This paper focuses on the role of Confucian clan in risk sharing and examines its dynamic impact on the development of the insurance sector. Strikingly, we find that Confucian clan hindered the development of the insurance sector at the initial stage of modern China while it promoted the development of the insurance sector at the current stage of modern China. Further analyses indicate three potential explanations underlying the contrasting results: the increasing risk unpredictability and severity of losses, the migration of clan members, and the influence of Western culture. The risksharing experience in clan groups enhances individuals’ awareness of insurance, which induces them to embrace formal insurance when clan-based risk sharing is incomplete. Our study provides valuable insights into the relation between informal risk sharing and formal insurance.

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 ffrm-level cross-sectional regressions indicate a signiffcantly 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.

This paper decomposes news-implied linkages into two types: leader-follower links (LF) and peer links (PE), based on people's reading and information-processing habits. We explore how the structure of information impacts processing costs and subsequently leads to market outcomes by examining momentum spillover effects via these distinct linkage types. Our findings indicate that the information structure of leader-follower links is more readily comprehensible to investors than peer linkages. We provide empirical evidence of this by demonstrating faster attention spillover from leader to follower than among peer firms, using Baidu search data. Furthermore, we document that due to the lower information processing cost, information transmits through the leader-follower linkages more quickly, leading to a weaker momentum spillover effect compared to the more complex and less easily perceivable peer links.

Based on a unique arrangement of trading and disclosure times around earnings announcements in the Chinese stock market, we provide evidence of a striking overnight-intraday disparity in terms of the reaction to earnings news. Specifically, we find that the overnight period exhibits a strong and consistent reaction to earnings announcements, whereas the intraday period trades against both the earnings news and the prior market reaction during the overnight period. In addition, we show that abnormal overnight returns on earnings announcement days exhibit strong predictability for future stock returns, consistent with the overnight returns containing valuerelevant signals. In contrast, we observe no return predictability for abnormal intraday returns on earnings announcement days, which as a result, also undermines the return predictability of abnormal daily returns. We propose possible explanations for the overnight-intraday disparity. We conclude that the differences in trading mechanisms between the two periods as well as in investor composition likely drive the phenomenon.

By using a proprietary real–time daily online sales data collected in China from 10–billion consumer accounts, this paper ffnds that the ffrm–level daily online sales growth (DOSG) can positively predict future one–day to more than three–month cumulative stock returns in the cross section, implying a growth premium in contrast to Lakonishok, Shleifer, and Vishny (1994). A spread portfolio that is long on stocks with high DOSG and short on stocks with low DOSG delivers an abnormal return of around 30 basis points per week. DOSG derives its short–run (e.g., weekly) predictability from investor sentiments, tilting to a behavioral explanation. However, it derives its medium to long–run (e.g., three–month) predictability from fundamentals, voting for a rational explanation. Our further evidence indicates that stocks with high DOSG experience more intensive information acquisition from retail investors and less severe crash risk, implying online sales as a channel for retail investors to get access to daily real–time ffrm fundamentals.

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.

Against the backdrop of the United Kingdom's withdrawal from the European Union (BREXIT), this study examines predictability in the stock markets of sixteen European countries, the United States, and the BRICS (Brazil, China, India, Russia, and South Africa) by analyzing how their returns predict the returns of sixteen commodities at different quantile levels. The study builds upon existing literature on predictability and extends it by investigating the impact of the BREXIT crisis on these markets. The findings suggest that investors can hedge their portfolios with various commodities during times of the BREXIT crisis, but caution is advised, and the trend of both equities and commodities should be closely monitored before making investment decisions.

We extend the ex-ante mean-variance (SVIX) asset pricing models of Martin (2017) and Martin-Wagner (2019) to a mean-variance-asymmetry (AVIX) framework by incorporating higher-moment and co-moment risk in asset pricing. Our proposed AVIX model is risk-neutral with left-tail asymmetries in returns to correct the SVIX approach's downside bias. We derive an option implied market beta of a stock as the weighted average of the betas of SVIX and AVIX. Empirically, the implied beta has significant predictability of risk/return relationship We develop an investible portfolio (MKT*) that mimics realized outcomes on the implied market index adjusted for volatility asymmetry.

We provide an easy method to identify purchases and sales initiated by retail investors using recent, widely available U.S. equity transactions data. Individual stocks with net buying by retail investors outperform stocks with negative imbalances by approximately 10 basis points over the following week. Less than half of the predictive power of marketable retail order imbalances is attributable to order flow persistence; contrarian trading (a proxy for liquidity provision) and public news sentiment explain little of the remaining predictability. There is suggestive (but only suggestive) evidence that retail marketable orders contain firm-level information that is not yet incorporated into prices.

Deep learning technology is rapidly adopted in financial market settings. Using a large data set from the Chinese stock market, we propose a return-risk trade-off strategy via a new transformer model. The empirical findings show that these updates, such as the self-attention mechanism in technology, can improve the use of time-series information related to returns and volatility, increase predictability, and capture more economic gains than other nonlinear models, such as LSTM. Our model employs Shapley additive explanations (SHAP) to measure the “economic feature importance” and tabulates the different important features in the prediction process. Finally, we document several economic explanations for the TF model. This paper sheds light on the burgeoning field on asset allocation in the age of big data.