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A Comparative Study of SVM and LSTM Deep Learning Algorithms for Stock Market Prediction

The paper presents a comparative study of the performance of Long Short-Term Memory (LSTM) neural network models with Support Vector Machine (SVM) regression models. The framework built as a part of this study comprises of eight models. In this, 4 models are built using LSTM and 4 models using SVM respectively. Two major datasets are used for this paper. One is the base standard Dow Jones Index (DJI) stock price dataset and another is the combination of this stock price dataset along with external added input parameters of crude oil and gold prices. This comparative study shows the best model in combination with our input dataset. The performance of the models is measured in terms of their Root Mean Squared Error (RMSE), Mean Squared Error (MSE), Mean Absolute Error, Mean Absolute Percentage Error (MAPE) and R squared (R2) score values. The methodologies and the results of the models are discussed and possible enhancements to this work are also provided.