Neural networks in financial trading

Sermpinis, G. , Karathanasopoulos, A., Rosillo, R. and de la Fuente, D. (2021) Neural networks in financial trading. Annals of Operations Research, 297(1-2), pp. 293-308. (doi: 10.1007/s10479-019-03144-y)

178881.pdf - Accepted Version



In this study, we generate 50 Multi-layer Perceptons, 50 Radial Basis Functions, 50 Higher Order Neural Networks and 50 Recurrent Neural Network and we explore their utility in forecasting and trading the DJIA, NASDAQ 100 and the NIKKEI 225 stock indices. The statistical significance of the forecasts is examined through the False Discovery Ratio of Bajgrowicz and Scaillet (J Financ Econ 106(3):473–491, 2012). Two financial everages, based on the levels of financial stress and the financial volatility respectively, are also applied. In terms of the results, we note that RNN have the higher percentage of significant models and present the stronger profitability compared to their Neural Network counterparts. The financial leverages doubles the trading performance of our models.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Sermpinis, Professor Georgios
Authors: Sermpinis, G., Karathanasopoulos, A., Rosillo, R., and de la Fuente, D.
College/School:College of Social Sciences > Adam Smith Business School > Economics
Journal Name:Annals of Operations Research
ISSN (Online):1572-9338
Published Online:24 January 2019
Copyright Holders:Copyright © 2019 Springer Science + Business Media B.V., part of Springer Nature
First Published:First published in Annals of Operations Research 297(1-2): 293-308
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher

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