Research

Working Papers

Research Awards

• 2025 CUNY Graduate Center Dissertation Year Fellowship $25,000: 1st Prize, one student each year

• 2025 E(urpoean)FA doctoral travel grant

• 2025 AFA doctoral travel grant

Going Supranational: Anomaly-Market Links and New Dimensions of Market Efficiency

with Xi Dong (Baruch College), Yan Li (Southwestern University of Finance and Economics and Baruch Graduate), David Rapach (Federal Reserve Bank of Atlanta), and Guofu Zhou (Washington University in St. Louis)

Presented at: 2025 AI in Finance, 2025 CFRN, 2025 AFA Poster, 2025 MFA, 2025 E(astern)FA, 2025 SWFA, “AI Era in Finance” Symposium 2024, Seminars at Washington University in St Louis, Aarhus University, Baruch College, City University of New York; Singapore Management University; University of Melbourne; Peking University; SWUFE

Abstract: We connect cross-sectional anomalies to time-series market return predictability using data from 44 non-US countries. While a large set of representative anomaly returns show limited predictive power for market returns at the country level, they exhibit strong predictability when aggregated to the supranational level. We develop an international analytical framework to explain this difference: cross-sectional mispricing corrections in one country can propagate into market-wide corrections in another, enhancing supranational predictability precisely when mispricing is more country-specific than global. We further decompose anomaly–market links into three analytically-grounded market (in)efficiency measures of broad relevance: systematic mispricing, overpricing dominance, and price randomness. Supported by data, they govern the strength and nature of anomaly–market links across global markets.

From Zoo to Jungle: Identifying Systemic Anomalies

with Xi Dong (Baruch College), Yan Li (Southwestern University of Finance and Economics (SWUFE) and Baruch Graduate), David Rapach (Federal Reserve Bank of Atlanta), and Guofu Zhou (Washington University in St. Louis (WashU))

Presented at: FWFS-GNY 2025 main session

Abstract: Cross-sectional anomalies are conventionally viewed as market-neutral strategies capturing small pockets of mispricing stranded in isolated market segments. We elevate their importance by developing three economic criteria, augmented by targeted feature engineering, to pinpoint systemically important anomalies — factors whose systematic mispricing propagates through time and across the entire market portfolio. Our criteria emerge from a decomposition framework that isolates three foundational drivers of the anomaly-to-market channel: (i) systematic mispricing, (ii) asymmetric persistence in over- versus underpricing, and (iii) price randomness. Applying this machinery, we uncover divergent sets of systemic anomalies in U.S. and international data. Finally, a Bayesian learning exercise that embeds these anomalies’ systemic nature provides strong evidence that the cross-section does, in fact, help predict the aggregate market.

From Earnings Surprises to Macroeconomic Slowdowns

Abstract: Using data over 1970-2023, I show that the SUE (standardized unexpected earnings) long–short factor negatively predicts macro-outcomes—lower industrial production and consumption and higher unemployment, after controlling for the market excess return. A consumption-based ‘flight-to-safety’ model explains this: investors move away from low-SUE (cash-short) firms ahead of downturns. Using LLM (ChatGPT)-based measures that extract macro-uncertainty signals from earnings-call transcripts, I show predictability strengthens when the long–short uncertainty gap widens.

Working in Progress

Riding the Market on Sound Waves

with Anne Chang (Baruch College) and Xi Dong (Baruch College)