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Open AccessArticle

Artificial Intelligence Modelling Framework for Financial Automated Advising in the Copper Market

1
Department of Market Research and Quantitative Methods, ESIC Business & Marketing School, 28223 Madrid, Spain
2
Department of System and Natural Resources, Technical University of Madrid, 28040 Madrid, Spain
3
Department of Finance and Market Research, Autonomous University of Madrid, 28049 Madrid, Spain
*
Author to whom correspondence should be addressed.
J. Open Innov. Technol. Mark. Complex. 2019, 5(4), 81; https://doi.org/10.3390/joitmc5040081
Received: 31 July 2019 / Revised: 23 September 2019 / Accepted: 6 October 2019 / Published: 9 October 2019
(This article belongs to the Special Issue Business Model Innovation)
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. View Full-Text
Keywords: robo-advisor; financial innovation; fintech; commodity investment; copper investment; neural networks; artificial intelligence robo-advisor; financial innovation; fintech; commodity investment; copper investment; neural networks; artificial intelligence
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Méndez-Suárez, M.; García-Fernández, F.; Gallardo, F. Artificial Intelligence Modelling Framework for Financial Automated Advising in the Copper Market. J. Open Innov. Technol. Mark. Complex. 2019, 5, 81.

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