Neuro-dynamic trading methods

Investment strategies are usually based on forecasting models, and these are optimized with respect to past predictive performance. However, the main goal of most investors is the optimization of a risk-adjusted performance measure, such as the well-known Sharpe index. This issue has been approached by a few different studies within the area of Neurocomputing. The present paper briefly describes and empirically compares some of the models and methods proposed in those studies. Such adaptive methods can be computationally demanding, and convergence to high-quality solutions can be difficult to achieve, yet they can be very useful in automated trading systems, namely for portfolio management. In particular, the Q-learning algorithm, when combined with neural networks for value function approximation, seems to be a reasonably competitive approach, although not overall superior to alternative ones.