Stock Market Modeling Using Artificial Neural Network and Comparison with Classical Linear Models

Stock market plays an important role in the world economy. Stock market customers are interested in predicting the stock market general index price, since their income depends on this financial factor; Therefore, a reliable forecast in stock market can be extremely profitable for stockholders. Stock market prediction for financial markets has been one of the main challenges in forecasting financial time series, in recent decades. This challenge has increasingly attracted researchers from different scientific branches such as computer science, statistics, mathematics, and etc. Despite a good deal of research in this area, the achieved success is far from ideal. Due to the intrinsic complexity of financial data in stock market, designing a practical model for this prediction is a difficult task. This difficulty increases when a wide variety of financial factors affect the stock market index. In this paper, we attempt to investigate this problem and propose an effective model to solve this challenge. Tehran’s stock market has been chosen as a real-world case study for this purpose. Concretely, we train a regression model by several features such as first and second market index in the last five years, as well as other influential features including US dollar price, universal gold price, petroleum price, industry index and floating currency index. Then, we use the trained system to predict the stock market index value of the following day. The proposed approach can be used by stockbrokers-trading companies that buy and sell shares for their clients to predict the stock market value. In the proposed method, intelligent nonlinear systems such as Artificial Neural Networks (ANNs) and Adaptive Network-based Fuzzy Inference System (ANFIS) have been exploited to predict the daily stock  market value of Tehran’s stock market. At the end, the performance of these models have been measured and compared with the linear classical models, namely, ARIMA and SARIMA. In the comparison phase, these time series data are imposed to non-linear ANN and ANFIS models; then, feature selection is applied on data to extract the more influencing features, by using mutual information (MI) and correlation coefficient (CC) criteria. As a result, those features with greater impact on prediction are selected to predict the stock market value. This task eliminates irrelevant data and minimizes the error rate. Finally, all models are compared with each other based on common evaluation criteria to provide a big picture of the exploited models. The obtained results approve that the feature selection by MI and CC methods in both ANFIS and ANN models increases the accuracy of stock market prediction up to 55 percentage points. Furthermore, ANFIS could outperform ANN in all five evaluation criteria.