Why is price momentum absent in China? Since momentum is commonly considered arising from investors’ under-reaction to fundamental news, we decompose monthly stock returns into news- and non-news-driven components and document a news day return continuation along with an offsetting non-news day reversal in China. The non-news day reversal is particularly strong for stocks with high retail ownership, relatively less recent positive news articles, and limits to arbitrage. Evidence on order imbalance suggests that stock returns overshoot on news days due to retail investors' excessive attention-driven buying demands, and mispricing gets corrected by institutional investors on subsequent non-news days. To avoid this tug-of-war in stock price, we use a signal that directly captures the recent news performance and re-document a momentum-like underreaction to fundamental news in China.

Although sentiment-driven investors are believed to play an important role in the Chinese stock market, there are very few sentiment measures at the individual stock level based on their trading activities. Due to the unique “T+1” trading rule in China, the low overnight return of stocks reflects intensified trading activities from short-term speculators. Therefore, we construct a sentiment measure for individual stocks based on the close-to-open return (CTO). We find that CTO positively predicts future stock returns in the cross-section, supporting the idea that low CTO, as an indicator of sentiment-driven excess demand, leads to lower subsequent returns. This finding is not driven by firm-specific news and alternative explanations based on risks, investor attention, or investor underreaction. Further analyses suggest that investors overpay for low-CTO stocks because of their inherent preference for this type of stock.

Purpose – Using a sample of 9,898 firm-year observations from 1,821 unique Chinese listed firms over the period from 2004 to 2019, this study aims to investigate whetherthe marketrewards meeting or beating analyst earnings expectations (MBE). Design/methodology/approach –The authors use an event study methodology to capture marketreactions to MBE. Findings – The authors document a stock return premium for beating analyst forecasts by a wide margin. However,there is no stock return premium forfirms that meet orjust beat analystforecasts, suggesting that the market is skeptical of earnings management by these firms. This market underreaction is more pronounced for firms with weak external monitoring. Further analysis shows that meeting or just beating analyst forecasts is indicative of superior future financial performance. The authors do not find firms using earnings management to meet or just beat analyst forecasts. Research limitations/implications – The authors provide evidence of market underreaction to meeting or just beating analyst forecasts, with the market’s over-skepticism of earnings management being a plausible mechanism for this phenomenon. Practical implications – The findings of this study are informative to researchers, market participants and regulators concerned about the impact of analysts and earnings management and interested in detecting and constraining managers’ earnings management. Originality/value – The authors provide new insights into how the market reacts to MBE by showing that the market appears to focus on using meeting or just beating analyst forecasts as an indicator of earnings management, while it does not detect managed MBE. Meeting or just beating analyst forecasts is commonly used as a proxy for earnings management in the literature. However, the findings suggest that it is a noisy proxy for earnings management.

We track the fundamental informed traders' (FITs) behavior and show the fundamental momentum effect in the Chinese stock market. We train the deep learning model with a set of fundamental characteristics to extract fundamental implied component from realized returns. The fundamental part characterizes the price movement driven by FITs. Fundamental momentum differentiates from the fundamental trend and is not quality minus junk (QMJ) factor. Underreaction bias helps explain the strategy, as it generates stronger profit during periods of low investor sentiment and aggregate idiosyncratic volatility. Fundamental momentum is not sensitive to changing beta and robust in subsamples and machine learning models.

In forecast surveys of aggregate macroeconomic and financial variables, the correlation between forecast errors and forecast revisions is positive at the consensus level, but negative at the individual level. Past literature has interpreted this discrepancy as evidence of underreaction to news at the aggregate level and overreaction at the individual level. In this paper, I challenge this view by arguing that noise in predictive judgment can account for the difference. Using a stylized model, I examine how introducing judgment noise at the individual level changes the interpretation of the correlation coefficients. First, a negative coefficient at the individual level no longer necessarily means overreaction. Second, the coefficient at the consensus level underestimates the degree of underreaction. Using forecast survey data, I provide evidence that judgment noise is large enough to reconcile the difference between the two coefficients. The structural parameter measuring over-/underreaction mainly points to underreaction, regardless of whether the model matches correlation coefficients at the individual or aggregate level.

Exploiting ffnancial news stories data, we construct news-implied linkages and document a strong lead-lag effect of ffrms with shared news coverage in China’s stockmarket. The news-link momentum strategy generates a monthly return of 1.33% and a four-factor alpha (Liu et al., 2019) of 1.43%. While prior evidence on the attention dynamics among ffrms with joint news coverage is limited, we show that the momentum spillover of news-linked ffrms is largely driven by investor underreaction. The return predictability from news links is also robust to controlling for alternative economic linkages. The ffndings suggest that information diffuses sluggishly among news-connected ffrms, thereby providing new evidence on the implication of media coverage for pricing efffciency.

Using novel data from a stock forum, we analyze return extrapolation in the cross-section. Our findings indicate that extrapolators overreact to the returns but underreact to the fundamentals. The post-earnings-announcement drift (PEAD) is more pronounced among firms with a high firm-level degree-of-extrapolation (DOX). Additionally, investors ask fewer questions about high-DOX firms’ fundamental information on official online interactive platforms. Extrapolation reduces the informativeness of stocks due to investors’ inattention to fundamentals. Furthermore, extrapolators’ overreaction to returns and underreaction to fundamentals increase stock price crash risks. These findings support explanations of extrapolation based on limited asymmetric attention.

We examine the process of stock prices adjusting to information conveyed by the trading process. Using the price impact of a trade to measure its information content, our analysis shows that the weekly price impact of market transactions has significant cross-sectional predictive power for returns in the subsequent week. The effect is sensitive to the level of informational asymmetry and is not due to excess liquidity demands or variations in rational risk premia. This finding suggests that prices may slowly incorporate trading information. We then characterize the key channel through which price underreaction occurs. We find that the price impact contains information that is not fully captured by public order flows and that a lead-lag effect exists regarding the arrival of information to different groups of investors. Hong and Stein’s (1999) gradual-information-diffusion theory seems the most likely explanation for price underreaction.