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)

Abstract

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
• Status: Published
• Refereed: Yes
• 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
• Publisher: Springer
• ISSN: 0254-5330
• 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