We analyze a model in which an anomaly is unknown to arbitrageurs until its discovery, and test the model implications on both asset prices and arbitrageurs’ trading activities. Using data on 99 anomalies documented in the existing literature, we find that the discovery of an anomaly reduces the correlation between the returns of its decile-1 and decile-10 portfolios. This discovery effect is stronger if the aggregate wealth of hedge funds is more volatile. Finally, hedge funds increase (reverse) their positions in exploiting anomalies when their aggregate wealth increases (decreases), further suggesting that these discovery effects operate through arbitrage trading.

We propose a latent-variables approach to recover extrapolative beliefs from asset prices. We estimate a present-value model of the price–dividend ratio of the market that embeds both return extrapolation and cash-flow extrapolation, alongside discount rates and rational expectations of dividend growth. This approach allows us to measure extrapolation bias without having to rely on survey data, and it inherently guarantees that the researcher focuses on a set of beliefs that matter for price formation. We show that extrapolative beliefs extracted from prices are highly correlated with surveys and that survey-based and price-based extrapolative beliefs share similar predictive properties for future returns, with the former improving upon the latter.

We use the advent of new credit default swap (CDS) trading conventions in April 2009—the CDS Big Bang—to study how a shock to funding liquidity impacts market liquidity. After the Big Bang, traders are required to pay upfront fees to execute CDS transactions, with the size of the fees depending on the level of CDS spreads. While CDS bid-ask spreads decline in aggregate after the Big Bang, they do so less for contracts that require larger fees. Furthermore, the funding effect is stronger for smaller and riskier firms and for noncentrally cleared contracts. The effect also becomes stronger after Deutsche Bank's exit.

We study the share privatization process in China to investigate whether and how the removal of market frictions is associated with efficiency gains. Prior to the reform, domestic A-shares were divided into tradable and non-tradable shares. As a result of the reform, holders of non-tradable shares compensated holders of tradable shares in order to make their shares tradable. We show that size is positively associated with both the gain in risk sharing and the price impact of more shares coming on the market as a result of the reform. Our study highlights the role of risk sharing in China's share issue privatization process. The Author 2011. Published by Oxford University Press on behalf of The Society for Financial Studies. All rights reserved. For Permissions, please e-mail: [email protected]., Oxford University Press.

We investigate the effect of managerial ability versus luck on bank loan contracting. Borrowers showing a persistently superior managerial ability over previous years (more likely due to ability) enjoy a lower loan spread, while borrowers showing a temporary superior managerial ability (more likely due to luck) do not enjoy any spread reduction. This finding suggests that banks can discern ability from luck when pricing a loan. Firms with high-ability managers are more likely to continue their prior lower loan spread. The spread-reduction effect of managerial ability is stronger for firms with weak governance structures or poor stakeholder relationships, corroborating the notion that better managerial ability alleviates borrowers’ agency and information risks. We also find that well governed banks are better able to price governance into their borrowers’ loans, which helps explain why good governance enhances bank value.

We survey a large, representative sample of retail investors in China to elicit their memories of stock market investments and their return expectations. We merge these survey data with administrative transaction data to test a model in which investors selectively recall past experiences to form their beliefs. Our analysis uncovers new facts about investor memory and highlights similarity-based recall as a key mechanism of belief formation in financial markets. A rising market prompts investors to recall their past experiences more positively, leading to more optimistic forecasts of future returns. Recalled experiences can explain cross-investor variation in return expectations and, in our setting, dominate actual experiences in their explanatory power. In the transaction data, we confirm that recalled experiences are reflected in investors’ trading decisions through a belief channel.

We construct real-time machine learning strategies based on a “universe” of fundamental signals. The out-of-sample performance of these strategies is economically meaningful and statistically significant, but considerably weaker than those documented by prior studies that use curated sets of signals as predictors. Strategies based on a simple recursive ranking of each signal’s past performance also yield substantially better out-of-sample performance. We find qualitatively similar results when examining past-return-based signals. Our results underscore the key role of feature engineering and, more broadly, inductive biases in enhancing the economic benefits of machine learning investment strategies.

We show that institutional investors are more likely to invest in firms from regions to which they have stronger social ties but find no evidence that these investments earn a differential return. Firms in regions with stronger social ties to locations with many institutional investors have higher valuations and liquidity. These effects are largest for small firms with little analyst coverage, suggesting that the investors’ behavior is explained by their increased awareness of firms in socially proximate locations. Our results highlight that the social structure of regions affects firms’ access to capital and contributes to geographic differences in economic outcomes.

Using data from a major online peer-to-peer lending platform, we document that, due to time pressure, investors appear to focus on interest rates and only partially account for credit ratings in their decisions. The effect is stronger for mobile-based investors than for PC-based ones. Our evidence suggests that this variation is caused by the difference in information content on the interfaces rather than differences in the devices’ physical attributes per se. Investors improve their decisions by slowing down and paying more attention to credit ratings after experiencing a loan default firsthand, but not after observing others experiencing defaults.

We show that hedge fund managers who more actively and astutely adjust the political sensitivity of their portfolios, in line with the dynamic U.S. political landscape, improve their investment performance. Funds that tilt their portfolios toward market segments expected to perform better during the new political regime, specifically around U.S. Presidential elections, generate significantly higher alphas. Further, hedge fund families with greater responsiveness to political changes exhibit persistently superior performance and are more likely to survive. Hedge fund investors reward more responsive fund managers with greater inflows.