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