School of Finance, Southwestern University of Finance and Economics, Chengdu, China.
School of Statistics, Chengdu University of Information Technology, Chengdu, China.
PLoS One. 2022 Jul 28;17(7):e0271595. doi: 10.1371/journal.pone.0271595. eCollection 2022.
This paper is concerned with the unsolved issue of how to accurately predict the financial market volatility. We propose a novel volatility prediction method for stock index futures prediction based on LSTM, PCA, stock indices and relevant futures. Inspired by the recent advancement of deep learning methodology, six models that combine a variety of artificial intelligence techniques are compared, including ANN, ANN(PCA), ANN(AE), LSTM, LSTM(PCA), and LSTM(AE). That is, in the design and comparison of the proposed AI models, we consider the combination of two dimensionality reduction methods (PCA and AE) and two typical neural networks (ANN and LSTM) in processing time series data. Besides, to further assess the prediction performance of the proposed models, two widely-applied statistical models (i.e. AR and EGARCH) on volatility prediction are used as benchmarks. In the empirical study, we collect financial trading data in both China and the US, and compare the performances of different models in predicting 5 days and 10 days ahead volatilities of stock index futures. In all, our analysis supports the use of LSTM(PCA) model to tackle those irregular and complex datasets.
本文关注的是如何准确预测金融市场波动率这一未解决的问题。我们提出了一种基于 LSTM、PCA、股票指数和相关期货的新型波动率预测方法，用于股票指数期货预测。受深度学习方法最新进展的启发，比较了六种结合多种人工智能技术的模型，包括 ANN、ANN（PCA）、ANN（AE）、LSTM、LSTM（PCA）和 LSTM（AE）。也就是说，在设计和比较所提出的 AI 模型时，我们考虑了在处理时间序列数据时结合两种降维方法（PCA 和 AE）和两种典型的神经网络（ANN 和 LSTM）。此外，为了进一步评估所提出模型的预测性能，我们将两种广泛应用于波动率预测的统计模型（即 AR 和 EGARCH）作为基准。在实证研究中，我们收集了中美两国的金融交易数据，并比较了不同模型在预测股票指数期货未来 5 天和 10 天波动率方面的性能。总的来说，我们的分析支持使用 LSTM（PCA）模型来处理那些不规则和复杂的数据集。
