Exploiting Investors Social Network for Stock Prediction in China's Market

TL;DR

This paper investigates how social media data from Chinese investors, specifically from Xueqiu, can be used to predict stock price movements in China's market by analyzing collective sentiment and perception features with nonlinear models.

Contribution

It introduces a novel approach using social media features from Xueqiu for stock prediction in China, focusing on retail investor influence and behavioral signals.

Findings

Social media features improve stock prediction accuracy.

Collective sentiment features are effective predictors.

Nonlinear models outperform traditional methods.

Abstract

Recent works have shown that social media platforms are able to influence the trends of stock price movements. However, existing works have majorly focused on the U.S. stock market and lacked attention to certain emerging countries such as China, where retail investors dominate the market. In this regard, as retail investors are prone to be influenced by news or other social media, psychological and behavioral features extracted from social media platforms are thought to well predict stock price movements in the China's market. Recent advances in the investor social network in China enables the extraction of such features from web-scale data. In this paper, on the basis of tweets from Xueqiu, a popular Chinese Twitter-like social platform specialized for investors, we analyze features with regard to collective sentiment and perception on stock relatedness and predict stock price movements by employing nonlinear models. The features of interest prove to be effective in our experiments.