Predicting Stock Market Trends using Machine Learning and Deep Learning Algorithms Via Continuous and Binary Data

D Hemamalini; C Nagamani; K Deepesh; P Kamal

doi:10.34293/sijash.v11iS3-July.7915

D Hemamalini Assistant Professor, Department of AI & DS, Arjun College Technology
C Nagamani Department of AI & DS, Arjun College 0f Technology
K Deepesh Department of AI & DS, Arjun College of Technology
P Kamal Department of AI & DS, Arjun College of Technology

DOI: https://doi.org/10.34293/sijash.v11iS3-July.7915

Keywords: Machine Learning, Stock Market Prediction, Literature Review, Deep Learning, Support Vector Machine, KNN, LSTM, ANN, Investment Decision

Abstract

This investigation aimed to utilize machine learning algorithms for predicting stock market movements in Iran. The study centered on three specific sectors - diversified finance, information technology (IT), and metals - within the Tehran Stock Exchange. Ten years of historical data were analyzed . Incorporating ten technical indicators. To achieve this goal, six machine learning models were deployed. Support Vector Regression (Linear)Support Vector Regression (RBF)Linear Regression, Random -Forests ,K-Nearest Neighbours (KNN) Decision Trees.