Forecasting stock price movement: new evidence from a novel hybrid deep learning model

Yang Zhao and Zhonglu Chen

Journal of Asian Business and Economic Studies, 2021, vol. 29, issue 2, 91-104

Abstract: Purpose - This study explores whether a new machine learning method can more accurately predict the movement of stock prices. Design/methodology/approach - This study presents a novel hybrid deep learning model, Residual-CNN-Seq2Seq (RCSNet), to predict the trend of stock price movement. RCSNet integrates the autoregressive integrated moving average (ARIMA) model, convolutional neural network (CNN) and the sequence-to-sequence (Seq2Seq) long–short-term memory (LSTM) model. Findings - The hybrid model is able to forecast both linear and non-linear time-series component of stock dataset. CNN and Seq2Seq LSTMs can be effectively combined for dynamic modeling of short- and long-term-dependent patterns in non-linear time series forecast. Experimental results show that the proposed model outperforms baseline models on S&P 500 index stock dataset from January 2000 to August 2016. Originality/value - This study develops the RCSNet hybrid model to tackle the challenge by combining both linear and non-linear models. New evidence has been obtained in predicting the movement of stock market prices.

Keywords: Stock price movement; RCSNet; ARIMA; CNN; LSTM; S&P 500 index; C52; G11; G12 (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:eme:jabesp:jabes-05-2021-0061

DOI: 10.1108/JABES-05-2021-0061

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