I am a doctoral candidate at the Muma College of Business, University of South Florida, with an anticipated graduation date in May 2024. My research interests span FinTech, Investment, Asset Pricing, Corporate Finance, and Capital Markets. Specifically, I specialize in harnessing advanced financial technologies and big data analytics, including the analysis of unstructured textual data like corporate disclosures and social media content. My work aims to investigate the behaviors of analysts and investors, as well as the subsequent market implications.
My research has been accepted in Review of Financial Studies and has been presented at pretigious conference, such as the FMA, FARS, Texas A&M Young Scholars Conference, SFA, FFC et al.
Download Zicheng(Leo)’s Curriculum Vitae
1. Title: Place your bets? The value of investment research on Reddit's Wallstreetbets, Dec 2023, with Daniel Bradley, Russell Jame, and Jan Hanousek, Reviews of Financial Studies, Published. (Link)
We examine the consequences of due diligence recommendations on Reddit’sWallstreetbets (WSB) platform. Before the Gamestop (GME) short squeeze, recommendations are significant predictors of returns and cash-flownews. This predictability is completely eliminated post-GME. Post-GME, the fraction of reports emphasizing price-pressure or attention-grabbing stocks dramatically increases, and the decline in informativeness is concentrated in these reports. Similarly, retail trade informativeness increases following DD reports in the pre-GME period, but not post-GME. Our findings are consistent with the view that the Gamestop event altered the culture of WSB, leading to a deterioration in investment quality that adversely impacted smaller investors.
1. Measuring Information Quality by Topic Attention Divergence: Evidence from Earnings Calls, 2023 (link)
Leveraging computational linguistics and 20 million turns of dialogues from earnings conference calls over the period 2006-2022, I introduce a novel measure that quantifies the disparity in narrative focus between managers’ disclosures and analysts’ questions during these calls, denoted as Topic Attention Divergence(TAD). A higher level of TAD indicates a higher level of firm-investor asymmetry and lower information quality. My results show that higher TAD in earnings conference calls inversely (positively) predict firms’ future stock liquidity (cost of equity capital). Further, the predictive power of TAD is more pronounced in firms with higher information processing costs, characterized by smaller firm size, lower levels of analyst coverage, and lower institutional ownership. A long-short portfolio sorted by TAD earns an annual 8.18% risk-adjusted return and cannot be spanned by existing factor models, suggesting that information quality is priced.
The result in graph above has been controlled for SUE, analyst forecast dispersion, analyst coverage, volatility(-5,-1), volume(-45,-3), firm characteristics(size, mtb, leverage, roa), momentum, length of Q&A , managerial and analyst sentiments, as well as institutional ownership.
2. Does It Pay to Follow Investment Advice on YouTube?, 2020
I examine the skill of non-professional analysts (NPAs) that make stock recommendations on Youtube.com, the largest global video-sharing platform. By extracting text from video and identifying top influencers’ buy and sell recommendations on the platform, I find some evidence of investment value. On average, NPAs generate large, but insignificant positive abnormal returns. However, there is significant dispersion. Upon further analysis, I find that 70% of influencers indeed generate Alpha. Exploiting this dispersion, I construct two hedge portfolios that consistently generate positive abnormal returns over the full sample period. Overall, despite regulators concerns over non-professionals providing investment advice, my results suggest this fear is unfounded.
3. Firms' Idiosyncratic Signals: What are The Market Consequences of Being Different?, 2023
Through the utilization of computational linguistics, this research introduces a novel measure of divergence in narrative attention between a firm and its industry. This measure is derived from earnings conference call transcripts, specifically focusing on the differences in narrative focus and tone between the firm and the industry average. The findings reveal that this divergence not only offers substantial incremental information but also predicts higher subsequent stock returns and firm volatility. These results persist even after accounting for firm-specific characteristics and existing proxies for divergence of opinion, highlighting the unique value of this new approach in understanding the dynamics between a firm and its industry.
1. Does Analysts’ Attention Matter? Evidence From Questions in Earnings Conference Calls, 2024, with Daniel Bradley
2. Why Would Their Attention Deviate? Evidence From Wall-street Analysts and Social Media Analysts, 2024
3. Short Selling Activism and Its Impact on Topic Attention, 2024, with Jan Hanousek
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