This paper investigates the impact of curation algorithms through the lens of stock market dynamics. By innovatively incorporating the dynamic interactions between social media platforms, investors, and stock markets, we construct the Social-Media-augmented Artificial Stock marKet (SMASK) model under the agent-based computational framework. Our findings reveal that curation algorithms, by promoting polarized and emotionally charged content, exacerbate behavioral biases among retail investors, leading to worsened stock market quality and investor wealth levels. Moreover, through our experiment on the debated topic of algorithmic regulation, we find limiting the intensity of these algorithms may reduce unnecessary trading behaviors, mitigates investor biases, and enhances overall market quality. This study provides new insights into the dual role of curation algorithms in both business ethics and public interest, offering a quantitative approach to understanding their broader social and economic impact.

With China being a large developing economy, the trading in China’s stock market is dominated by retail investors, and its government actively participates in this market. These features are quite different from those of typical developed markets, and This review focuses on two important questions: how do retail and institutional investors trade in China and why? We have three main findings after reviewing 100+ previous studies. First, small retail investors have low financial literacy, exhibit behavioral biases, and not surprisingly, negatively predict future returns; whereas large retail investors and institutions are capable of process information, and they positively predict future returns. Second, the macro- and firm-level information environment in China is slowly but gradually improving. Finally, the Chinese government actively adjusts their regulations of the stock market to serve the dual goals of growth and stability, with many of them being effective, while some may not generate intended consequences.

Using unique data on Alipay users' investment accounts, we find that, in addition to generating better performance than investors’ self-directed portfolios, robo-advising has a positive spillover effect on its adopters in terms that it improves their investment behaviors. Investors have more diversified portfolios and exhibit fewer behavioral biases in portfolio management and fund choices in their self-directed accounts after adopting robo-advising. The spillover effect is more prominent for adopters who interact with the service more actively and who were less sophisticated before adopting the app. We also find that adopters learn from the robo-advisor by simply imitating its portfolios or strategies. Collectively, this study provides large-sample, non-laboratory evidence that robo-advising effectively plays a role in educating investors through repeated interactions with its adopters and setting investment models that are easy to follow.

We examine how stakes affect investor behaviors. In our unique setting, the same investors trade stocks in real accounts using their own money and, at the same time, trade in a simulated setting. Our real-world within-investor estimation produces strong evidence that investors exhibit stronger biases and perform worse in their higher-stakes real accounts than in their lower-stakes simulated accounts. Even with no monetary stakes, investors exhibit strong biases in their simulated accounts, and biases in the two types of accounts are strongly positively correlated. Such behavioral consistency suggests that low-stakes experimental methods, although imperfect, can be informative about real-world human behaviors. Using account data from two brokerage companies, we find that investors exhibit a stronger disposition effect on positions with greater portfolio weight. Hence, the finding that stakes-strengthening-biases may not be unique to the comparison between no-monetary and high-monetary stakes.

Using comprehensive account-level data from 2016 to 2019, we examine retail investor trading behavior in the Chinese stock market. We separate millions of retail investors into five groups by their account sizes and document strong heterogeneity in their trading dynamics and performance. Retail investors with smaller account sizes cannot predict future price movements correctly, in the sense that they buy future losers and sell future winners. These investors fail to process public news and display behavioral biases such as overconfidence and gambling preferences. In sharp contrast, retail investors with larger account balances predict future returns correctly, incorporate public news in their trading, and gain more in stocks which are more attractive to investors with behavioral biases. For liquidity provision, the smaller retail investors follow daily momentum strategies, demanding immediate liquidity, while they become contrarian over weekly horizons, and they contribute positively towards firm-level liquidity. On the contrary, larger retail investors ae contrarian at daily horizons, providing immediate liquidity, but their potentially informed trades demand liquidity over longer terms.

In the developing Chinese capital market which dominated by individual investors and potentially suffer from more behavioral biases, we simultaneously examine the trading and monitoring role of mutual funds (as the largest institutional investor in China) in corporate acquisition activities where there are potentials for a wide disparity of interest between institutional investors and controlling shareholders. We find the level of holding by all mutual funds is not a superiors indicator of deal quality, there are some evidence that the collective holdings by the largest fund management companies positively relate to the deal quality and they potentially play the monitoring role in M&A event. Our paper contributes to the existing literature that “transient investors” can also gain from monitoring in the market where institutional investors has less dominant position.

Recent theoretical studies (Aggarwal and Wu,2006; Mei,Wu and Zhou,2004) show that trade-based stock price manipulation is a possible source of the momentum effect. This paper proposes three sets of testable hypotheses and provides empirical evidence for a manipulation-based explanation of momentum.Using weekly data on 14 CITIC industries in the Shanghai A-share market from 1997 to 2006, our analysis of industry momentum shows that cumulative returns first increase then decrease across holding periods, and the returns monotonically decrease across formation periods. This return pattern is consistent with a so-called”pump and dump”scheme,where momentum is created by manipulators and chased by speculators. We attribute the source of momentum to the positive own-autocorrelation, which dominates the cross-autocorrelation effect of industry returns. We also find that momentum profits are higher in the bull than in bear market, and most of the profits come from the gains of winning industries rather than the losses of losing industries. These empirical results,when related to some well-documented behavioral biases of Chinese speculators,tell us a possible stock-market manipulation story of momentum.