An experiment with ANNs and Long-Tail Probability Ranking to Obtain Portfolios with Superior Returns

In an experimental study, we investigated the application of artificial neural networks (ANNs) and long-tail probability ranking in constructing investment portfolios to achieve superior returns compared to a benchmark. Our objective is to demonstrate that portfolio formation can be conceptualized as a classification problem by leveraging the inherent capabilities of ANNs to capture complex relationships and facilitate more informed decisions regarding portfolio composition. We conducted the experiment using lagged asset return information to predict stock returns, employing a pilot sample of 70 assets and a validation sample consisting of all companies belonging to the Standard & Poor's 500 (S&P 500) index. The study covers the period from 2018 to 2022, with 585,650 daily observations of active assets. The results indicate that the classification method proposed in this study, using the asymmetric probabilities of the Student´s $$t$$ t distribution, outperforms the market and traditional portfolios. Furthermore, the results suggest that the combined approach of ANN and security classification based on their asymmetric leptokurtic probabilities demonstrates superiority over portfolios that rely solely on security signal classification.