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This work presents a novel approach to prediction of financial asset prices. Its main contribution is the combination of compensatory fuzzy logic and the classical technical analysis to build an efficient prediction model. The interpretability properties of the model allow its users to incorporate and consider virtually any set of rules from technical analysis, in addition to the investors’ knowledge related to the actual market conditions. This knowledge can be incorporated into the model in the form of subjective assessments made by investors. Such assessments can be obtained, for example, from the graphical analysis commonly performed by traders. The effectiveness of the model was assessed through its systematic application in the stock and cryptocurrency markets. From the results, we conclude that when the model shows a high degree of recommendation, the actual financial assets show high effectiveness.

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Stock portfolio management consists of defining how some investment resources should be allocated to a set of stocks. It is an important component in the functioning of modern societies throughout the world. However, it faces important theoretical and practical challenges. The contribution of this work is two-fold: first, to describe an approach that comprehensively addresses the main activities carried out by practitioners during portfolio management (price forecasting, stock selection and portfolio optimization) and, second, to consider uptrends and downtrends in prices. Both aspects are relevant for practitioners but, to the best of our knowledge, the literature does not have an approach addressing them together. We propose to do it by exploiting various computational intelligence techniques. The assessment of the proposal shows that further improvements to the procedure are obtained when considering downtrends and that the procedure allows obtaining portfolios with better returns than those produced by the considered benchmarks. These results indicate that practitioners should consider the proposed procedure as a complement to their current methodologies in managing stock portfolios.