Application of Deep Learning Architectures in Stock Price Forecasting: A Convolutional Neural Network ‎Approach

Algorithms based on a Convolutional Neural Network (CNN), which is a branch of Deep Learning (DL), have seen significant progress in picture and video analyses in recent years. Success of these new models has led to widespread use of them in various fields, including text mining and time series data. DL is part of a broader family of machine learning methods that attempts to model high-level concepts using learning at multiple levels and layers and extract features of higher levels from the raw input. This survey investigated the abilities of different CNN architectures to predict stock prices. Upon running the model with various architectures and parameters for the stock price of Esfahan Steel Company, the results showed that a CNN with max-pooling layers (a combination of Batch size=64, filters=256, and ReLU Activation Function) and Mean Absolute Percentage Error (MAPE) of 1.79% and Normalized Root Mean Square Error (NRMSE) of 2.71% had a higher prediction accuracy than other CNN architectures and Recurrent Neural Network (RNN).IntroductionAmong the various deep learning techniques that have many applications in different sciences, specific algorithms like Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Convolutional Neural Network (CNN) have been used by researchers due to their characteristics of financial time series (Sezer, Gudelek, & Ozbayoglu, 2020). CNN is a feed-forward Artificial Neural Network (ANN) that takes its inputs as 2-D matrices. Unlike a fully connected neural network like Multi-Layer Perception (MLP) neural network, the locality of data within its input vector (or matrix) is important (Sezer & Ozbayoglu, 2018).CNN has different architectures. Usually one specific architecture is focused on in each study conducted in this field. In this study, however, the architectures used in various studies were surveyed in the first level and each selected architecture was optimized by using different parameters in the second level. Finally, the best performances of the architectures with various parameters were compared to choose the optimized model. The effective studies in model development are shown in Table 1. Table (1) Effective studies in model developmentArt.MethodDatasetFeature SetHorizonLivieris, E. Pintelas, & P. Pintelas (2020)Using two convolutional layers with different filtersGoldPrice data1 dayGao, Zhang, &Yang (2020)Simple CNNS & P500CSI300Nikkei225Price data, volume, technical indicators 1 dayCNN with a drop