STOCK PRICE PREDICTION USING MACHINE LEARNING

Registration ID: IJNRD_218469

Published ID: IJNRD2404459

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MOHAMMADAZHARSAQUIB, DR.B.RAGHU

ABSTRACT: The nature of stock market movement has always been ambiguous for investors because of various influential factors. This study aims to significantly reduce the risk of trend prediction with machine learning and deep learning algorithms. Four stock market groups, namely diversified financials, petroleum, non-metallic minerals and basic metals from Tehran stock exchange, are chosen for experimental evaluations. This study compares nine machine learning models (Decision Tree, Random Forest, Adaptive Boosting (Adaboost), eXtreme Gradient Boosting (XGBoost), Support Vector Classifier (SVC), Naïve Bayes, K- Nearest Neighbors (KNN), Logistic Regression and Artificial Neural Network (ANN)) and two powerful deep learning methods (Recurrent Neural Network (RNN) and Long short-term memory (LSTM). Ten technical indicators from ten years of historical data are our input values, and two ways are supposed for employing them. Firstly, calculating the indicators by stock trading values as continues data, and secondly converting indicators to binary data before using. Each prediction model is evaluated by three metrics based on the input ways. The evaluation results indicate that for the continues data, RNN and LSTM outperform other prediction models with a considerable difference. Also, results show that in the binary data evaluation, those deep learning methods are the best; however, the difference becomes less because of the noticeable improvement of models’ performance in the second way.

Published Paper   E-Certificate

MOHAMMADAZHARSAQUIB & DR.B.RAGHU (April-2024). STOCK PRICE PREDICTION USING MACHINE LEARNING. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(4), e553-e559. https://ijnrd.org/papers/IJNRD2404459.pdf

Volume 9 Issue 4, April-2024

Pages : e553-e559

Paper Reg. ID: IJNRD_218469

Published Paper Id: IJNRD2404459

Downloads: 000122253

Research Area: Computer Science & Technology 

Author Type: Indian Author

Country: WARANGAL, TELANGANA, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2404459.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2404459

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

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