Equity pricing in China: firm connections, style investment, and disagreement spillover

Abstract

The thesis encompasses three essays in empirical asset pricing and focuses on the role of firm linkage and belief dispersion in the Chinese stock market. Specifically, we explore the role of economic linkages in return predictability (Chapter 2), the impact of style-based disagreement on asset pricing (Chapter 3), and the spillover effects of investor disagreement across economically connected firms (Chapter 4). These three chapters present new empirical findings and offer insights that can be useful for investors, policymakers, and market regulators.

In Chapter 2, Firm connection and equity return predictability – Graph-based machine learning methods, we develop a unified framework that aggregates popular economic linkage indicators into a collective graph-encoded linkage via graph-based machine learning methods. Using data from 2003 to 2022 in the Chinese equity market, we find that our graph-encoded linkage measure exhibits significant predictive power for future stock returns and significantly outperforms traditional individual linkage measures. Our empirical analyses reveal a pronounced momentum spillover effect across economically connected firms. We show that our measure contains incremental information on firm fundamental connections relative to well-documented alternative measures, and its predictive ability can be explained by the investor inattention hypothesis. This chapter contributes to the literature exploring economic linkages that generate lead-lag predictability and sheds new light on the interconnected nature of companies in an economy via advanced machine learning techniques.

In Chapter 3, Style investment, disagreement, and expected stock returns, we investigate how heterogeneity in retail investors' investment styles shapes disagreement and its asset pricing implications. Empirically, we construct a novel style disagreement measure by quantifying opinion divergence across distinct retail investor styles based on the Internet stock message board postings with machine learning methods, which allows us to better capture cross-style heterogeneity in beliefs. Using data from 2012 to 2023 in the Chinese equity market, we show that higher style disagreement generally predicts lower subsequent returns and higher trading volume, and these effects are strongly style dependent. Disagreement among technical and growth investors predicts significantly negative subsequent returns, whereas disagreement among fundamental investors predicts positive returns. The negative relation between style disagreement and stock returns can be rationalized by a mispricing channel. This chapter speaks to the literature of style investment and sheds new light on the pricing of stocks in a market populated by retail investors.

In Chapter 4, Disagreement spillover across linked firms, we examine whether and how investor disagreement spills over across fundamentally linked firms. Using data from 2006 to 2024 in the Chinese equity market, we document a robust spillover effect where a firm's belief dispersion is positively predicted by the prior disagreement of its linked peers. Further analysis shows that spillovers are amplified by greater ownership overlap, greater connectedness in the co-holding investor network, and heightened attention around peers’ earnings announcements. In addition, we find that the spillover-driven component of disagreement is economically meaningful, as it forecasts higher future stock price crash risk and lower informational efficiency. These findings advance our understanding of the sources driving disagreement and provide new insights into how belief dispersion propagates through economic networks.

Date of Award	19 Jul 2026
Original language	English
Awarding Institution	University of Nottingham
Supervisor	Qingxin Meng (Supervisor), Xiaoquan Liu (Supervisor) & Wenli Huang (Supervisor)