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Authors: Ivan Letteri ; Giuseppe Della Penna ; Giovanni De Gasperis and Abeer Dyoub

Affiliation: Department of Information Engineering, Computer Science and Mathematics, University of L’Aquila, via Vetoio, Coppito, L’Aquila, Italy

Keyword(s): Neural Networks, Machine Learning, Stock Trading, Stock Market Prediction, Quantitative Finance, Algorithmic Trading, Technical Analysis.

Abstract: Traders commonly test their trading strategies by applying them on the historical market data (backtesting), and then reuse on their (future) trades the strategy that achieved the maximum profit on such past data. In this paper we propose a novel technique, that we shall call forwardtesting, that determines the strategy to apply by testing it on the possible future predicted by a deep neural network that has been designed to perform stock price forecasts and trained with the market historical data. Our results confirm that neural networks outperform classical statistical techniques when performing such forecasts, and their predictions allow to select a trading strategy that, when applied to the real future, results equally or more profitable than the strategy that would be selected through the traditional backtesting.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Letteri, I.; Della Penna, G.; De Gasperis, G. and Dyoub, A. (2023). Trading Strategy Validation Using Forwardtesting with Deep Neural Networks. In Proceedings of the 5th International Conference on Finance, Economics, Management and IT Business - FEMIB; ISBN 978-989-758-646-0; ISSN 2184-5891, SciTePress, pages 15-25. DOI: 10.5220/0011715300003494

@conference{femib23,
author={Ivan Letteri. and Giuseppe {Della Penna}. and Giovanni {De Gasperis}. and Abeer Dyoub.},
title={Trading Strategy Validation Using Forwardtesting with Deep Neural Networks},
booktitle={Proceedings of the 5th International Conference on Finance, Economics, Management and IT Business - FEMIB},
year={2023},
pages={15-25},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011715300003494},
isbn={978-989-758-646-0},
issn={2184-5891},
}

TY - CONF

JO - Proceedings of the 5th International Conference on Finance, Economics, Management and IT Business - FEMIB
TI - Trading Strategy Validation Using Forwardtesting with Deep Neural Networks
SN - 978-989-758-646-0
IS - 2184-5891
AU - Letteri, I.
AU - Della Penna, G.
AU - De Gasperis, G.
AU - Dyoub, A.
PY - 2023
SP - 15
EP - 25
DO - 10.5220/0011715300003494
PB - SciTePress