Corporate Fundamentals and Stock Price Co-Movement

TL;DR

This paper presents a novel big data framework for analyzing stock co-movement and its relation to fundamentals, validated on Chinese banking stocks, offering insights for investors and policymakers.

Contribution

It introduces a new multi-model approach to identify leading co-movement stocks and their fundamental drivers using high-frequency data and advanced regression techniques.

Findings

Banks' influence is affected by wealth management and interbank activities.

Fundamental factors like equity multiplier and non-performing loans impact co-movement.

Framework is robust and applicable across sectors and markets.

Abstract

We introduce an innovative framework that leverages advanced big data techniques to analyze dynamic co-movement between stocks and their underlying fundamentals using high-frequency stock market data. Our method identifies leading co-movement stocks through four distinct regression models: Forecast Error Variance Decomposition, transaction volume-normalized FEVD, Granger causality test frequency, and Granger causality test days. Validated using Chinese banking sector stocks, our framework uncovers complex relationships between stock price co-movements and fundamental characteristics, demonstrating its robustness and wide applicability across various sectors and markets. This approach not only enhances our understanding of market dynamics but also provides actionable insights for investors and policymakers, helping to mitigate broader market volatilities and improve financial stability. Our model indicates that banks' influence on their peers is significantly affected by their wealth management business, interbank activities, equity multiplier, non-performing loans, regulatory requirements, and reserve requirement ratios. This aids in mitigating the impact of broader market volatilities and provides deep insights into the unique influence of banks within the financial ecosystem.