Performance Evaluation of Machine Learning Algorithms for Stock Price and Stock Index Movement Prediction Using Trend Deterministic Data Prediction

Munish Khanna, Mohak Kulshrestha, Law K. Singh, Shankar Thawkar, Kapil Shrivastava

Source Title: International Journal of Applied Metaheuristic Computing (IJAMC)13(1)

ISSN: 1947-8283|EISSN: 1947-8291|EISBN13: 9781799885405|DOI: 10.4018/IJAMC.292511

MLA

Khanna, Munish, et al. "Performance Evaluation of Machine Learning Algorithms for Stock Price and Stock Index Movement Prediction Using Trend Deterministic Data Prediction." IJAMC vol.13, no.1 2022: pp.1-30. http://doi.org/10.4018/IJAMC.292511

APA

Khanna, M., Kulshrestha, M., Singh, L. K., Thawkar, S., & Shrivastava, K. (2022). Performance Evaluation of Machine Learning Algorithms for Stock Price and Stock Index Movement Prediction Using Trend Deterministic Data Prediction. International Journal of Applied Metaheuristic Computing (IJAMC), 13(1), 1-30. http://doi.org/10.4018/IJAMC.292511

Chicago

Khanna, Munish, et al. "Performance Evaluation of Machine Learning Algorithms for Stock Price and Stock Index Movement Prediction Using Trend Deterministic Data Prediction," International Journal of Applied Metaheuristic Computing (IJAMC) 13, no.1: 1-30. http://doi.org/10.4018/IJAMC.292511

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Abstract

This experimental study addresses the problem of predicting the direction of stocks and the movement of stock price indices for three major stocks and stock indices. The proposed approach for processing input data involves the computation of ten technical indicators using stock trading data. The dataset used for the evaluation of all the prediction models consists of 11 years of historical data from January 2007 to December 2017. The study comprises four prediction models which are Long Short-Term Memory, XGBoost, Support Vector Machine ( and Random forests. Accuracy scores and F1 scores for each of the prediction models have been evaluated using this input approach. Experimental results reveal that a continuous data approach using ten technical indicators gives the best performance in the case of the Random Forest classifier model with the highest accuracy of 84.89% (average wise 83.74%) and highest F1 score of 89.33% (average wise 83.74%). The experiments also give us an insight into why a Naïve Bayes Classification model is not a suitable prediction model for the above task.