We examine the risk-return trade-off in market anomalies within the A-share market, showing that even decaying anomalies may proxy for latent risk factors. To balance forecast bias and variance, we integrate the 1/N and mean-variance frameworks, minimizing out-of-sample forecast error. Treating anomalies as tradable assets, we construct optimized long-short portfolios with strong performance: an average annualized Sharpe ratio of 1.56 and a certainty-equivalent return of 29.4% for a mean-variance investor. These premiums persist post-publication and are largely driven by liquidity risk exposures. Our results remain robust to market frictions, including short-sale constraints and transaction costs. We conclude that even decaying market anomalies may reflect priced risk premia rather than mere mispricing. This research provides practical guidance for academics and investors in return predictability and asset allocation, especially in the unique context of the Chinese A-share market.

This paper explores the impact of government intervention on local analysts’ earnings forecasts, based on a scenario of financial misconduct in Chinese state-owned enterprises (SOEs). The results show that, under the influence of the government, local analysts’ earnings forecasts for SOEs with financial misconduct are less accurate and more optimistically biased. Further heterogeneity analysis reveals that forecast bias by local analysts is greater when officials have stronger promotion incentives, when regions are less market-oriented and have a larger share of the state-owned economy, and when SOEs contribute more to taxation and employment. In further analysis, we find that local analysts have a more optimistic tone in reports targeting non-compliant SOEs. Local analysts who depend heavily on political information will also issue more biased and optimistic forecasts on SOEs with violations. Finally, as a reward for achieving government goals, the local brokerages affiliated with these analysts and providing these optimistic forecasts are more likely to become underwriters in seasoned equity offerings of SOEs. This paper reveals that government intervention significantly influences analyst forecasts, providing implications for understanding the sources of analyst forecast bias.

This study investigates the impact of analysts' information source diversity on forecast bias and investment returns. We combine the GPT-4o model and text similarity, to extract the names of information sources from the text of analyst in-depth reports. Using 349,200 sources, we calculate information diversity scores based on the variety of data sources to measure analysts’ ability of selecting relevant information. The findings reveal that higher information diversity significantly reduces forecast bias and enhances portfolio returns. The effect is particularly pronounced for large companies, state-owned enterprises, those with low analyst coverage, low firm-specific experience, and reports with positive forecast revisions. Institutional investors recognize the value of this skill, while retail investors remain largely unaware, which contributes to financial inequality. This study highlights the critical role of information diversity in analyst performance.

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 study how institutional investors utilize potentially biased information by analyzing the e ect of IPO underwriters' earnings forecasts on investors' bidding behaviors in Chinese IPO auctions. Despite the presence of upward biases in underwriters' earnings forecasts, we  nd that investors' bid prices are higher in IPOs with higher earnings forecasts. The investors' positive reaction to biased information can be explained in a rational expectation model where the underwriter has valuable information about the IPO but has a biased incentive in presenting the information to investors. Consistent with the model's predictions, we  nd that an investor's bid price is more sensitive to the underwriter's earnings forecast when the forecast bias is expected to be smaller, when the relative precision of the underwriter's information over the investor's information is higher, and when the investor has a higher valuation of the IPO.