Further improvement of adaptive supervised learning decision (ASLD) network in stock market

We apply a neural network model called adaptive supervised learning decision network (ASLD), proposed by Xu and Cheung (1997), that maximize the expected return. In generating the trading signals for training the neural network used in the ASLD system, besides maximizing the profit gain, we have also applied the portfolio technique related to Sharpe ratio (1994) which consider expected risk in addition. Instead of making use of the original design of the Sharpe ratio maximization, we have replaced the traditional risk with a more sophisticated quantity called “downside risk” proposed by Sortino and van der Meer (1991) and “upside volatility” we proposed. Moreover, a regularization idea is introduced to make the portfolio distributed more evenly over the indexes. Lastly, using the augmented Lagrangian method, we have developed system that can either control the expected return and minimize the downside risk, or control the downside risk and maximize the expected return