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.
Highlights
The financial innovation industry has substantially grown up in recent years due to the effort of companies to create and manage dynamic and competitive processes to reinvent their services [1].Financial advising and wealth management companies using artificial intelligence (AI) transformed their business model [2], offering new services to main street investors with an innovative business model known as robo-advisors
The modelling framework is suitable for its application by the robo-advisory industry because of two specific features: 1) the collection of the input variables can be fully automated, and 2) the prediction is five days ahead and permits active data-driven recommendations to rebalance the positions of the portfolio
Acknowledging that the literature has successfully modelled the recent history of copper prices, and to challenge the predictive ability of the proposed framework, the criterion for selecting the training periods was to choose those with the highest volatility
Summary
The financial innovation industry has substantially grown up in recent years due to the effort of companies to create and manage dynamic and competitive processes to reinvent their services [1]. One of the key differences between traditional asset management advisory firms and robo-advisors is that robo-advisors provide added value recommendations in risky portfolios to individual investors by means of AI investment algorithms and the delivery of advice is web-based with little human intervention [4] This innovation permits the reduction of fees and investment thresholds of financial advice, easing access for the companies to the long tail market [5] and providing benefit from the aggregation of many small investors instead of focusing only on the great fortunes. The modelling framework is suitable for its application by the robo-advisory industry because of two specific features: 1) the collection of the input variables can be fully automated, and 2) the prediction is five days ahead and permits active data-driven recommendations to rebalance the positions of the portfolio. The final section concludes with a discussion of the findings, their implications and some limitations
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