Machine learning methods for big data trade off bias for precision in prediction. To understand the implications for financial markets, I formulate a trading model with a prediction technology where investors optimally choose a biased estimator. The model identifies a novel cost of complexity that arises endogenously. This effect makes it optimal to ignore costless signals and introduces in- and out-of-sample return predictability that is not driven by priced risk or behavioral biases. Empirically, the model can explain patterns of vanishing predictability of the equity risk premium. The model calibration is consistent with a technological shift following the rise of private computers and the invention of the internet. When allowing for heterogeneity in information between agents, complexity drives a wedge between the private and social value of data and lowers price informativeness. Estimation errors generate short-term price reversals similar to liquidity demand.

This paper develops an asset market-based test for preference for the timing of resolution of uncertainty. Our main theorem provides a characterization of preference for early resolution of uncertainty in terms of the risk premium of assets realized during the period when the informativeness of macroeconomic announcements is resolved. Empirically, we find support for preference for early resolution of uncertainty based on evidence on the dynamics of the implied volatility of S&P 500 index options before FOMC announcements.

I examine the implications of time inconsistency, modeled by hyperbolic discounting, for the excessive trading puzzle and asset prices. I show that unlike the case of long-term contracting with naive time-inconsistent agents where the welfare inefficiency of naivete disappears, dynamic trading allows time-consistent agents to exploit naive agents even over long horizons. In addition, partial awareness of the naivete bias induces leading trading motives such as gambling behavior and perceived information advantage, which can serve as a microfoundation for the puzzling excessive trading volume observed empirically. I show that the asymmetric information about the extent of partial naivete creates uncertainty about the optimal trading contract that the time-consistent agent can offer and endogenously generates extra risks in her consumption dynamics. As a result, the presence of naive time-inconsistent investors increases the risk-free rate, volatility, and risk premium in the economy.

Present-value logic says that aggregate stock prices are driven by discount-rate and cash-flow expectations. Dividends and net repurchases are both cash flows between the firm and household sectors. Aggregate dividend-price ratios do not forecast dividend growth, but do robustly forecast future buybacks and issuance. Long-run variance decompositions say that discount-rate and cash-flow expectations contribute equally to aggregate dividend-price-ratio variation.

Stock valuation ratios contain expectations of returns, yet, their performance in predicting returns has been rather dismal. This is because of an omitted variable problem: valuation ratios also contain expectations of cash flow growth. Time-variation in cash flow volatility and a structural shift toward repurchases have magnified this omitted variable problem. We show theoretically and empirically that scaling prices by forward measures of cash flows can overcome this problem yielding optimal return predictors. We construct a new measure of the forward price-to-earnings ratio for the S&P index based on earnings forecasts using machine learning techniques. The out-of-sample explanatory power for predicting one-year aggregate returns with our forward price-to-earnings ratio ranges from 7% to 11%, thereby beating all other predictors and helping to resolve the out-of-sample predictability debate (Goyal and Welch, 2008).

We argue that State of the Union (SOTU) addresses by the U.S. President function as announcements about broad government policies related to upcoming legislative activity of the administration. Unlike traditional macroeconomic and monetary policy announcements, SOTU addresses go back to the 1930s, which allows to expand the sample and context for the announcement effects for financial markets. We find that stock market returns on SOTU days are about ten times larger than on other days, are less volatile, and show strong pre-announcement drift prior to the address. SOTU returns increase in adverse economic, political and high volatility times, more so than on other announcement days. The overall evidence supports the risk premium/uncertainty resolution channel for announcement effects.

We document a regime change in the U.S. Treasury market post-Global Financial Crisis (GFC): dealers switched from net short to net long Treasury bonds. Consistent with this change, we derive ``net-long'' and ``net-short'' Treasury curves that account for dealers' balance sheet costs, and show that actual Treasury yields moved from the net short curve pre-GFC to the net long curve post-GFC. This regime change helps explain negative swap spreads post-GFC and the co-movement among swap spreads, dealer Treasury positions, yield curve slope, and covered-interest-parity violations, and implies changing effects for a wide range of monetary and regulatory policy interventions.

We examine the opportunity cost of foreign exchange (FX) intervention when both CIP and UIP deviations are present. We consider a small open economy that receives international capital flows through constrained international financial intermediaries. Deviations from CIP come from limited arbitrage or through a convenience yield, while UIP deviations are also affected by risk. We show that the sign of CIP and UIP deviations may differ for safe haven countries. We examine the optimal policy of a constrained central bank planner in this context. We find that there may be a benefit, rather than a cost, of FX reserves if international intermediaries value more the safe haven properties of a currency that domestic households. We show that this has been the case for the Swiss Franc and Japanese Yen.

The rise in net international bond positions of non-US investors over the last decade can account for the long-run surge in net dollar hedging positions in FX derivatives. The latter influence spot exchange rates through CIP arbitrage. Using intermediaries' capital ratio as a supply shifter, we identify a price inelastic derivative demand by institutional investors and document that changes in their net hedging positions can explain approximately 30% of all monthly variation in the seven most important dollar exchange rates from 2012 to 2022.

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.

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.

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.

We construct novel measures of net and gross short covering to examine when short sellers exit positions. We find that idiosyncratic limits to arbitrage, such as adverse stock price movements, volatility, and equity lending fees, are associated with significantly higher position closures. In contrast, we find little evidence that aggregate limits to arbitrage, including VIX, funding liquidity, and market liquidity, affect short covering. Short covering predicts future returns in the wrong direction, but only if it is induced by limits to arbitrage, consistent with the hypothesis that short sellers are forced to exit too early. It is also associated with lower price efficiency, higher future anomaly returns, and better performance of other informed traders. These results show that firm-level limits to arbitrage are important determinants of trading behavior and future returns.

Micro-level geographic proximity is associated with higher returns from short selling, with short trades by institutions near the target headquarters followed by more negative abnormal returns. Proximity matters more for stocks that are small, volatile, and have less analyst coverage, as well as for stocks with low market correlations and inefficient prices. Funds exhibiting larger effect of proximity are smaller and have higher returns and idiosyncratic volatility. The relationship between distance and returns is weaker during the COVID-19 pandemic. Overlapping nearby bars and restaurants between target and short seller matter but not during holidays, suggesting social interactions as a channel.

Short-sale costs eliminate the abnormal returns on asset pricing anomaly portfolios. While many anomalies persist out-of-sample, they cannot profitably be exploited due to stock borrow fees. Using a comprehensive sample of 162 anomalies, the average long-short portfolio return is a significant 0.15% per month before short-sale costs, and the returns are due to the short leg.

However, the average is −0.02% once returns are adjusted for borrow fees. The anomalies are not profitable even before accounting for fees if the high-fee observations, 12% of stock dates, are excluded from the analysis. Thus, short sale costs explain why many anomalies persist.

Central banks have engaged in a tightening cycle by raising rates, yet decided not to first reduce their balance sheets. We show that the scarcity of government bonds that followed the European Central Bank's Quantitative Easing efforts impeded the transmission of rate hikes to money market rates. When the ECB increased its policy rates by 50bp, the borrowing rate for loans collateralized by the most scarce bonds increased by only 30bp, in the repo market. We show that the lack of pass-through is priced in the yield of government bonds, which increased less for scarcer bonds. Heterogeneous bond holdings across institutions imply that the cost of (collateralized) funding varies significantly across European institutions.

How should monetary policy respond to deteriorating financial conditions? We develop and estimate a dynamic new Keynesian model with financial intermediaries and sticky long-term corporate leverage to show that active response to movements in credit conditions often helps to mitigate losses in aggregate consumption and output associated with macro fluctuations. A (credible) monetary policy rule that includes credit spreads is thus welfare-improving sometimes even obviating the need for explicit inflation targeting.

This paper investigates the stock market implications of the Federal Reserve's ability to control inflation, focusing on investor uncertainty and learning about it. Investor uncertainty about the Fed's ability to control inflation heightens stock market volatility and risk premium, particularly during pronounced monetary tightening and easing cycles. Moreover, investor learning generates an asymmetry that amplifies the impact of inflation surprises when the Fed tightens or loses its inflation control credibility, causing particularly high volatility and risk premium. Empirical tests support our model's predictions, highlighting the importance of investors learning about the Fed's ability to control inflation in shaping financial markets.