Dissertations, Theses, and Capstone Projects
Date of Degree
6-2026
Document Type
Doctoral Dissertation
Degree Name
Doctor of Philosophy
Program
Business
Advisor
Xi Dong
Committee Members
Lin Peng
Dexin Zhou
Guofu Zhou
Subject Categories
Finance and Financial Management
Abstract
This dissertation consists of five chapters.
Chapter 1: This chapter briefly introduces this dissertation.
Chapter 2: This chapter “Going Supranational: Anomaly-Market Linkages and New Dimensions of Market Efficiency” connects cross-sectional anomalies to time-series market return predictability using data from 44 non-US countries. While a large set of representative anomaly returns shows limited predictability for market returns at the country level, we find they become strikingly strong predictors when aggregated to the supranational level. We rationalize this result with an economically disciplined reduced-form framework: cross-sectional mispricing, appearing country-specific, informs market-wide mispricing in linked countries. Guided by our framework, we develop three supranational market (in)efficiency measures—systemic mispricing, overpricing dominance, and randomness dominance—and show that they govern the properties of anomaly–to-market linkages across supranational groups. Overall, our study introduces a spatiotemporal perspective for next-generation asset pricing models and international diversification.
Chapter 3: This chapter From Earnings Surprises to Macroeconomic Slowdowns shows that the SUE (standardized unexpected earnings) long–short factor negatively predicts macrooutcomes—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, we show predictability strengthens when the long–short uncertainty gap widens.
Chapter 4: This chapter “From Zoo to Jungle: Identifying Systemic Anomalies” digs deeper into the anomaly-market linkage by identifying systemic anomalies. 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.
Chapter 5: This chapter concludes.
Recommended Citation
Li, Yanran, "The Micro Origins of Macro Predictability: Cross-Sectional Anomalies and Time-Series Dynamics" (2026). CUNY Academic Works.
https://academicworks.cuny.edu/gc_etds/6788
