The neural network structure consists of a fully connected layer, in which all neurons are combined with adjacent layers. The ANN has a structure in which relations between input and output values are learned through iterative weight adjustments. Typical ANNĪ typical ANN model is a data processing system consisting of layers, connection strengths (weights), a transfer function, and a learning algorithm. Finally, we draw conclusions in Section 5.
Section 4 demonstrates the empirical results and analysis. Section 3 introduces the proposed model for stock market prediction in this study. Section 2 describes the theoretical background for typical ANN, SVM, and CNN. The remainder of this paper is organized as follows. This study compared the forecasting accuracies of the proposed model and the typical ANN model as well as support vector machine (SVM) model to verify the usefulness of deep learning for image recognition in the stock market.
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For applying the CNN, various technical indicators, which are used for technical analysis, have been generated as predictors (input variables) of the prediction model, and these technical indicators were converted to images of the time series graph. Unlike typical neural network structures, the CNN, which is most commonly applied to analyze visual imagery, can improve learning performance by convolution and pooling processes.
In this study, a stock price prediction model based on convolutional neural network (CNN) and technical analysis is proposed to validate the applicability of new learning methods in stock markets. However, since many financial market variables are intertwined with each other directly or indirectly, it is difficult to predict future stock price movements by using technical indicators alone, even when applying a typical deep learning model. proposed a stock price prediction model based on deep learning techniques using open-high-low-close (OHLC) price and volume and derived technical indicators in the Korean stock market. In recent years, many researchers have suggested that artificial neural networks (ANNs) provide an opportunity to achieve profits exceeding the market average by using technical indicators as predictors in stock markets. Due to the limitation of fundamental analysis, many studies related to stock market prediction using technical analysis have been conducted. For example, forecasting timeliness can be reduced, subjectivity can be intervened, and the difference between stock price and intrinsic value can be maintained for a long time. When fundamental analysis is applied, some problems may occur. Fundamental analysis is a method of analyzing all elements that affect the intrinsic value of a company, and technical analysis is a way of predicting future stock price through graph analysis. Stock market prediction methods can be categorized into fundamental analysis and technical analysis. Therefore, the study of stock market prediction has been a very important issue for investors. With the globalization and development of information and communication technology (ICT), however, many people are looking toward stock markets for earning excess returns under a convenient investment environment. There is a high degree of uncertainty in the stock market, which makes it difficult to predict stock price movements. insisted that the stock market can be influenced by complex factors, such as business and economic conditions and political and personal issues. Due to the random walk characteristic, stock market prediction using past information is very challenging. Random walk characteristics in stock markets mean that the stock price moves independently at every point in time. Stock markets have random walk characteristics.
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This study addresses two critical issues regarding the use of CNN for stock price prediction: how to use CNN and how to optimize them.
To examine the performance of the proposed method, an empirical study was performed using the S&P 500 index. From the experimental results, we can see that CNN can be a desirable choice for building stock prediction models. For verifying the usefulness of deep learning for image recognition in stock markets, the predictive accuracies of the proposed model were compared to typical artificial neural network (ANN) model and support vector machine (SVM) model. When applying CNN, 9 technical indicators were chosen as predictors of the forecasting model, and the technical indicators were converted to images of the time series graph. In this paper, we propose a stock price prediction model based on convolutional neural network (CNN) to validate the applicability of new learning methods in stock markets. Stock market prediction is a challenging issue for investors.
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