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.

Using a sample of U.S. commercial banks from 2009 to 2022, we find that the flow of non-core deposits, rather than that of core deposits, becomes more sensitive to bank performance as banks receive increased attention on Twitter. This effect is particularly pronounced during periods of poor bank performance, when Twitter discussions are more influential, and for banks with more liquidity mismatch. Our results suggest that social media, rather than merely disseminating information about bank performance, makes depositors aware of their peers’ attention to banks, thereby intensifying the sensitivity of deposit outflows to weak fundamentals.

Using text data from Reddit, we construct inter-firm linkages based on shared discussions and common authors on social media. We find that firms linked on social media have similar fundamentals characteristics. The positive predictability of the returns of their Reddit peer stocks on focal stocks’ future returns suggests a sluggish dissemination of information. Our findings show that social media activities capture the collective cognition of the public, effectively reflecting the financial network in an implicit way.

The “Internet celebrity economy” is a business model aimed at capitalizing on online traffic based on the purchasing power of users on social media in which “influencers”—highly influential individuals—exercise their marketing power to create a fandom. China has witnessed an abrupt outbreak in its “Wanghong” (internet celebrity) economy since 2016, eventually leading to consecutive high closes for related stocks from around 2020. The empirical findings are as follows: First, investors’ attention to Wanghong stocks and cumulative abnormal returns (CARs) are significantly positively associated. However, operational results and CARs are weakly linked, implying that the economic impact of intense influencer marketing is short-lived, and abnormal returns constitute an anomaly. Second, the positive abnormal returns of Wanghong stocks last approximately six months, which overlaps with the boom period of the Wanghong index based on influencer news articles.

This study investigates how digital transformation affects public sentiment toward firms on social media platforms in China. Using 2008-2022 data on Chinese listed companies and multivariate regression analysis, this paper identifies that digital transformation boosts positive online comments and sentiment. This relationship is mediated by gains in total factor productivity from digital initiatives. Moreover, concurrent green transformation positively moderates the effect, amplifying the impact of digital moves on online positive sentiment. Heterogeneous results reveal that the digital transformation effect on online positive sentiment is greater for state-owned, high-tech, and large companies. To our knowledge, this is the pioneering study to examine the linkage between corporate digital transformation and online public sentiment. The findings reveal whether, how, and when digital transformation shape more favorable public sentiment and online buzz. Companies can leverage digitalization, productivity improvements, and green development to foster positive perceptions and enhance their online reputation.

During the past decade, Wechat-public-accounts (WPAs) have gained increasing popularity as a novel tool for voluntary disclosure among Chinese public firms. This paper examines whether WPA disclosures provide value-relevant information to the market. Using a topic model to process over 1.6 million WPA articles during 2012-2020 and an event study design, we find that the stock market reacts strongly following a WPA disclosure event and the magnitude varies with the topic and textual feature of the WPA articles. We further present evidence that firms use their WPAs to provide new information rather than reinforce information that is already presented in other channels. Moreover, financial analysts, journalists, and retail investors rely on corporate WPAs for their information production. Collectively, our findings indicate that corporate WPAs are an economically significant source of new information for market participants that supplement traditional disclosure channels considered in prior studies.

A deep learning method is applied to predict stock portfolio allocation in the Chinese stock market. We use 6 original price and volume series as benchmark model settings and further explore the model's predictive performance with social media sentiment. Our results show that our model can achieve a high out-of-sample Sharp ratio and annual return. Moreover, social media sentiment could increase the performance for both Sharp ratio and annual return while reducing annual volatility. We provide an end-to-end stock portfolio allocation model based on deep neural networks.

We conducted a nationwide field experiment in China to evaluate the direct and indirect impacts of assigning firms to public or private citizen appeals treatments when they violate pollution standards. There are three main findings. First, public appeals to the regulator through social media substantially reduce violations and pollution emissions, while private appeals cause more modest environmental improvements. Second, experimentally adding “likes” and “shares” to social media appeals increases regulatory effort, suggesting visibility as an important mechanism. Third, treatment pollution reductions are not offset by control firm increases, based on randomly varying the proportion of treatment firms at the prefecture-level.