Stock Market Forecasting with Machine Learning

P. Jacob Vijaya Kumar, Shaik Fayaz, Moghal Rasool Baig, Shaik Mohammad Ershad, Vadla Uday Kiran

2025

Abstract

Stock prices shift each day. People look for ways to predict where they might move next. Computers learn from past trends and patterns to make smart guesses about future changes. Some methods focus on recognizing trends. XGBoost, Random Forest, and Support Vector Regression study past stock behavior to predict upcoming movements. Others focus on time-based patterns. LSTM and GRU observe how prices change over time, adapting as they learn. Accuracy matters. Randomized Search CV helps adjust machine learning models for better results. Bayesian optimization refines deep learning models, improving their performance step by step. No single approach is enough. Machine learning and deep learning predictions are blended together, reducing errors and increasing reliability. Users need simple access. A web tool built with Streamlit presents forecasts in a clear way. Data comes from Yahoo Finance will ensure up-to-date stock information is used. By combining these methods stock predictions become sharper. This approach offers a better way to understand future market trends.

Paper Citation

in Harvard Style

Kumar P., Fayaz S., Baig M., Ershad S. and Kiran V. (2025). Stock Market Forecasting with Machine Learning. In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25; ISBN 978-989-758-777-1, SciTePress, pages 697-703. DOI: 10.5220/0013942400004919

in Bibtex Style

@conference{icrdicct`2525,
author={P. Kumar and Shaik Fayaz and Moghal Baig and Shaik Ershad and Vadla Kiran},
title={Stock Market Forecasting with Machine Learning},
booktitle={Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25},
year={2025},
pages={697-703},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013942400004919},
isbn={978-989-758-777-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25
TI - Stock Market Forecasting with Machine Learning
SN - 978-989-758-777-1
AU - Kumar P.
AU - Fayaz S.
AU - Baig M.
AU - Ershad S.
AU - Kiran V.
PY - 2025
SP - 697
EP - 703
DO - 10.5220/0013942400004919
PB - SciTePress