This study reconsiders the timing of the equity risk factors by using the flexible neural networks specified for image recognition to determine the timing weights. The performance of each factor is visualized to be standardized price and volatility charts and `learned' by flexible image recognition methods with timing weights as outputs. The performance of all groups of factors can be significantly improved by using these ``deep learning--based'' timing weights. In addition, visualizing the volatility of factors and using deep learning methods to predict volatility can significantly improve the performance of the volatility-managed portfolio for most categories of factors. Our further investigation reveals that the timing success of our method hinges on its ability in identifying ex ante regime switches such as jumps and crashes of the factors and its predictability on future macroeconomic risk.

We develop an information choice model where information costs are sticky and co-move with firm-level intrinsic uncertainty as opposed to temporal variations in uncertainty. Incorporating analysts' forecasts, we predict a negative relationship between information costs and information acquisition, as proxied by the predictability of analysts' forecast biases. Finally, the model shows a contrasting pattern between information acquisition and intrinsic and temporal uncertainty, where intrinsic uncertainty strengthens return predictability of analysts' biases through the information cost channel, while temporal uncertainty weakens it through the information benefit channel. We empirically confirm these opposing relationships that existing theories struggle to explain.

We introduce a novel perspective on private information, specifically microstructure-based private information, to unravel how institutional investors predict stock returns. Using tick-by-tick transaction data from the Chinese stock market, we find that in retail-dominated markets, institutional investors positively predict stock returns, consistent with findings from institution-dominated markets. However, in contrast to the traditional view that institutional investors primarily rely on value-based private information, our results indicate that microstructure-based private information contributes almost as much to their predictive power as value-based private information does, with both components jointly accounting for approximately two-thirds of the total predictive power of institutional order flow. This finding reveals that retail investors’ trading activities significantly impact institutional investors, naturally forcing them to balance firm value information with microstructure information, thus profoundly influencing the price discovery process in the stock market.

This paper improves the expected return variable and the corresponding trend factor documented by Han, Zhou, and Zhu (2016) and reveals the incremental predictability of this novel expected return measure on stock returns in the Chinese stock market. Portfolio analyses and firm-level cross-sectional regressions indicate a significantly positive relation between the improved expected return and future returns. These results are robust to the short-, intermediate-, and long-term price trends and other derived expected returns. Our improved trend factor also outperforms all trend factors constructed by other expected returns. Additionally, we observe that lottery demand, capital states, return synchronicity, investor sentiment and information uncertainty can help explain the superior performance of the improved expected return measure in the Chinese stock market.

Beyond a bias toward local stocks, investors prefer companies in certain cities over others. This study uses the geographic network of investor-followed stocks from stock watchlists to identify intercity investment preferences in China. We measure the city-pair connectivity by its likelihood of sharing an investor in common whose stock watchlist is highly concentrated in the firms of that city pair. We find that a higher connectivity-weighted aggregate stock demand-to-supply ratio across connected cities is associated with higher stock valuations, higher turnover, better liquidity, and lower cost of equity for firms in the focal city. The effects are robust to controls for geographic proximity and the broad investor base, are stronger among small firms, extend to stock return predictability, and imply excess intercity return comovement. Our results suggest that city connectivity revealed on the stock watchlist helps identify network factors in asset pricing.

Prior literature shows that implied volatility spreads between call and put options are positively related to future underlying stock returns. In this paper, however, we demon- strate that the volatility spreads are negatively related to future out-of-the-money call option returns. Using unique data on option volumes, we reconcile the two pieces of evidence by showing that option demand by sophisticated, firm investors drives the posi- tive stock return predictability based on volatility spreads, while demand by less sophis- ticated, customer investors drives the negative call option return predictability. Overall, our evidence suggests that volatility spreads contain information about both firm funda- mentals and option mispricing.

Behavioral theories predict that firm valuation dispersion in the cross-section (‘‘dispersion’’) measures aggregate overpricing caused by investor overconfidence and should be negatively related to expected aggregate returns. This paper develops and tests these hypotheses. Consistent with the model predic- tions, I find that measures of dispersion are positively related to aggregate valuations, trading volume, idiosyncratic volatility, past market returns, and current and future investor sentiment indexes. Disper- sion is a strong negative predictor of subsequent short- and long-term market excess returns. Market beta is positively related to stock returns when the beginning-of-period dispersion is low and this rela- tionship reverses when initial dispersion is high. A simple forecast model based on dispersion signifi- cantly outperforms a naive model based on historical equity premium in out-of-sample tests and the predictability is stronger in economic downturns.

Beyond a bias toward local stocks, investors prefer companies in certain cities over others. This study uses the geographic network of investor-followed stocks from stock watchlists to identify intercity investment preferences in China. We measure the city-pair connectivity by its likelihood of sharing an investor in common whose stock watchlist is highly concentrated in the firms of that city pair. We find that a higher connectivity-weighted aggregate stock demand-to-supply ratio across connected cities is associated with higher stock valuations, higher turnover, better liquidity, and lower cost of equity for firms in the focal city. The effects are robust to controls for geographic proximity and the broad investor base, are stronger among small firms, extend to stock return predictability, and imply excess intercity return comovement. Our results suggest that city connectivity revealed on the stock watchlist helps identify network factors in asset pricing.

Investors' previous experiences with a stock affect their willingness to repurchase it. Using Chinese investor-level brokerage data, we find that investors are less likely to repurchase stocks that have increased in value since they were sold. We then construct a novel measure of Regret to capture investors' repurchase regret and investigate its asset pricing implications. Stocks with higher Regret experience lower buying pressure from retail investors in the future, leading to lower future returns. In terms of economic magnitude, portfolios with low Regret generate 12% more annualized abnormal returns. Further analyses show that the pricing effect of Regret is more pronounced among lottery-like stocks and those in which investors have previously gained profit. The results are robust to alternative estimations.

Using text data from Reddit, we construct inter-firm linkages based on shared discussions and common authors on social media. We find that firms linked on social media have similar fundamentals characteristics. The positive predictability of the returns of their Reddit peer stocks on focal stocks’ future returns suggests a sluggish dissemination of information. Our findings show that social media activities capture the collective cognition of the public, effectively reflecting the financial network in an implicit way.