Automatic Stock Market Forecasting System Based on Extended Language Template Model

摘要

Time-series forecasting is an important research area in several domains. Recently, natural networks have been very successfully applied in time series to improve multivariate prediction ability. Several neural network models have already been developed for the market prediction. Some are applied to predicting the change of future interest rate and exchange rate; some are applied to recognizing certain price patterns that are characteristic of future price changes. This paper presents a neural network model for technical analysis of stock market, and its application to a buying and selling timing prediction system for stock index of Japan. This paper also describes a natural language generation system using Language Template Model to express prediction information of CSI 300 Index in natural language for non-expert users. This system has evolved to be one of the most comprehensive grammars of English for prediction expressions. The residuals deal with references, appendix, acknowledges, etc.