Predicting the daily return direction of the stock market using hybrid machine learning algorithms

Financial Innovation

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[1] 0000 0000 9364 6281, grid.260128.f, Laboratory for Investment and Financial Engineering, Department of Engineering Management and Systems Engineering, Missouri University of Science and Technology, 221 Engineering Management, 600 W. 14th Street, 65409-0370, Rolla, MO, USA;0000 0004 0486 8069, grid.254277.1, Graduate School of Management, Clark University, 313B Carlson Hall, 950 Main Street, 01610, Worcester, MA, USA;

关键词: Daily stock return forecasting;    Return direction classification;    Data representation;    Hybrid machine learning algorithms;    Deep neural networks (DNNs);    Trading strategies;   

DOI  :  10.1186/s40854-019-0138-0

来源: publisher

【 摘 要 】

Big data analytic techniques associated with machine learning algorithms are playing an increasingly important role in various application fields, including stock market investment. However, few studies have focused on forecasting daily stock market returns, especially when using powerful machine learning techniques, such as deep neural networks (DNNs), to perform the analyses. DNNs employ various deep learning algorithms based on the combination of network structure, activation function, and model parameters, with their performance depending on the format of the data representation. This paper presents a comprehensive big data analytics process to predict the daily return direction of the SPDR S&P 500 ETF (ticker symbol: SPY) based on 60 financial and economic features. DNNs and traditional artificial neural networks (ANNs) are then deployed over the entire preprocessed but untransformed dataset, along with two datasets transformed via principal component analysis (PCA), to predict the daily direction of future stock market index returns. While controlling for overfitting, a pattern for the classification accuracy of the DNNs is detected and demonstrated as the number of the hidden layers increases gradually from 12 to 1000. Moreover, a set of hypothesis testing procedures are implemented on the classification, and the simulation results show that the DNNs using two PCA-represented datasets give significantly higher classification accuracy than those using the entire untransformed dataset, as well as several other hybrid machine learning algorithms. In addition, the trading strategies guided by the DNN classification process based on PCA-represented data perform slightly better than the others tested, including in a comparison against two standard benchmarks.

【 授权许可】

CC BY