Financial time series forecasting using rough sets with time weighted rule voting
We propose a trend prediction model for financial assets with a rough sets approach.For the rough set model an adaptive time weighted rule voting method is proposed.We do experiments with real life stock data, the results are compared with SVM.We show that our model outperforms other models, including baseline buy and hold.Time weighted rule voting stabilizes classification accuracy of the model. In the paper we investigate experimentally the feasibility of rough sets in building profitable trend prediction models for financial time series. In order to improve the decision process for long time series, a novel time weighted rule voting method, which accounts for information aging, is proposed. The experiments have been performed using market data of multiple stock market indices. The classification efficiency and financial performance of the proposed rough sets models was verified and compared with that of support vector machines models and reference financial indices. The results showed that the rough sets approach with time weighted rule voting outperforms the classical rough sets and support vector machines decision systems and is profitable as compared to the buy and hold strategy. In addition, with the use of variable precision rough sets, the effectiveness of generated trading signals was further improved.
Expert Syst. Appl.