Time Series Prediction with LSTM Networks and Its Application to Equity Investment
Time Series Prediction with LSTM Networks and Its Application to Equity Investment
复制标题
LSTM网络的时间序列预测及其在股权投资中的应用
DOI：
10.1007/978-981-15-4498-9_4
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
Makimoto Naoki
中科院分区：
文献类型：
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作者：
Matsumoto Ken;Makimoto Naoki
预测金融时间序列一直是市场分析和投资策略的传统且重要的主题。然而，由于高噪声水平和易失性特征，捕获数据的统计特征并不容易。另一方面，人工智能技术创新在各领域快速推进。特别是，长短期记忆（LSTM）已广泛应用于自然语言处理和语音识别中。在本文中，我们通过将 LSTM 与其他机器学习模型（例如逻辑回归和支持向量机）进行比较来研究 LSTM 的预测性能。首先通过应用它们来预测不同类型的模拟时间序列数据来研究这些模型的特征。然后，我们进行了一项实证研究，以预测 TOPIX Core 30 的股票回报，并将其应用于投资组合选择问题。总体而言，LSTM 显示出比其他方法更好的性能，这与 Fischer 和 Krauss (Eur J Oper Res 270(2):654–669, 2018) 对于 S&P500 数据的结果一致。
Forecasting financial time series has been traditional and important theme for market analysis and investment strategy. However, it is not easy to capture the statistical characteristics of the data due to high noise level and volatile features. On the other hand, technological innovation by artificial intelligence is progressing rapidly in various fields. Especially, long short-term memory (LSTM) has been widely used in natural language processing and speech recognition. In this paper, we study prediction performance of LSTM by comparing it with other machine learning models such as logistics regression and support vector machine. The characteristics of these models were first investigated by applying them to predict different types of simulated time series data. We then conducted an empirical study to predict stock returns in TOPIX Core 30 with application to portfolio selection problem. Overall, LSTM showed favorable performance than other methods, which is consistent with Fischer and Krauss (Eur J Oper Res 270(2):654–669, 2018) for S&P500 data.