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

Abstract

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.

Portfolio selection: a fuzzy-ANP approach

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Extreme learning with chemical reaction optimization for stock volatility prediction

“…Where H + is the pseudo inverse or Moore-Penrose inverse of H. Prospective readers may refer to (Zhong & Enke, 2019;Zhang et al, 2019) for more details on ELM.…”

“…The most widely used ANNs for financial time series prediction include multilayer perceptron (Wang et al, 2012;Xi et al, 2014), radial basis functional network (Shen et al, 2011), and higher order neural networks such as functional link artificial neural network (Majhi et al, 2009;Nayak et al, 2012) and Pi-Sigma neural networks (Nayak et al, 2016). Stock market return forecasting is demonstrated by Zhong and Enke (Zhong & Enke, 2017;Zhong & Enke, 2019). To analyze the relationship between minimum cost and maximum return, a generalized soft cost consensus model under a certain degree of consensus is proposed by Zhang et al (Zhang et al, 2019).…”

Machine learning forecasting of USA and PRC balance of trade in context of mutual sanctions

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