This paper shows that trends typically used for monetary policy guidance are also effective in predicting market excess returns. Using a linear combination method across 14 economic and financial predictor variables, we find that moving-average trends outperform the variables’ current values in forecasting market returns. Incorporating neural networks further improves these predictions. Our findings underscore the importance of trends, supporting the Federal Reserve’s emphasis on integrating trends with lagged variables. When accounting for nonlinearity, we find that market return predictability is significantly greater than commonly believed. Our results are robust across both U.S. and global equity markets. JEL C52, C53, C55, C58, G17

[Recent winners have temporarily higher loadings than recent losers on the growth rate of industrial production. The loading spread derives mostly from the positive loadings of winners. The growth rate of industrial production is a priced risk factor in standard asset pricing tests. In many specifications, this macroeconomic risk factor explains more than half of momentum profits. We conclude that risk plays an important role in driving momentum profits.]

This paper examines the effect of the All‐Star award on the performance of Chinese financial analysts. Leveraging unique voting data from 2007 to 2016 and a regression discontinuity design (RDD), we find that the All‐Star award significantly enhances recipients' fundamental analysis. Awarded analysts issue more accurate earnings forecasts, and their stock recommendations convey greater information content for firms with higher information asymmetry. RDD results also indicate that award recipients gain increased resources and greater flexibility in reallocating time and effort. Post award, analysts concentrate on fewer industries, cover more firms within each industry, issue forecasts more frequently, expand their teams, and conduct more site visits. Surveys of analysts and institutional investors corroborate these findings, highlighting increases in site visits and roadshows following the award. Overall, the results suggest that the All‐Star award boosts analyst performance by fostering more concentrated coverage and improving access to both internal and external resources.

I study how the disclosure of the liquidity coverage ratio mandated for a group of systemically important U.S. banks affects peer banks' liquidity holdings. I predict that the disclosure mitigates uncertainty about aggregate liquidity risk by providing insight into the likelihood of market‐wide liquidity shocks and specific sources of liquidity stress. This uncertainty resolution, in turn, reduces nondisclosing banks' precautionary demand for liquidity. Using bank business interactions to measure the treatment intensity of the disclosure, I find that more treated nondisclosing banks cut their liquidity significantly more in response to the disclosure. In addition, the disclosure rule was followed by lower overall liquidity and a build‐up of systemic risk, indicating an economically considerable disclosure spillover effect in the aggregate. My paper reveals a new economic force, the spillover effect of mandated liquidity disclosure, that shapes banks' liquidity holdings.

I develop a theory to study the consequences of providing more detailed information to rationally inattentive investors. I first consider a simple data‐provision problem and show that adding more data or detail in financial statements can make it more difficult for investors to extract information. Consequently, investors who have limited information‐processing capacity may prefer less detailed information. I also show that when investors' decisions are complements, providing details in addition to a summary may reduce investors' welfare. More specifically, because of increased disclosure of details, a coordination failure could occur in investors' attention‐allocation decisions. By showing that adding more detail in financial statements can lead to an information overload problem for investors, this study yields valuable insights for accounting standard setters.

Buyers often search across sellers to learn which product best fits their needs. We study how sellers manage these search incentives through their disclosure strategies (e.g., product trials, reviews, and recommendations) and ask how competition affects information provision. If sellers can observe the beliefs of buyers or can coordinate their strategies, then there is an equilibrium in which sellers provide the “monopoly level” of information. In contrast, if buyers’ beliefs are private, then there is an equilibrium in which sellers provide full information as search costs vanish. Anonymity and coordination thus play important roles in understanding how advice markets work.

An empirical q-factor model consisting of the market factor, a size factor, an investment factor, and a profitability factor largely summarizes the cross section of average stock returns. A comprehensive examination of nearly 80 anomalies reveals that about one-half of the anomalies are insignificant in the broad cross section. More importantly, with a few exceptions, the q-factor model's performance is at least comparable to, and in many cases better than that of the Fama-French (1993) 3-factor model and the Carhart (1997) 4-factor model in capturing the remaining significant anomalies.

[A new methodology for equity valuation arises from the perspective of managers' supply of capital assets. Under q-theory, managers optimally adjust the supply of assets to changes in their market value. The first-order condition of investment then provides a valuation equation that infers asset prices from managers' costs of supplying the assets. This equation fits well the Tobin's q levels across many testing assets, including portfolios formed on q. With current investment-to-capital as the only input, the supply approach does not require cash flow forecasts or discount rate estimates, both of which are notoriously difficult to obtain in practice.]

[We investigate whether skilled hedge fund managers are more likely to pursue unique investment strategies that result in superior performance. We propose a measure of the distinctiveness of a fund's investment strategy based on historical fund return data. We call the measure the "Strategy Distinctiveness Index" (SDI). We document substantial cross-sectional variations as well as strong persistence in SDI. Our main result indicates that, on average, a higher SDI is associated with better subsequent performance. After adjusting for risk, funds in the highest SDI quintile outperform funds in the lowest quintile by 3.5% in the subsequent year.]