Unsupervised feature extraction with convolutional autoencoder with application to daily stock market prediction

Abstract

Due to the volatility and noise of the stock market, accurately obtaining the trend of the stock market is a challenging problem, and gets the attention of many researchers and speculators. Recently, convolutional neural network (CNN) has been used to automatically learn effective features and predict stock market trends. In CNN-based methods reported so far, less focus has been paid to time series information of the stock, but is very crucial for stock forecasting. In this study, an unsupervised feature extraction method with convolutional autoencoder (CAE) with application to daily stock market prediction is proposed, which has a higher prediction than traditional models.The proposed method mainly consists of the data processing part, unsupervised feature learning part, and the support vector machine model part. Data processing part includes time series data transform into two-dimensional data and data normalization. CAE network-based unsupervised feature learning is designed by fusing convolution and autoencoder. In order to verify the performance of the model, various initial financial and economic variables of stock indices are chosen for prediction experiments. The experimental results on different stock indices demonstrate a significant improvement in prediction's performance compared with the baseline methods. K E Y W O R D S convolutional autoencoder network, convolutional neural network, stock market prediction, support vector machine INTRODUCTIONThe stock market has become an important component for listed companies to raise funds from investors. However, the law of stock price change is difficult to accurately grasp, and most investors usually depend on subjective judgment to conduct stock trading. 1,2 The stock market has always been affected by political factors, the industry specific factors, the world economic situation, and the price trends are highly nonlinear and nonstationary. [3][4][5] Therefore, effective and efficient predicting the stock is an extremely challenging task for both investors and researchers. And thus, it is significant to research how to build a universal and effective model to forecast future movements of the stock market.There are a number of advanced methods that are applied for prediction of the stock market. Depending on the relationship between the historical behavior and future trend of the stock price is processed, forecasting methods can be divided into three categories. [6][7][8] Among statistical methodologies, due to the interpretability, linear regression, autoregression, and moving average are helpful in financial time series forecasting. Assume that the stock price trend is a result of historical behaviors, extracting effective features from the original stock data is the key of the prediction process. However, features are subjectively designed, and the accurate prediction of models mostly depended on the A preliminary version of this article has been published by the seventh International Conference on Information Science and Control Engineering.

Survey of feature selection and extraction techniques for stock market prediction

“…Bhanja et al ( 2022 ) Technical indicators Autoencoder 5 ML classifiers 2 market indices 30. Xie et al ( 2021 ) Fundamental indica-tors Autoencoder SVM 5 market indices 31. Dami et al ( 2021 ) Basic features Autoencoder LSTM 10 stocks 32.…”

“…Bhanja et al ( 2022 ) Direction of return over 86% Accuracy 30. Xie et al ( 2021 ) Direction prediction 53.3%–57.4% Accuracy 31. Dami et al ( 2021 ) Returns prediction 0.022–0.039 MAE 32.…”

“…Subsequently, we excluded 83 articles that did not mention the feature selection methods applied. Therefore, we obtained 32 relevant papers (27 in journals (Alsubaie et al 2019;Aloraini 2015;Li et al 2022;Kumar et al 2016Nabi et al 2019;Yuan et al 2020;Shen and Shafiq 2020;Haq et al 2021;Sun et al 2019;Hao 2017, 2020;Gunduz et al 2017;Siddique and Panda 2019;Singh and Khushi 2021;Ampomah et al 2020Ampomah et al , 2021Qolipour et al 2021;Das et al 2019;Tang et al 2018;Chong et al 2017;Bhanja and Das 2022;Xie and Yu 2021;Dami and Esterabi 2021;Gunduz 2021;Barak et al 2017;Farahani and Hajiagha 2021), and 5 in conference proceedings (Botunac et al 2020;Cai et al 2012;Labiad et al 2016;Rana et al 2019;Iacomin 2015). Figure 2 illustrates the article selection method.…”

“…The authors demonstrated that the ML classifiers with the CNN-based AE approach achieved over 80% accuracy for the single-step and multi-step ahead predictions of the S&P BSE SENSEX and Nifty 50 stock market index datasets. Xie and Yu ( 2021 ) applied the convolution- based autoencoder (CAE) method to select distinct financial and economic features for the daily direction (up and down) prediction of different stock market indices. They concluded that the average accuracy of the CAE method was approximately 3% higher than that of other methods (i.e., DNN, LSTM, SVM, and PCA) for selected stock indices.…”

A deep learning framework with convolutional long short‐term memory for influenza‐like illness trend estimation

“…With the rapid advancements in intelligent data-driven methods, time series forecasting (TSF) systems have been extensively investigated due to their contribution to decision-making processes in different real-world applications such as environmental forecasting, 1 healthcare science, 2 traffic flow, 3 and financial markets. 4 In general, most researchers have employed the traditional statistical models and the classical machine learning techniques for modeling TSF problems that contain linear or complex nonlinear patterns. The prediction accuracy of these techniques depends entirely on choosing high-quality features that capture complex and nonlinear relationships.…”