Adaptive Rival Penalized Competitive Learning and Combined Linear Predictor with application to financial investment

Abstract

We have recently proposed an architecture called Rival Penalized Competitive Learning and Combined Linear Predictor (RPCL-CLP) to model financial time series with a certain degree of success (Cheung et al., 1995). Experiments have shown that RPCL-CLP outperforms ClusNet (Hsu et al., 1993), but it still has features which can be further improved. We propose a modified version called Adaptive RPCL-CLP which can automatically select the number of the initial cluster nodes for RPCL (Xu et al., 1993) and adaptively train the linear predictor's parameters in each cluster node as well as the gating network. We apply it to the forecasting of foreign exchange rates and the Shanghai stock price. As shown by experiments, this adaptive version is much better than RPCL-CLP, and with a trading system it can bring in more returns in foreign exchange market trading.