banks' deposit demand and identify the relationship between bank market power and the slope of deposit demand. Exploiting a fixed effects identification strategy by comparing branches with the same parent bank located in different cities within the same county, we find that bank market power makes deposit demand curve steeper. Steeper deposit demand curve attenuates the spillover effects on the local deposit market of stock market fluctuations. Counties with more bank market power also experience less contractions in small business lending when stock market performance is good. Overall, our results suggest that bank market power is important in insulating and stabilizing local deposit and lending market from the spillover effects of the stock market.

A beneficial effect of bank regulation may play out through the asset management sector. When asset managers count on a central bank to support market liquidity in a systemic event, they take on fire-sale risk that is excessive from a social perspective. However, the extent of risk-taking today also incorporates the spread that bank dealers would charge for absorbing fire sales tomorrow. If regulation constrains banks' balance-sheet space, the expected spread would be higher, reining in excesses in asset managers' risk-taking and ultimately raising welfare.

This paper investigates the impact of trading on the commodity futures risk premium. We focus on intra-commodity spreading positions and study the asset pricing implications of spreading pressure (SP), that is, spreading positions scaled by open interest, on the cross-section of commodity futures returns. We document that SP negatively predicts futures excess returns. A battery of empirical tests shows that SP helps separate commodities that trade based on economic fundamentals from commodities that are subject to market frictions introduced via commodity index investments. We propose an SP factor, a long-short portfolio based on SP that is priced in the commodity futures market, even after controlling for well-known factors, and is robust to accounting for omitted variable biases and measurement errors.

We uncover a significant relationship between the persistence of marketing employment strategy and fund performance in the U.S. mutual fund industry. Using regulatory filings, we show a large heterogeneity in fund companies' marketing employment share, which refers to the fraction of employees devoted to marketing and sales. Not only does the marketing employment share increase in family size and predict subsequent fund flows, but it is also persistent across fund families. A framework based on Bayesian persuasion and costly learning helps explain the observed strategic marketing decision. Regarding an optimal marketing plan, fund companies with different skill types commit to heterogeneous marketing employment strategies. Conditional on the skill level, fund companies' optimal marketing employment share responds to their past performance differently. Low-skill funds only conduct marketing following good-enough past performance, whereas high-skill funds maintain a high marketing employment share even with very poor past performance. Consistent with the model prediction, we show that the volatility of the marketing ratio is negatively correlated with the long-term performance of fund companies.

After a new direct board connection is formed to a product market peer, a firm's gross margin increases by 0.8 p.p. Gross margin also rises by 0.4 p.p. after a new connection is formed to a peer indirectly through a third intermediate firm. Board connections have positive profitability spillovers on the closest rivals, and the effects are stronger when the newly connected peers share major corporate customers, have more similar business descriptions, or are located closer to each other. Using retail scanner data, we further provide direct evidence that new board connections are related to higher product prices of consumer goods.

Using a sample of 101,834 cross-border deals announced between 1985 and 2018, we show that merger flows involve acquirers from more democratic countries than their targets. This result is primarily driven by a “pull” factor, that is, firms in countries with weaker democratic institutions attract cross-border deals. Democratic institutions have a fundamental influence on international merger activity because they are conducive to better corporate governance standards. Further decomposing the effect reveals that democratic institutions also play a direct role that is not due to investor protection or economic development. Our findings imply that democracy is an important, omitted determinant of cross-border mergers.

Private Equity (PE) has grown into a substantial asset class, but there remain major problems with measuring PE fund returns. Investors continue to use the internal rate of return (IRR) as a key measure of fund performance. It is well known that early returns of cash can have a substantial impact on fund IRRs, but the magnitude and causes of this effect have not previously been systematically analysed. We demonstrate that the IRR is affected by two biases: a convexity bias, and a “quit-whilst-ahead” bias arising because the returns on PE projects tend to covary with their durations. Both bias the IRRs of PE funds upwards.

Using parametric and non-parametric estimation techniques, we show that these biases boost fund IRRs by an average of around 3% per annum. This represents a substantial proportion of the amount by which the average net PE fund IRR (around 12% per annum) appears to outperform returns on listed equity indices.

Fund cash multiples and PMEs become similarly biased if they are annualized to try to make them comparable with other assets. We further demonstrate that alternative performance measures which have been suggested by practitioners are also biased, which confirms how poorly understood these effects are. Failure to take proper account of these biases is likely to lead investors into badly misinformed investment decisions.

I present a theory of investor selling behavior in which the disposition effect arises because investors are slow to update their beliefs about the values of the assets they hold. I show numerically that the theory generates a disposition effect, propensity to sell functions, and other trading statistics that are in line with empirical estimates. I also show that the theory generates a reversed disposition effect at the end of the tax year, a weaker effect for more sophisticated investors, a stronger effect in more volatile stocks, and a disposition effect in short sales.

We study the impact of investors’ benchmarking concerns on market efficiency and asset pricing. Both separative and integrative learning technologies are examined as investors allocate limited attention across assets. We show that benchmarking can increase the price informativeness of benchmarked asset as investors optimally adopt integrative learning to observe a combined signal about asset payoffs. This is contrary to the result from the existing literature that assumes separative learning. Benchmarking can also increase the overall market efficiency with either type of learning. Yet, the implications for asset prices and comovements can be qualitatively different under different learning technology.

The value factor has no incremental pricing power in the Fama-French (2015) five-factor model. Thereby, its pricing power is primarily subsumed by the investment factor. We show that the strong relationship between the two factors arises because their sorting variables—book-to-market and investment—are both driven by shocks to expected cash flows and discount rates. We document that only stocks driven by shocks to discount rates contain the factors’ cross-sectional pricing information. The value and investment premia based on these stocks are more than 50% higher than the usual value and investment premia. Importantly, adjusted versions of the value and investment factors that use only such discount rate shock-driven stocks cannot subsume each other and improve the five-factor model’s pricing power. Thus, a value factor built from stocks for which book-to-market is actually a good indicator of expected returns captures incremental pricing information and is no longer redundant. Consequently, multifactor models should include such a value factor.

We find that loans to a borrower sharing similar technologies with the bank’s prior borrowers have lower loan spreads, likely due to reduced costs in loan screening and monitoring from bank’s accumulated knowledge. Such effect cannot be explained by product market competition, technology value and innovation ability or other firm characteristics. We show that borrower technology similarity is informative about firm creditworthiness. Despite identification challenges, we use a structural bank-borrower matching model to show that the total economic surplus for banks and borrowers can be enhanced by matching banks to borrowers with a high technology similarity to the bank’s prior borrowers. This technology similarity also plays an important role in the bank’s learning-by-lending process.

Unlike labor income, human capital is inseparable from individuals and does not accrue to creditors at default. As a consequence, human capital investment should be more resilient to “debt overhang” than labor supply. We develop a dynamic model displaying this important difference. We find that while both labor supply and human capital investment are hump-shaped in leverage, human capital investment tails off less aggressively as leverage builds up. This is especially the case when human capital depreciation rates are lower. Importantly, because skills acquisition is only valuable when households expect to supply labor in the future, the anticipated greater reduction in labor supply due to debt overhang back-propagates into a reduction in skills acquisition ex ante. Using individually identifiable data, we provide empirical support for the model.

This paper compares various machine learning models to predict the cross-section of emerging market stock returns. We document that allowing for non-linearities and interactions leads to economically and statistically superior out-of-sample returns compared to traditional linear models. Although we find that both linear and machine learning models show higher predictability for stocks associated with higher limits to arbitrage, we also show that this effect is less pronounced for non-linear models. Furthermore, significant net returns can be achieved when accounting for transaction costs, short-selling constraints, and limiting our investment universe to big stocks only.

We study the extent to which actively managed mutual funds bet on the CEO. Focusing on firms with CEO turnovers in a particular month, we find significantly higher trading activity and exit rates for funds holding this stock in that month compared to all other months and compared to all other firms. The trading activity and exit rates are higher for raided CEOs and serial CEOs, consistent with some funds placing larger bets on CEOs with higher perceived managerial ability. In further tests, we find strong persistency in the tendency for some funds to bet on the CEO, and show that such funds are less likely to be team managed, and have larger portfolio weights on firms in industries where managerial skills are more valuable. They charge higher fees, but despite that, their net returns are similar. Overall, our results uncover that betting on the CEO is an investment strategy of some actively managed mutual funds. We finally show that this strategy is upheld in equilibrium in a model where the motive for trade is differences in opinion about the importance of the CEO.

We develop a machine learning (ML) approach to establish new insights into how memory affects financial market participants’ belief formation processes in the field. Using analyst forecasts as proxies for market beliefs, we extract analysts' mental contexts and recalls that shape forecasts by training an ML memory model. First, we find that long-term memories are salient in analysts’ recalls. However, compared to an ML benchmark trained to fit realized earnings, analysts pay more attention to distant episodes in regular times but less during crisis times, leading to recall distortions and therefore forecast errors. Second, we decompose analysts' mental contexts and show that they are mainly shaped by past earnings and forecasting decisions instead of current firm fundamentals as indicated by the ML benchmark. This difference in contexts further explains the recall distortion. Third, our comprehensive memory model reveals the significance of specific memory features and channels in analysts' belief formation, including the temporal contiguity effect and selective forgetting.

I show that, in a DSGE setting with heterogeneous shareholders, the stock market risk premium and volatility decrease as monitoring increases. Monitoring arises because inside shareholders have an incentive to extract private benefits from firm output, whereas outside shareholders have the incentive to limit this extraction. Monitoring varies positively with the share of outside shareholder ownership, such that monitoring represents a source of cross-sectional and time-series variation in equilibrium asset pricing moments. I present empirical evidence supporting these theoretical results.

The microfinance industry serves over 140 million borrowers worldwide, and has been hailed as a means of fighting poverty and stimulating growth of small businesses in low- and middle-income countries. Yet evidence from several countries suggests a negligible average impact of microcredit on the performance of small businesses. In this paper, I explore the impact of equity-like microfinance contracts with performance-contingent payments, which provide a greater amount of risk-sharing than the standard rigid microcredit contract. I conduct artefactual field experiments with a sample of business owners who were participating in two broader field experiments in Kenya and Pakistan that had provided their businesses with large capital injections. I find that contracts with performance-contingent repayments outperform standard microcredit contracts, by stimulating more profitable investment choices, especially for the most risk-averse individuals. Loss-averse individuals also particularly value equity-like contracts, which provide downside protection in return for upside profit sharing. However, individuals who exhibit non-linear probability weighting prefer debt contracts, especially in the presence of a skewed profits distribution (where there is a low probability of very high outcomes, which such individuals overweight). I structurally estimate these three distinct dimensions of risk preferences using a prospect-theoretic model and show that relatively simple tweaks to contract design (specifically, capping upside profit sharing) can improve the feasibility of equity-like contracts. By utilising financial technology developments that improve screening and monitoring, and by taking into consideration three distinct elements of risk preferences, financial institutions that cater to small firms can unlock new forms of performance-contingent capital to provide better risk-sharing and improve client welfare.

I quantify the costs of realized flood disasters for banks and create a novel measure of bank-level flood risk exposure using expected flood risk estimates and mortgage lending data. I document that banks with large shares of mortgages in affected areas experience lower profits and capital ratios following flood disasters. In the cross-section of stock returns, small banks with high exposure to flood risk underperform other banks, on average, by up to 9.6% per year; this implies that exposure to flood is not fully priced. Underperformance persists when controlling for the negative effects of disasters on realized returns and adjusting for investors’ climate change concerns. The findings support regulatory concerns that bank equity is exposed to physical risk from climate change.

This paper uses proprietary data on self-reported employee reviews from Glassdoor.com to study the relationship between employee satisfaction and mutual funds’ performance. Using the staggered adoption of Anti-SLAPP (Strategic Lawsuits Against Public Participation) laws in the U.S. and variation from mergers between asset management companies to identify exogenous variation in job satisfaction, we find that employee satisfaction is positively linked to fund performance and size but that only performance-critical employees' satisfaction matters. A one-point increase on the 5-point scale of employee satisfaction leads to a 36bps increase in annual abnormal fund performance. Finally, while there is a positive effect of employee satisfaction on risk-taking, we cannot establish a causal relationship.

We evaluate the performance of common stock trade classification algorithms to infer the trade direction of option trades. Using a large sample of matched intraday transactions and Open/Close data, we show that the algorithms’ success to classify option trades is considerably lower than for stocks. The weak performance is due to sophisticated customers who often use limit orders instead of market orders to implement their trading strategies. These traders’ behavior varies over time and across exchanges with different pricing models. We introduce new rules that enhance existing algorithms and improve classification accuracy by 9% to 47%. Applying our new rules to construct a long-short trading strategy for stocks based on option order imbalance increases Sharpe ratios from 2.65 to 4.07.

In this paper, we provide an estimate of the ex-ante risk premia on earnings announcements based on the option market. We find that the risk premia are time-varying and have predictive power on future stock returns. The well-documented positive post-earnings-announcement drift (PEAD) is present only when the risk premia are high. After controlling for the announcement risk premia, the PEAD factor of the literature no longer has any abnormal returns. Moreover, while trading option straddles is not profitable unconditionally, conditional on high ex-ante risk premia, it becomes profitable even net of transaction costs.

ETFs add a new layer of market-making to the corporate bond market that improves the market quality of the underlying bonds. Dealers use the flexibility of the primary corporate bond ETF market as a warehouse to manage inventory. The face value of ETF holdings in investment grade (high yield) bonds is 9.1% (25.9%) greater on the downgrade date than thirty days prior. Bonds eligible for inclusion in ETFs with the most active primary markets overreact less than other downgraded bonds from the same issuer. This new layer of market-making leads to a negative relation between ETF ownership and idiosyncratic volatility.

We examine the causal effect of politicians’ partisan ideologies on firms’ industrial pollution

decisions. Using a regression discontinuity design involving close U.S. congressional elections, we show that plants increase pollution and invest less in abatement following close Republican wins. We also find evidence of reallocation: firms shift emissions away from areas represented by Democrats. However, costs rise and M/B ratios decline for firms whose representation becomes more Democratic, suggesting that politicians’ ideological demands can be privately costly. Pollution-related illnesses spike around plants in Republican districts, suggesting that firms’ passthrough of politicians’ ideologies can have real consequences for local communities.

Using granular auction data on 15 million item purchases in Brazilian public procurements between 2005-2021, we document a widespread pattern that the lowest bidder (``kamikaze'') does not satisfy required formalities after the auction is concluded, which allows the second-lowest bid to win the auction. Such a pattern can be observed in up to 15-20% of procurement auctions and results in 15-17% higher procurement prices as compared to similar auctions procuring the same product or service items, organized by the same government institutions, and even having the same winning firm. Kamikaze firms are smaller, younger, and tend to be co-owned by the same ultimate owner as the winning firm. Using observed kamikaze behaviour as a marker, we aim to measure how higher procured prices contribute to real outcomes by public service providers by reducing the budget available for sourcing other items. Taking the case of hospital mortality data, we see an increased number of deaths in the four quarters after an increased fraction of procurement auctions involving kamikazes.

Our study introduces an experimental framework to examine the role of attention in the decision-making process, with a particular focus on its impact on learning and belief formation. In order to identify attention, we draw upon the Salient Theory developed by Bordalo, Gennaioli, and Shleifer (2012). We conduct a two-stage online experiment and uncover several noteworthy findings. Firstly, slightly more than half of the participants show salient thinking characteristics, indicating a proclivity to overweight the standout option among a set of alternatives. Secondly, our results reveal that participants tend to overreact to salient signals and, more importantly, that overreaction is mainly driven by salient thinkers. Additionally, salient thinkers exhibit a greater degree of optimism than others when they receive positive signals, which is further amplified when these signals are infrequent. Lastly, while the salience anomaly is more pronounced in the short term, it disappears over an extended estimation period.

Using a unique experiment in the credit market, we find that anonymous loan applications mitigate racial disparities. When names are on applications, ethnic minorities are 10.7% less likely to receive online loan offers than otherwise identical majority applicants; anonymizing applications eliminates such disparities. After receiving online loan offers, applicants need to visit lenders in person for identity verification before loan origination. Despite that race is revealed to lenders, racial disparities in loan origination also decrease. We do not find significant racial gaps in loan performance either before or after anonymization. Further tests show that accurate statistical discrimination is unlikely to explain our results.

Using comprehensive issuance-level information for dollar-denominated short-term debt, we show that investments by money market mutual funds (MMFs) significantly reduce issuers’ funding frictions. Issuers without MMF funding pay approximately 10 basis points more for placing their short-term debt, even when comparing issuers with small MMF investments to issuers without MMF investments. Funding costs increased for issuers who lost their MMF investors because of an exogenous regulatory shock to the MMF industry in 2016. Issuers who lose their MMF investors reduce their outstanding short-term debt by more than 50% and issue debt with shorter durations, suggesting that MMF investments reduce an issuers’ funding fragility.

We study the effect of a central bank digital currency (CBDC) on the money market. A CBDC is equivalent to a 100% reserve requirement to fund those transactions that require CBDC, contrary to transactions that require bank deposits that only need partial reserve backing. We find that a higher fraction of transactions conducted with CBDC will drain reserves and tend to increase the interbank rate. The effect of CBDC remuneration is however ambiguous. A higher CBDC rate increases its value as a payment instrument. This leads to lower funding costs and larger investment, decreasing or increasing the demand for reserves and the interbank market rate, depending on which effect dominates. We show that a cap on CBDC will reduce the interbank rate and the deposit rate, as banks need less deposits to buy reserves. A CBDC design with tiered remuneration does not bring additional benefits relative to a single (lower) remuneration rate.

We propose a novel method to impute daily mutual fund trades in individual stocks from data on quarterly fund holdings, monthly total net assets, and daily fund returns – so that the method can be applied to standard CRSP mutual fund data. We set up an (underidentified) system of linear equations and solve the underidentification issue with an iterative method that applies random and adaptive constraints on trade incidence. The method produces daily, stock-level trade estimates with associated confidence levels. Validation and simulation analyses using proprietary daily fund trading data show good accuracy, especially for larger trades.

This paper studies the link between government guarantees for banks and bank asset concentration. We show theoretically that these guarantees, when combined with high leverage, incentivize banks to further invest in asset classes they are already heavily exposed to. We confirm these predictions using U.S. panel data, exploiting exogenous changes in banks' political connections for variation in bailout expectations. At the bank level, we find that higher bailout probabilities are associated with higher portfolio concentration. At the bank-loan class level, we find that banks respond to an increase in their bailout expectations by further loading up on loan classes that already have a high weight in their portfolio.

I study the spillover effects of removing barriers to growth in one product market on entry and growth of firms in the downstream/customer market. Constrained firms produce low quality goods and, in turn, hamper access to high quality inputs for downstream firms. I exploit the repeal of product reservation in India, whereby hundreds of products stop being reserved for exclusive production by small firms. With an increase in production of high quality goods in the input market, entry in the downstream product market increases. Entrants are not worse on observable characteristics. Productive downstream incumbents grow and less productive ones shrink. My results imply that business dynamism has positive spill-over effects along the supply chain.

While various theories stress the importance of technology for cryptocurrency valuation, empirical evidence is limited. In this paper, we study whether technology aspects of cryptocurrencies matter for their valuations, using machine learning methods to construct a technology index from initial coin offering whitepapers. We then track down the performance of cryptocurrencies from their initial offering to long-term valuations. We find that the cryptocurrencies with high technology indexes are more likely to succeed and less likely to be delisted subsequently. Moreover, the technology index strongly and positively predicts the long-run performances of cryptocurrencies. Overall, the results suggest that technological sophistication is an important determinant of cryptocurrency valuations.

In this paper, I define employee morale as employees’ attitudes toward and perceptions of the tasks the employees have in the companies they work for, toward companies’ dynamics and working conditions, and toward their interactions with fellow colleagues. I explore how employee morale affects post-merger integration and performance and post-merger merged firm employee morale using various proxies. The paper makes several novel findings. First, mergers between firms with similar employee morale are more likely to merge and achieve greater short-run and long-run post-merger synergies. Second, firms with high and similar morale achieve better post-merger integration than firms with low and similar morale and complementary morale. Third, companies with similar morale experience a more rapid rate of completion and exhibit a higher likelihood of completion. Fourth, target companies with high employee morale take less time to be integrated into acquiring companies, regardless of the acquiring companies’ employee morale. Fifth, acquiring companies value the employee morale profile of target companies and they tend to go after target companies with high level and low dispersion in dimensions of employee morale. Sixth, distance between acquirer and target employee morale is negatively associated with post-merger employee morale level and positively associated with post-merger employee morale changes. Finally, the observed acquirer price runup reflects takeover rumors generated from acquirer employees.

We study how prepayment penalties influence credit availability and borrowers' post-origination loan performance using a fuzzy regression discontinuity design that exploits exogenous variation due to idiosyncrasies of US state law. Estimates show a prepayment penalty 1) reduces the probability of delinquency by 5.1%, 2) the odds of foreclosure by 4.9%, 3) increases the likelihood of loan origination by 2.5%, and 4) lowers interest rates by 6.4%. The effect sizes are larger among riskier borrowers. The findings are consistent with theoretical models' predictions that prepayment penalties act as a borrower commitment device which acts against adverse selection as refinancing leads to a deterioration of the quality of lenders' mortgage pools.

This paper studies how banking regulators should disclose the models they use to assess banks that have reporting discretion. In my setting, assessments depend on both economic conditions and the fundamental of banks' asset. The regulatory models provide signals about economic conditions and banks report the fundamental of their asset. On the one hand, disclosing the models helps banks to understand how their assets perform under different economic environments. On the other hand, it induces banks with assets that are socially undesirable to manipulate the report and obtain favorable assessments. While the regulator can partially deter manipulation by designing the assessment rule optimally, the disclosure of regulatory models is necessary. The optimal disclosure policy is to disclose the regulatory models when the assessment rule is more likely to induce manipulation and keep them secret otherwise. In this way, disclosure complements the assessment rule by reducing manipulation in cases that harm the regulator more. The analyses speak directly to the supervisory stress test and climate risk stress test.

Why are over-the-counter markets the dominant market structure for many asset classes? We argue that non-anonymous trading and repeated interactions facilitate trading relationships that solve a moral hazard problem. Using data from a large European OTC dealer, we provide show that customers with a strong relationship obtain much more favorable bid-ask spreads, and that this effect is even stronger during market stress. We present evidence that moral hazard, not adverse selection, drive relationships. Customers with strong relationships can credibly commit to a fixed trading size, to not trade with other dealers and to not exploit the dealer when quotes are stale. We also show that the scope of OTC relationships extends across asset classes and that investment banks' organisational structure is designed to ensure that traders offer discounts to highly valued customers.

I develop a real options model to explain average returns and return volatilities of stock portfolios sorted on the book-to-market ratio. While average returns increase monotonically across portfolios, return volatilities are U-shaped. My model combines business cycle variations with countercyclical economic uncertainty. Operating leverage and procyclical growth options make both value stocks and growth stocks risky, generating U-shaped return volatilities. Growth stocks additionally load on the negative variance risk premium which reduces their expected return. Using structural estimation, my model jointly fits average returns and return volatilities, thereby solving a long-standing problem in investment-based asset pricing. Further reduced-form evidence supports the model channels.

We combine high-frequency stock returns with risk-neutralization to extract the daily common component of tail risks perceived by investors in the cross-section of firms. We find that our tail risk measure significantly predicts the equity premium, variance risk premium and realized moments of market returns at short-horizons. Furthermore, a long-short portfolio built by sorting stocks on their recent exposure to tail risk generates abnormal returns with respect to standard factor models and helps explain the momentum anomaly. Incorporating investors’ preferences via risk neutralization is fundamental to our findings.

I show that an asset pricing model for the equity claims of a value-maximizing firm can be constructed from its optimal financial contracting behavior. Deals between firms and financiers reveal the importance of contractible states for firm's equity value, namely the stochastic discount factor the firm responds to. I empirically evaluate the model in the cross section of expected equity returns. I find that the financial contracting approach goes a long way in rationalizing observed cross-sectional differences in average returns, also in comparison to mainstream asset pricing models.

I analyze welfare in a model of financial markets where information acquisition is endogenous, information has real effects, and all agents are rational. Agents who derive a private benefit from holding the asset (hedgers) and agents who do not (speculators) have different incentives to acquire information. Multiple equilibria may arise but, in a given equilibrium, information acquisition by one type of agent precludes acquisition by the other. Speculators may produce either too little or too much information. Information acquisition by hedgers entails an additional welfare cost because of foregone gains from trade. I discuss regulatory implications.

This paper analyzes insider-trading regulations, focusing on two recent proposals: advance disclosure and ''cooling-off periods.'' The former requires an insider to disclose his trading plan at adoption, while the latter mandates a delay period before execution. Disclosure increases stock price efficiency but has mixed welfare implications. If the insider has large liquidity needs, in contrast to the conventional wisdom from ''sunshine trading,'' disclosure can even reduce the welfare of all investors. A longer cooling-off period increases outside investors' welfare but decreases stock price efficiency. Its implication on the insider's welfare depends on whether the disclosure policy is already in place.

We document the presence of market power in the equity securities lending market and evaluate its impact on different investor groups and valuations. Our analysis reveals high market concentration, non-competitive fees, and low inventory utilization in the cross-section of stocks. Motivated by this evidence, we develop and estimate a dynamic asymmetric-information model that sheds light on the benefits of this current market structure for both security lenders and short sellers. We find that lending fee income raises shares lenders' equity valuations by 1.5% for large-cap, low-fee stocks, by up to 25% for small-cap stocks, and by even more than 100% for nano-cap stocks. Our model further yields estimates of the distribution of alphas from shorting different segments of the cross-section of stocks, indicating that fees reduce short-sellers' profits by about 60%.

We show that bonds issued by financial firms have higher spreads than bonds issued by industrial firms with the same rating and we denote this difference the financial premium. During the period 1987-2020 the premium was on average 43bps in the U.S. corresponding to a 31% higher spread and the premium is higher for lower ratings and in financial crises. Furthermore, the premium relates to measures of systemic risk and predicts economic activity. We derive a model that explains the empirical results: banks hold a diversified portfolio of corporate bonds (loans) and bank bonds therefore reflect more systematic risk than the individual corporate bonds.

We use new monthly security-level data on portfolio holdings, flows, and returns of U.S. households to understand asset demand across multiple asset classes. Our data cover a wide range of households across the wealth distribution – including ultra-high-net-worth (UHNW) households – and holdings in many asset classes, including public and private assets. We first develop a descriptive model to summarize households’ rebalancing behavior. We find that less wealthy households rebalance from liquid risky assets to cash during market downturns, while UHNW households tend to purchase risky assets during those periods and thus stabilize market fluctuations. This pattern is particularly pronounced for U.S. equities. Across risky asset classes, three factors explain most of the variation in portfolio rebalancing and those factors target the long-term equity premium, the credit premium, and the premium on municipal bonds. Next, we develop a new framework to estimate demand curves across asset classes. While nesting traditional models as a special case, our framework allows for a muted response of asset demand to fluctuations in asset prices and easily extends to account for inertia. Our new estimator of asset demand curves exploits variation in second moments of returns and portfolio rebalancing, and can even be used when only a fraction of all holdings in a market can be observed. Our preliminary results indicate that asset demand elasticities are smaller than those implied by standard theories, vary significantly across the wealth distribution, and are negative for various groups, pointing to positive feedback trading. In sum, we think that our framework and data paint a coherent picture of U.S. households that captures, quite uniquely, their rebalancing behavior across the wealth distribution and across broad asset classes.

How should an investor value financial data? The answer is complicated because it depends on the characteristics of all investors. We develop a sufficient statistics approach that uses equilibrium asset return moments to summarize all relevant information about others' characteristics. It can value data that is public or private, about one or many assets, relevant for dividends or for sentiment. While different data types, of course, have different valuations, heterogeneous investors also value the same data very differently, which suggests a low price elasticity for data demand. Heterogeneous investors' data valuations are also affected very differentially by market illiquidity.

We propose a dynamic theory of financial market concentration in settings where some investors trade strategically because of price impact. The distribution of risk and wealth determines market power, and wealth evolves over time given strategic portfolio choices. In equilibrium, the most well-capitalized investors remain under-diversified to capture rents, generating concentration and volatility in the wealth distribution. Conversely, wealth concentration leads to inflated asset prices, unequal returns to wealth, and poor liquidity that further exacerbates the distortions from market power. We discuss applications of our framework, and derive implications for evaluating welfare using asset pricing data.

We show that an increase in stock return exposure to media attention to narratives, measured with standard methods for extracting topic attention from news text, leads to a lower stock price informativeness about future fundamentals. Empirically, narrative exposure explains over 86% of idiosyncratic variance in the cross-section, and both narrative exposure and non-systematic information channels—idiosyncratic variance and variance related to public information—decrease stock price informativeness. Moreover, stocks with high narrative exposure demonstrate elevated trading volume. To rationalize the empirical results, we suggest a mechanism based on disagreement among investors arising due to the differential processing of information in media narratives.

This study investigates the effects of targeted credit rationing by banks on firms that are likely to generate negative externalities. We use data from Operation Choke Point, a regulatory initiative in the United States that aimed to limit bank relationships with firms in high-risk industries for fraud and money laundering. Our analysis of supervisory loan-level data reveals that targeted banks reduce lending and terminate relationships with affected firms. However, these firms fully substitute credit availability by obtaining loans from non-targeted banks under similar terms, resulting in no changes in total debt, investment, or profitability. Our findings suggest that targeted credit rationing is ineffective in promoting change.

We study the determinants of the liquidity dry-up in the commercial paper market in March 2020 and the role of central bank interventions in reviving the market. We show that the dry-up was driven by money market funds (MMFs) - the key investors in the commercial paper market - that faced investor outflows. Using security-level fund holdings, we establish that the liquidity crisis in MMFs affected corporate funding: non-financial companies were less likely to issue commercial paper if their commercial paper was held by funds experiencing larger investor outflows. We show that the revival of the market was driven by the ECB’s intervention in the European non-financial commercial paper market leading to better terms and conditions for eligible firms.

We develop a simple model of the liquidity and insurance capacity of the interbank network arising from loan syndication. We find that the liquidity capacity has increased significantly following the introduction of liquidity regulation, and that the liquidity co-insurance is economically important for the corporate sector. We also find that borrowers with higher reliance on credit lines have become more likely to obtain credit lines from syndicates with higher liquidity capacities. The increase in liquidity capacities and the assortative matching on liquidity characteristics has strengthened the importance of large banks as liquidity providers to the corporate sector.

Financial literacy affects wealth accumulation, and pension planning plays a key role in this relationship. In a large field experiment, we employ a digital pension aggregation tool to confront a treatment group with a simplified overview of their current pension claims across all pillars of the pension system. We combine survey and administrative bank data to measure the effects on actual saving behavior. Access to the tool decreases pension uncertainty for treated individuals. Average savings increase especially for the financially less literate. We conclude that simplification of pension information can potentially reduce disparities in pension planning and savings behavior.

We provide evidence of a positive causal link between financial knowledge acquired through business education and returns on stock investments. Using exogenous variation generated by admission thresholds to university business programs in Sweden, we document that early investments in financial sophistication causes individuals to invest significantly more in the stock market, to earn higher portfolio returns, and to end up accumulating higher levels of wealth. Investments in financial sophistication at the launch of economic life thus significantly alters the life cycle wealth profiles of individuals.

We study how bankers affect financial decisions of their social connections. Register data from Finland allow us to relate professional transitions into the banking industry to the financial decisions of the newly appointed banker’s family members. This within-individual identification strategy reveals a strong positive effect on financial market participation. The banker effect declines in social distance, emanates largely from nonparticipating individuals, and is stronger for riskier assets. Market participants appear unaffected. Additional results suggest bankers’ sales skills are instrumental for effecting change rather than their financial knowledge solely. These insights are relevant for understanding the design of policies attempting to improve financial literacy, the impact and value of financial advice, and the nature of expertise in financial markets.

We study the relation between banks’ environmental reporting and lending activities. We create a proxy for environmental-themed disclosures using content analysis on banks’ investor reports. Taking advantage of granular loan-level data from a euro-area credit registry, we show that banks with extensive environmental disclosures lend more to brown borrowers and do not provide more credit to firms in green industries. We find that these results are not driven by banks’ financing of brown borrowers’ transition to greener technologies. Instead, these banks lend to the weakest borrowers in brown industries, especially if they have low capital adequacy. Our results suggest that European banks overemphasize their climate goals and credentials, but continue to be tied to their established credit relationships with polluting borrowers.

Does an increase in credit supply affect firms' likelihood to invest in green technologies? To answer this question, we use text algorithms to extract information on green investments from the comments to the financial statements of Italian SMEs between 2015 and 2019. To identify the effect of credit supply, we use all loans disbursed by banks operating in Italy to construct a firm-specific time-varying instrument for credit availability. We find a large positive elasticity of green investments to credit supply. The effect is concentrated among firms with high availability of internal capital and in areas with higher preferences for environmental protection. Subsidies and market competition can spur green investments if combined with environmental awareness.

How do bankers treat green firms? Utilizing unique loan application and banker preference data from a mid-sized bank, we find that customer managers, serving as front-line bankers, provide more favorable recommendations for green firms, particularly when they hold strong green values. However, a minority of environmentally skeptical bankers counteract this trend. These brown managers fake green interests when their recommendations bear no weight, and conversely, diminish their endorsements to green firms when they do hold significance. Additionally, brown loan officers, acting as superiors to these managers, strive to offset positive green firm evaluations by downgrading them.

I analyze the effects of bank competition on gender and racial gaps in entrepreneurship. Exploiting interstate bank deregulation from 1994 to 2021, I find that stronger bank competition increases the quantity and quality of banking services provided to minority borrowers. Developing a novel measure of discrimination using narrative information in the complaints filed with the Consumer Financial Protection Bureau, I show that bank competition reduces discrimination, loosening the financial constraints of female and minority entrepreneurs. Stronger bank competition also reduces the gender and racial gap in firm performance and business equity accumulation, fostering wealth equality and generating equitable economic growth.

We analyze, benchmark, and run randomized controlled trials on a panel of 7,463 U.S. entrepreneurs making incentivized sales forecasts. We assess accuracy using a novel administrative dataset obtained in collaboration with a leading US payment processing firm. At baseline, only 13% of entrepreneurs can forecast their firm’s sales in the next three months within 10% of the realized value, with 7.3% of the mean squared error attributable to bias and the remaining 92.7% attributable to noise. Our first intervention rewards entrepreneurs up to $400 for accurate forecasts, our second requires respondents to review historical sales data, and our third provides forecasting training. Increased reward payments significantly reduce bias but have no effect on noise, despite inducing entrepreneurs to spend more time answering. The historical sales data intervention has no effect on bias but significantly reduces noise. Since bias is only a minor part of forecasting errors, reward payments have small effects on mean squared error, while the historical data intervention reduces it by 12.4%. The training intervention has negligible effects on bias, noise, and ultimately mean squared error. Our results suggest that while offering financial incentives make forecasts more realistic, firms may not fully realize the benefits of having easy access to past performance data

We employ a dynamic search-and-matching model with bargaining between venture capitalists (VCs) and startups, utilizing two symmetric incentivized resume rating (IRR) experiments involving real US VCs and startups, to explain the matching outcome in the US entrepreneurial finance industry. Participants evaluate randomized profiles of potential collaborators, incentivized by the real opportunities for preferred cooperative partnerships. Using these experimental behaviors and real-world portfolio data as inputs to our structural estimation, we identify a significant impact of various human and non-human traits on equilibrium continuation values, matching likelihoods, and payoffs from matching for both startups and VCs. These traits include startups’ human assets (i.e., educational background, entrepreneurial experiences) and non-human assets (i.e., traction, business model), as well as investors’ human capital (i.e., entrepreneurial experiences) and organizational capital (i.e., previous financial performance, fund size). Results show that, while the total value of matching increases, the share of a startup/VC's payoff in the total value of matching diminishes substantially (in the range of .65 to .35) when the counterparty type becomes more attractive. Ultimately, we find that variations in the matching likelihood play a dominant role in explaining how the expected payoff from collaboration varies for startups and VCs when dealing with attractive and unattractive counterparty types.

When political institutions fail to control firm externalities, responsible investors can act as substitutes for government intervention. Individual investors, however, are unlikely to consider the aggregate effects of their choices, which raises the question of whether responsible capital is efficiently allocated across firms in the economy. In a general equilibrium model with heterogeneous social attitudes, we show that responsible investors tend to concentrate on a subset of firms while excluding others. This concentration of green capital can create product market power and crowd out the green investments of excluded firms. If this crowding-out dominates, aggregate CSR investments and welfare are higher without responsible investments.

This paper models strategic voting on ESG proposals by blockholders with heterogeneous reputational concerns and varying levels of commitment to ESG values. ESG activists, whose public-good gains from intervention are not attenuated by selling shareholders’ free-riding, rationally sponsor even long-shot proposals. Proposals that lower firm value but produce environmental benefits pass with positive, but perhaps small, probability. Our analysis leads to some non-obvious insights: neither increases in blockholders’ personal commitments to ESG values nor increases in blockholder dispersion reliably increase the probability of proposal success. However, the probability of success is uniformly increased both by increasing overall reputational pressure on blockholders and by increasing the gap between the pressure faced by the most and least pressured blockholders.

Blanket exclusion of “brown” stocks is seen as the best way to reduce their negative externalities by starving them of capital. We show that a more effective strategy may be tilting – holding a brown stock if the firm has taken a corrective action. While such holdings allow the firm to expand, they also encourage the action. We derive conditions under which tilting dominates exclusion for externality reduction. If the action is not publicly observable, the investor might not tilt even if she can gather private information on the action – tilting would lead to accusations of greenwashing. The presence of an arbitrageur who buys underpriced stocks increases the relative effectiveness of tilting. A responsible investor who is partially profit-motivated may be more likely to tilt than one whose sole objective is minimizing externalities.

We study the relationship between corporate taxation and carbon emissions in the U.S. We show that dirty firms pay lower profit taxes. This relationship is driven by dirty firms benefiting disproportionately more from the tax shield of debt due to their higher leverage. In addition, we document that the higher leverage of dirty firms is fully accounted for by the larger share of tangible assets owned by such firms. We build a general-equilibrium multi-sector economy and show that a revenue-neutral increase in profit taxation could lead to large decreases in aggregate carbon emissions without any noticeable change in GDP.

This paper exploits the operation of a sea wall built to protect the city of Venice from increasingly high tides to provide evidence on the capitalization of public infrastructure investment in climate change adaptation into housing values. Properties above the sea wall activation threshold experience a permanent reduction in flood risk and expected damages, which are reflected in higher prices. Using a difference-in-differences hedonic design we show that the sea wall led to a 4.5% increase in the value of the residential housing stock in Venice, which is a lower bound on the total welfare gains generated by the infrastructure.

We study the link between exposure to pollution information and investment portfolio allocations, exploiting the rollout of air quality monitoring stations during 2006-2019 in India. Using a triple-difference framework, we show that retail investors' investments in ``brown'' stocks become more negatively related to local air pollution after a monitoring station appears nearby. Since green stocks do not outperform brown stocks over this period, we suggest that our findings are likely driven by investor tastes and pollution salience rather than a shift in expected returns.The effect of pollution information on investment choices is most prominent amongst tech-savvy investors who are most plausibly ``treated'' by real-time pollution data, and by younger investors who tend to be more sensitive to environmental concerns. Overall, our results provide micro-level support for the view that salience of environmental conditions affect investors' tastes for green versus brown investments.