Artificial intelligence modeling framework for Financial Automated Advising in the Copper Market

Méndez-Suárez, Mariano and Garcia Fernandez, Francisco and Gallardo Moya, Fernando (2019). Artificial intelligence modeling framework for Financial Automated Advising in the Copper Market. "Journal of open Innovation", v. 5 (n. 4); ISSN 2199-8531. https://doi.org/10.3390/joitmc5040081.

Description

Title: Artificial intelligence modeling framework for Financial Automated Advising in the Copper Market
Author/s:
  • Méndez-Suárez, Mariano
  • Garcia Fernandez, Francisco
  • Gallardo Moya, Fernando
Item Type: Article
Título de Revista/Publicación: Journal of open Innovation
Date: October 2019
ISSN: 2199-8531
Volume: 5
Subjects:
Freetext Keywords: robo-advisor; financial innovation; fintech; commodity investment; copper investment; neural networks; artificial intelligence
Faculty: E.T.S.I. Montes, Forestal y del Medio Natural (UPM)
Department: Sistemas y Recursos Naturales
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

Financial innovation by means of Fintech firms is one of the more disruptive business model innovations from the latest years. Specifically, in the financial advisor sector, worldwide assets under management of artificial intelligence (AI)-based investment firms, or robo-advisors, currently amount to US$975.5 B. Since 2008, robo-advisors have evolved from passive advising to active data-driven investment management, requiring AI models capable of predicting financial asset prices on time to switch positions. In this research, an artificial neural network modelling framework is specifically designed to be used as an active data-driven robo-advisor due to its ability to forecast with today's copper prices five days ahead of changes in prices using input data that can be fed automatically in the model. The model, tested using data of the two periods with a higher volatility of the returns of the recent history of copper prices (May 2006 to September 2008 and September 2008 to September 2010) showed that the method is capable of predicting in-sample and out-of-sample prices and consequently changes in prices with high levels of accuracy. Additionally, with a 24-day window of out-of-sample data, a trading simulation exercise was performed, consisting of staying long if the model predicts a rise in price or switching to a short position if the model predicts a decrease in price, and comparing the results with the passive strategies, buy and hold or sell and hold. The results obtained seem promising in terms of both statistical and trading metrics. Our contribution is twofold: 1) we propose a set of input variables based on financial theory that can be collected and fed automatically by the algorithm. 2) We generate predictions five days in advance that can be used to reposition the portfolio in active investment strategies.

More information

Item ID: 67939
DC Identifier: https://oa.upm.es/67939/
OAI Identifier: oai:oa.upm.es:67939
DOI: 10.3390/joitmc5040081
Deposited by: Memoria Investigacion
Deposited on: 02 Aug 2021 10:17
Last Modified: 02 Aug 2021 10:17
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