The Proposed Algorithm to Select Appropriate Features for Predicting Tehran Stock Exchange Index

Document Type : Original Article

Authors

1 Ph.D. Candidate in Financial Management, Shahid Beheshti University, Tehran, Iran.

2 Associate Prof., Department of Financial Management and Insurance, Shahid Beheshti University, Tehran, Iran.

3 Professor, Department of Business Management, Shahid Beheshti University, Tehran, Iran.

Abstract

The performance of an intelligent model largely depends on the selection of the most relevant and most influential input variables and the lowest complexity of the learning model. Therefore, in the present study, to predict the index of Tehran Stock Exchange based on financial and economic variables, first prioritize featuresWith MID algorithm, then 4 different neural network models (MLP, SVR, RBF, DNN) are used, which are the most important and innovative prediction models. According to the results of the analysis of the studied models, an algorithm is proposed to select the appropriate features on the index, as ISF-MID, and are compared with several similar methods. The data used in this study were collected daily in the period of 18/01/2014 to 21/08/2018. Evaluation of the models was performed by K-fold cross validation method. The MAE, MSE, and RMSE criteria are also used to evaluate the performanceof the mentionedmodels. The results show that with the proposed method, with 7 selected features, it is possible to achieve high accuracy in predicting the daily index of the Tehran Stock Exchange.

Keywords

References

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Financial Management Perspective
Volume 11, Issue 34
September 2021
Pages 35-67

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