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.
Abstract (translated)
金融市场剧烈波动影响全球人类。因此，相对准确的波动预测至关重要。我们认为，人为与互联网互动所产生的海量数据来源可能会为大市场走势时期市场参与者的行为提供新的视角。首先我们选取28个与金融相关的关键词作为公众情绪和宏观经济因素的指标。然后，从2006年6月1日至2017年10月29日，手动收集基于百度指数的每日搜索量的28个单词。使用这些搜索量数据应用长期短期记忆神经网络来预测CSI300波动率。与基准GARCH模型相比，我们的预测更准确，这证明了LSTM神经网络在波动率预测中的有效性。
URL
https://arxiv.org/abs/1805.11954
