Utilizing data from the East Money stock forum, one of China's most influential financial portals, this study examines how online discussions among individual investors influence managerial learning from stock prices to make investment decisions. We find that higher discussion intensity is associated with lower investment-q sensitivity, indicating that investors' online discussions impede managerial learning from prices. This effect operates through a decline in information asymmetry and the probability of informed trading. Furthermore, the negative impact is more pronounced when managers exhibit stronger incentives to learn from market signals. Overall, our findings support a crowding-out mechanism: online discussions crowd out the private information gathering of informed traders, thereby reducing the informativeness of prices for real investment decisions.

We examine the impact of government social media on capital markets using China's staggered adoption of local government microblogs. A difference-indifferences design reveals economically significant increases in firm value, with stronger effects for more content-rich, original, and interactive microblogs. We find improvements in the corporate information environment and reductions in perceived uncertainty, with larger effects for firms with limited external information coverage and in stronger business environments. During the COVID-19 Wuhan lockdown, firms in regions with more active microblogs achieved better stock performance. Overall, government social media is a powerful tool for enhancing transparency, firm valuation, and market resilience.

We find that firm-specific social media tone influences leveraged trading. A more positive tone predicts greater next-day net margin purchasing, driven predominantly by sentiment. High margin purchasing following positive social media tone consistently yields inferior performance over both short and long horizons. Short sellers are collectively more sophisticated. They capitalize on fluctuations in social media tone, both positive and negative, through strategies that adjust to different tone windows and holding periods. While experienced, rational short sellers can swiftly profit from temporary negative sentiment, high short selling following persistently high social media tone is highly profitable over longer horizons.

We conduct a field experiment in China, o↵ering small business owners a conditional social media advertising subsidy. Beyond boosting business revenue and employment, the inter-vention significantly increases access to fintech credit: treated firms are more likely to open online stores and obtain online loans, while bank credit remains una↵ected. Our findings reveal a “fintech accelerator” mechanism—digital marketing drives sales growth that directly improves firms’ eligibility for fintech lending—demonstrating how targeted digital interven-tions can enhance financial inclusion and reshape credit allocation for small businesses.

Green bonds, pivotal to green finance, draw growing attention from scholars and investors. Social media’s proliferation has amplified the influence of investor sentiment, necessitating robust analysis of its market impact. However, general sentiment lexicons often fail to capture domain-specific slang and nuanced expressions unique to China’s bond market, leading to inaccuracies in sentiment analysis. Thus, this study constructs a specialized sentiment lexicon for the green bond market, namely the COBC (Chinese online bond comments sentiment lexicon), to dissect bond market slang and investor remarks. Compared to three general lexicons (Textbook, SnowNLP, and VADER), it improves the average prediction accuracy by approximately 87.2% in sentiment analysis of Chinese online language within the green bond domain. Sentiment scores derived from COBC-based dictionary analysis are systematically integrated as predictive features into a two-stage hybrid predictive model is proposed integrating Support Vector Machine (SVM), Auto-Regressive Moving Average (ARMA), Bidirectional Long Short-Term Memory Networks (BiLSTM), and Attention Mechanisms to forecast China's green bond market, represented by the China Bond 45 Green Bond Index. First, ARMA-SVR is employed to extract residuals and statistical features from the green bond index. Then, the BiLSTM-Attention model is applied to assess the impact of investor sentiment on the index. Empirical results show that incorporating investor sentiment significantly enhances the predictive accuracy of the green bond index, achieving an average of 67.5% reduction in Mean Squared Error (MSE), and providing valuable insights for market participants and policymakers.

To specify the peer effects that affect influencers’ sponsored content strategies, the current research addresses three questions: how influencers respond to peers, what mechanisms drive these effects, and the implications for social media platforms. By using a linear-in-means model and data from a leading Chinese social media platform, the authors address the issues of endogenous peer group formation, correlated unobservables, and simultaneity in decision-making and thereby offer evidence of strong peer effects on the quantity of sponsored content but not its quality. These effects are driven by two mechanisms: a social learning motive, such that following influencers emulate leading influencers, and a competition motive among following influencers within peer groups. No evidence of competition motive among leading influencers or defensive strategies by leading influencers arises. Moreover, peer effects increase influencers’ spending on in-feed advertising services, leading to greater platform revenues, without affecting the pricing of sponsored content. This dynamic may reduce influencers’ profitability, because their rising costs are not offset by higher prices. These findings emphasize the need for balanced strategies that prioritize both platform growth and influencer sustainability. By revealing how peer effects influence competition and revenue generation, this study provides valuable insights for optimizing content volume, quality, and financial outcomes for social media platforms and influencers.

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.