In this study, we propose a new text-mining method for long-term market analysis. Using our method, we performe out-of-sample tests using monthly price data of financial markets; Japanese government bond market, Japanese stock market, and the yen-dollar market. First we extract feature vectors from monthly reports of Bank of Japan. Then, trends of each market are estimated by regression analysis using the feature vectors. As a result of comparison with support vector regression, the proposal method could forecast in higher accuracy about both the level and direction of long-term market trends. Moreover, our method showed high returns with annual rate averages as a result of the implementation test.
2011 JSAI (The Japanese Society for Artificial Intelligence)