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

arXiv: https://arxiv.org/abs/1805.11954 · HTML: https://mlsanity.com/paper/1805.11954/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 May 2018.

## Built on by (most cited first, 4 of 4)

- [Multi-task Envisioning Transformer-based Autoencoder for Corporate Credit Rating Migration Early Prediction](https://mlsanity.com/paper/2207.04539/) (2022)
- [Forecasting High-Dimensional Covariance Matrices of Asset Returns with Hybrid GARCH-LSTMs](https://mlsanity.com/paper/2109.01044/) (2021)
- [Forex Trading Volatility Prediction using Neural Network Models](https://mlsanity.com/paper/2112.01166/) (2021)
- [Futures Quantitative Investment with Heterogeneous Continual Graph Neural Network](https://mlsanity.com/paper/2303.16532/) (2023)

## Abstract (arXiv)

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.