In this paper, we use granular trading data from Taiwan between 2012 and 2016 to examine how local social capital influences retail investor behavior during corporate social irresponsibility (CSIR) events. Therefore, we are responding to longstanding calls in the international finance literature to explore investor behavior in non-US markets with distinct institutional and cultural characteristics. We find that investors residing in cities with higher social capital are less likely to purchase underpriced stocks following the announcements of negative events despite the potential for positive abnormal returns. This norm-driven restraint reflects a form of socially responsible investing motivated by community-based values rather than economic rationality. By documenting this behavior in an East Asian market, we extend the external validity of social norm theories developed in Western settings and contribute to a more nuanced understanding of how localized social preferences can influence asset pricing and capital allocation in a global context.

We investigate the relationships among search symbols, trading performance, and investor participation. We use two specific datasets from Google Trends’ search volume index. The search volume by number ticker significantly predicts high returns and high investor participation when applied by active retail investors investing in large firms. This does not hold true for less active retail investors who use Chinese company name tickers as their search terms. Our results indicate that the heuristic usage of number tickers to search for company information helps active retail investors to obtain better trading performance compared with less active retail investors who use Chinese company name tickers.

We examine whether household income influences the trading styles of retail investors and their investment performance. To investigate this question, we use a unique dataset of branch-level trading that contains all retail investors and observe that those investors with high income trade more and earn significantly higher returns in the stock market. In addition, this income effect becomes stronger for highly risky stocks, such as gambling or lottery-like stocks. These findings are in line with the information model theorized by Peress (2004) in which wealthy investors take extra risks by trading more stocks.

Using a novel proxy of investors' speculative demand constructed from online search interest in investment concepts, we examine how speculative demand affects the returns of Chinese stocks. We find that speculative demand increases following high market returns and predicts subsequent return reversals. Moreover, the speculative demand explains more variation in subsequent returns of A shares (more populated by retail investors) than B shares (less populated by retail investors). Our findings support the recently developed attention theory.