In classical models of information in asset markets, people learn from others only indirectly by observing market prices or quantities observed in markets. There is growing evidence that more direct forms of social interaction, such as conversation, also affect the decisions of economic agents. An open question is whether and how social interactions improve market efficiency.
Models of rational learning with social information transmission suggest that social interactions improve market efficiency. However, social interactions can also create free-riding incentives that discourage private information production. Furthermore, in models with imperfectly rational investors, social interactions can propagate incorrect beliefs or naive trading strategies, reducing information efficiency.
I plan to provide insight into these important questions by using a novel dataset of Facebook social connections. Given Facebook’s scale and the relative representativeness of its user body, the data provide a comprehensive measure of the geographic structure of social networks. Specifically, I am interested in the following questions. How do social networks affect the diffusion of information, price reactions, and investors' trading behaviors? What’s the relationship between a firm’s geographic locations in the social network and its valuation? Do social connections affect capital allocation in crowdfunding marketplaces? To what extent do social ties across regions where firms are located reveal a new dimension of these firms' fundamental comovements that previous studies have not identified? Do social interactions facilitate the spillover of innovation and the propagation of technology shocks across firms?
To address these questions, I plan to employ a rich set of data that include regional demographic and socioeconomic indicators, information from corporate balance sheets, financial market indicators, detailed trading records of stocks, as well as data from social network platforms and internet search engines. Through carefully designed empirical analysis, my research attempts to improve our understanding of social networks' role in economics and financial markets. The research also aims to provide policy implications regarding market designs that harvest social networks' power and promote allocation efficiency.
The proposed project aligns well with the statutory goals of the Keynes Fund in that it will shed light into the important question of how social networks contribute to “sources and consequences of failure of market efficiency,” and in particular, “capital market mispricing.” The proposed project will also speak to the “interactions between the financial markets and the real economy,” and therefore has the potential to “extend the frontiers of traditional economics.”
An article related to this research was published in the Financial Times on December 9, 2021: 'Meme Mania is Reshaping US Markets'. Professor Peng worked through a vast digital database of stock trading flows and social media networks to study the phenomena of “lottery stocks”.
Face Value: Trait Impressions, Performance Characteristics, and Market Outcomes for Financial Analysts
Face Value: Trait Impressions, Performance Characteristics, and Market Outcomes for Financial Analysts, Lin Peng, Siew Hong Teoh, Yakun Wang, Jiawen Yan, Journal of Accounting Research, Volume 60, Issue 2 (2022)
Using machine learning–based algorithms, we measure key impressions about sell-side analysts using their LinkedIn photos. We find that impressions of analysts’ trustworthiness (TRUST) and dominance (DOM) are positively associated with forecast accuracy, especially after recent in-person meetings between analysts and firm managers. High TRUST also enhances stock return sensitivity to forecast revisions, especially for stocks with high institutional ownership. In contrast, the impression of analysts’ attractiveness (ATTRACT) is only positively associated with accuracy for new analysts or when a firm has a new CEO or CFO. Furthermore, while high DOM helps male analysts’ chances of attaining All-Star status, it reduces female analysts’ accuracy and the likelihood of winning the All-Star award. In addition, the relation between TRUST and accuracy is modulated by the disclosure environment and is attenuated by Regulation Fair Disclosure. Our results suggest that face impressions influence analysts’ access to information and the perceived credibility of their reports.
Social Networks, Trading, and Liquidity
Social Networks, Trading, and Liquidity, Lin Peng, Qiguang Wang and Dexin Zhou, The Journal of Portfolio Management Market Microstructure, Vol. 48(5) (2022)
The recent meme stock saga has drawn attention to the growing role of social networks in capital markets. In this article, the authors summarize the latest research that uses large-scale, representative, real-world social network data to study social networks’ influences on trading, liquidity, and valuations of stocks. Institutional investors invest more heavily in stocks if there are strong social ties between the geographic locations of the institution’s headquarters and the firm’s headquarters. Further, a firm’s social ties to large institutional investors reduce its cost of capital, increase its valuation, and strengthen its liquidity. Social networks help to timely disseminate important news releases into prices but also trigger belief divergence and generate persistent excess trading. Moreover, social interactions can amplify investors’ behavioral biases and contribute to retail investors’ attraction to lottery-type stocks. The authors provide additional examples to further illustrate why the roles of social networks are of particular importance to market participants.
