A Neural Networks Approach to Portfolio Choice

Abstract: This study investigates a neural networks approach to portfolio choice. Linear regression models are extensively used for prediction. With the return as the output variable, one can come to understand its relation to the explanatory variables the linear regression is built upon. However, if the relationship between the output and input variables is non-linear, the linear regression model may not be a suitable choice. An Artificial Neural Network (ANN) is a non-linear statistical model that has been shown to be a “good” approximator of non-linear functions. In this study, two different ANN models are considered, Feed-forward Neural Networks (FNN) and Recurrent Neural Networks (RNN). Networks from these models are trained to predict monthly returns on asset data consisting of macroeconomic data and market data. The predicted returns are then used in a long-short portfolio strategy. The performance of these networks and their corresponding portfolios are then compared to a benchmark linear regression model. Metrics such as average hit-rate, mean squared prediction error, portfolio value and riskadjusted returns are used to evaluate the model performances. The linear regression and the feed-forward model yielded good average hit-rates and mean squared-errors, but poor portfolio performances. The recurrent neural network models yielded worse average hit-rates and mean squared prediction errors, but had outstanding portfolio performances