Stability of China's Stock Market: Measure and Forecast by Ricci Curvature on Network

TL;DR

This paper introduces a novel approach using Ricci curvature from network geometry to measure and forecast the systemic stability of China's stock market, demonstrating its effectiveness in capturing market fluctuations and predicting future risks.

Contribution

It is the first to apply Ricci curvature analysis to forecast the stability of a domestic stock market, linking geometric measures with financial stability assessment.

Findings

Ricci curvature effectively captures market stability fluctuations.

The hybrid model accurately predicts future market trends.

Ricci curvature serves as a good indicator for market risk and volatility.

Abstract

The systemic stability of a stock market is one of the core issues in the financial field. The market can be regarded as a complex network whose nodes are stocks connected by edges that signify their correlation strength. Since the market is a strongly nonlinear system, it is difficult to measure the macroscopic stability and depict market fluctuations in time. In this paper, we use a geometric measure derived from discrete Ricci curvature to capture the higher-order nonlinear architecture of financial networks. In order to confirm the effectiveness of our method, we use it to analyze the CSI 300 constituents of China's stock market from 2005--2020 and the systemic stability of the market is quantified through the network's Ricci type curvatures. Furthermore, we use a hybrid model to analyze the curvature time series and predict the future trends of the market accurately. As far as we know, this is the first paper to apply Ricci curvature to forecast the systemic stability of domestic stock market, and our results show that Ricci curvature has good explanatory power for the market stability and can be a good indicator to judge the future risk and volatility of the domestic market.