  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.
