Predicting Stock Market Trends using Machine Learning and Deep Learning Algorithms Via Continuous and Binary Data
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.
Copyright (c) 2024 D Hemamalini, C Nagamani, K Deepesh, P Kamal

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.