Qihong Ruan 阮启宏
qr33@cornell.edu
PhD in Economics, Cornell University (2025)
My CV can be found here.
I graduated from Cornell University with a PhD in Economics. Here is my dissertation abstract. I am a Financial Economist specializing in Market Microstructure, Digital Assets, and AI in Finance.
I investigate the network structure of retail investor trading and its consequences for the U.S. equity market. Using Nasdaq's Retail Trading Activity Tracker (RTAT) from 2016 to 2024, I construct a directed network of stocks based on lead-lag correlations in retail trading volume. The methodology is first validated using the iShares Bitcoin Trust (IBIT) as a prototype. This approach successfully identifies economically meaningful peers, including crypto-related firms, growth tech stocks, large-cap ETFs, and inflation-hedging ETFs, whose metrics predict IBIT's market dynamics.
Applying this network framework to U.S. common stocks, I find that the average return of a stock's retail trading peers robustly predicts its future returns. I demonstrate that the effect operates through two primary channels: (1) price discovery, where retail networks aggregate dispersed information, evidenced by stronger effects when peer average ret is positive, the peer-own return gap is large, during "buy the dip" episodes, and in weaker institutional information environments; and (2) liquidity provision, where coordinated retail buying systematically provides depth to the market, particularly on the bid side, leading market makers to widen spreads to compensate for adverse selection risk. The return predictability is persistent and does not reverse, suggesting the permanent incorporation of new information rather than temporary price pressure.
Finally, I extend the analysis across the full CRSP universe, revealing that the peer effect is a pervasive phenomenon. It is statistically significant across various asset types—including common stocks, foreign stocks, REITs, ETPs, and American Depositary Receipts (ADRs)—and exchanges. The effect's magnitude varies logically across market segments, proving stronger for assets with higher information asymmetry, such as ADRs, and more pronounced on NASDAQ than on the NYSE. In contrast, volatility-linked products on the Cboe exchange do not exhibit significant cross-predictability.