Alfred Qi FAN
Alfred Qi FAN
Postdoctoral Research Fellow
Postdoctoral Research Fellow
supervisor: Prof. Yingying Li
[English CV] [中文简历]
Clear Water Bay, Kowloon, Hong Kong SAR, P.R. China
Clear Water Bay, Kowloon, Hong Kong SAR, P.R. China
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Research Interests: Asset Pricing, Behavioral Finance, Financial Big Data, Sustainable Finance
Working Papers
Working Papers
Retail Ebb and Flow and the Overnight–Intraday Return Gap
with Yongkil Ahn (SeoulTech), Dongwoo Noh (HKUST) and Stella Y. Park (Sogang)
Conditionally Accepted, Journal of Financial and Quantitative Analysis
This version: October, 2026 [SSRN]
Presentations: APAD 2025, FMA 2024, HARC 2024, CAFM 2023, AFBC 2023, AsianFA 2023
[Abstract] In most stock markets, average overnight returns substantially exceed average intraday returns. Using accurate and exhaustive investor-type-level flow data from Korea, we show that retail trading intensity drives this overnight–intraday return gap. We establish causality by instrumenting for retail trading proportion (RTP) with nominal share prices, which affect retail trading through accessibility constraints but carry no fundamental information. We attribute this relationship to the recurrent "retail ebb and flow": driven by sensation-seeking and illusion of control, retail investors seek daytime exposure by systematically net buying at the open and net selling at the close. Consistent with this mechanism, day trading concentrates among young, male investors using mobile and desktop platforms and intensifies following high volatility.
Expected Return in Night and Day: The Role of Trading Volume
with Zhiheng He (Renmin U of China)
Reject and Resubmit, Journal of Financial and Quantitative Analysis
This version: March, 2026 [SSRN]
Presentations: NFA 2025, SoFiE 2025 (poster), AFBC 2024, CICF 2024,CIRF-CFRI 2024
[Abstract] We document a novel high-volume overnight premium and intraday discount. Stocks with high trading volume exhibit remarkable future outperformance (underperformance) during the overnight (intraday) period. This phenomenon is highly persistent and prevalent across global equity markets. We rationalize such contrasting night-day effect using a model with information asymmetry. Driven primarily by the overconfidence of late-informed investors, the cycling belief dispersion peaks at market open and dissipates at close, with trading volume capturing the intensity of this dispersion. This mechanism is supported by high-frequency evidence that real-time idiosyncratic volatility, acting as a proxy for investor disagreement, decays over the intraday period.
[Abstract] We document significant outperformance of stocks whose industry peers currently experience salient (attention-grabbing) gains, especially for those with strong social ties. The long-short strategy based on this effect can earn a risk-adjusted return of 12.5% (t = 5.58) per year. The pricing results differ from well-known cross-stock momentum and can’t be attributed to risk-based explanations. The effect is more pronounced when focal firms are more centered on social networks, or receive less attention, or are faced with greater limits of arbitrage. We argue that such peer-level underpricing is mainly due to the asymmetric effect of attention-induced price pressure from focal firm, as buying decisions of peer firms (due to salient outperformance) is more important than selling when investors has very limited attention.
On the Business Models of ESG Rating Agencies
with Hao Liang (NTU) and Chengshuang Zhang (SMU)
Draft Available Upon Request
Presentations: CICF 2026
This paper evaluates the informational efficiency of the "investor-pays" business model in ESG ratings. We present a rational-expectations model showing that rating agencies substitute genuine methodology enhancement for backward-looking data manipulation when information costs decline. Empirically, using multiple LSEG data vintages, we interpret retroactive score updates as evolutionary refinements of the evaluation framework, rather than pure data manipulation. This is evidenced by the significantly stronger predictive power of updated scores for future returns. Leveraging the announcement of ISSB formation as a regulatory shock, we further show that the stock market positively reacts to a standardized disclosure frameworks. Our findings suggest that the investor-pays model facilitates a more precise alignment between ESG ratings and market-relevant sustainability outcomes.
Bridging Structural and Reduced-Form Estimation of the Stochastic Discount Factor
with Xiaohong Chen (Yale), Yingying Li (HKUST) and Xinghua Zheng (HKUST)
New Draft Coming Soon
Presentations: HKUST Finance Brown Bag, SFS Cavalcade Asia-Pacific 2026 (scheduled)
We develop a framework that bridges structural and reduced-form estimation of the stochastic discount factor (SDF). The approach integrates structural restrictions from macroeconomic models estimated using high-dimensional macro-financial data with a reduced-form SDF constructed from a large set of asset characteristics. The bridge is two-directional: structural restrictions discipline the pricing kernel and recover state-dependent marginal utility, while the reduced-form representation translates the pricing kernel into traded risk exposures and interprets the economic drivers underlying the latent state component. Empirically, the estimated SDF is strongly countercyclical and the structural-guided reduced-form SDF delivers significantly lower out-of-sample pricing errors than relative to structural and reduced-form benchmarks. The optimal portfolios with guided factor selection earn strong out-of-sample returns and are not spanned by leading factor models.
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