Abstract
Stock index prediction is considered as a difficult task in the past decade. In order to predict stock index accurately, this paper proposes a novel prediction method based on S-system model. Restricted gene expression programming (RGEP) is proposed to encode and optimize the structure of the S-system. A hybrid intelligent algorithm based on brain storm optimization (BSO) and particle swarm optimization (PSO) is proposed to optimize the parameters of the S-system model. Five real stock market prices such as Dow Jones Index, Hang Seng Index, NASDAQ Index, Shanghai Stock Exchange Composite Index, and SZSE Component Index are collected to validate the performance of our proposed method. Experiment results reveal that our method could perform better than deep recurrent neural network (DRNN), flexible neural tree (FNT), radial basis function (RBF), backpropagation (BP) neural network, and ARIMA for 1-week-ahead and 1-month-ahead stock prediction problems. And our proposed hybrid intelligent algorithm has faster convergence than PSO and BSO.
Highlights
Stock market plays a leading and crucial role in the market mechanism, which connects the savers and investors [1, 2].e operating mechanism of the stock market reflects the situation of national economy and is recognized as the signal system of the national economy [3, 4]
As a classical statistical model, the ARIMA model has proposed to predict the New York Stock Exchange (NYSE) and Nigeria Stock Exchange (NSE), and the results revealed that the ARIMA model performed better for short-term prediction [8,9,10]
In order to test the performance of our method clearly, five states of the art methods (Deep Recurrent Neural Network (DRNN) [40], flexible neural tree (FNT) [19], RBFNN [17], BPNN [14], and ARIMA [8]) are used to predict five stock indexes
Summary
Stock market plays a leading and crucial role in the market mechanism, which connects the savers and investors [1, 2].e operating mechanism of the stock market reflects the situation of national economy and is recognized as the signal system of the national economy [3, 4]. Many machine learning (ML) methods containing statistical models, artificial neural networks, and hybrid prediction models have been proposed to model and predict the stock index. As a classical statistical model, the ARIMA model has proposed to predict the New York Stock Exchange (NYSE) and Nigeria Stock Exchange (NSE), and the results revealed that the ARIMA model performed better for short-term prediction [8,9,10]. Adebiyi et al made the comparison of ARIMA and ANN models for stock price prediction and found that the stock forecasting model based on ANN approach had superior performance over ARIMA models [11]. Let stock time series data to be [X1, X2, . E data in the box are utilized as the input vector, and the data on the right side of the box is the prediction value. Two forecasting strategies, 1-week-ahead (m 7) and 1-month-ahead (m 30), are utilized in this paper
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