Carteiras de Black Litterman com Análises Baseadas em Redes Neurais

Abstract

This paper presents an autonomous portfolio management system. Autonomous investment systems consist of a series of buy and sell rules on financial markets, which can be executed by machines, oriented to maximizing investor gains. The system uses a Neural Network approach for monitoring the market and the Black-Litterman model for portfolio composition. The ten most traded assets from the Bovespa Index are analyzed, with dedicated neural networks, which suggests future return estimates using technical indicators as input. Those estimates are inserted in the Black-Litterman model which proposes daily portfolio composition using long and short positions. The results are compared to a second autonomous trading system without the Black-Litterman approach, referred to as Benchmark. The numerical results show a great performance compared to the Benchmark, especially the risk-return ratio, captured by the Sharpe Index. Such results suggest that the use of Bayesian inference models combined with neural networks may be a good alternative in portfolio management.