Long Short-Term Memory Networks for CSI300 Volatility Prediction with Baidu Search Volume

TL;DR

This paper demonstrates that LSTM neural networks utilizing Baidu search volume data can more accurately predict CSI300 market volatility than traditional models, highlighting the value of internet-based indicators in financial forecasting.

Contribution

It introduces a novel approach combining Baidu search data with LSTM networks for stock market volatility prediction, outperforming traditional GARCH models.

Findings

LSTM models outperform GARCH in volatility prediction accuracy.

Search volume data from Baidu improves forecasting performance.

Internet-based indicators are valuable for financial market analysis.

Abstract

Intense volatility in financial markets affect humans worldwide. Therefore, relatively accurate prediction of volatility is critical. We suggest that massive data sources resulting from human interaction with the Internet may offer a new perspective on the behavior of market participants in periods of large market movements. First we select 28 key words, which are related to finance as indicators of the public mood and macroeconomic factors. Then those 28 words of the daily search volume based on Baidu index are collected manually, from June 1, 2006 to October 29, 2017. We apply a Long Short-Term Memory neural network to forecast CSI300 volatility using those search volume data. Compared to the benchmark GARCH model, our forecast is more accurate, which demonstrates the effectiveness of the LSTM neural network in volatility forecasting.