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Author: Ivan Letteri

Affiliation: Department of Life, Health and Environmental Sciences, University of L’Aquila, Italy

Keyword(s): Machine Learning, Statistical Analysis, Algorithmic Trading, K-Means++, Granger Causality Test.

Abstract: The purpose of our research was to explore volatility-based trading strategies in financial markets to leverage market dynamics for capital gain. We sought to introduce a strategy that integrated statistical analysis with machine learning to predict stock market trends. Our method involved using the k-means++ clustering algorithm to examine the mean volatility of the nine largest stocks in both the NYSE and Nasdaq markets. The clusters formed the basis for understanding relationships among stocks based on their volatility patterns. We further subjected the mid-volatility clustered dataset to the Granger Causality Test, which helped identify stocks with strong predictive connections. These stocks were crucial in formulating our trading strategy, serving as trend indicators for decisions on target stock trades. Our empirical approach included thorough backtesting and performance analysis. Our findings demonstrated the effectiveness of our method in exploiting profitable trading opportunities. This was achieved through predictive insights derived from volatility clusters and Granger causality relationships among stocks. In conclusion, our research contributed to the field of volatility-based trading strategies by offering a methodology that combined a statistical approach with machine learning. This enhanced the predictability of stock market trends. (More)

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Paper citation in several formats:
Letteri, I. (2024). Stock Market Forecasting Using Machine Learning Models Through Volatility-Driven Trading Strategies. In Proceedings of the 6th International Conference on Finance, Economics, Management and IT Business - FEMIB; ISBN 978-989-758-695-8; ISSN 2184-5891, SciTePress, pages 96-103. DOI: 10.5220/0012607200003717

@conference{femib24,
author={Ivan Letteri},
title={Stock Market Forecasting Using Machine Learning Models Through Volatility-Driven Trading Strategies},
booktitle={Proceedings of the 6th International Conference on Finance, Economics, Management and IT Business - FEMIB},
year={2024},
pages={96-103},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012607200003717},
isbn={978-989-758-695-8},
issn={2184-5891},
}

TY - CONF

JO - Proceedings of the 6th International Conference on Finance, Economics, Management and IT Business - FEMIB
TI - Stock Market Forecasting Using Machine Learning Models Through Volatility-Driven Trading Strategies
SN - 978-989-758-695-8
IS - 2184-5891
AU - Letteri, I.
PY - 2024
SP - 96
EP - 103
DO - 10.5220/0012607200003717
PB - SciTePress