In recent years, a number of researches are conducted to analyze and forecast stock values in the area of artificial intelligence. The most researches use monthly news and focus on the stock value fluctuation forecast on a monthly basis, but financial traders usually require the daily forecast of stock market prices. To forecast daily change of stock market, sector news should be taken into consideration because we think that they impact on stock market. The primary objective of our research is to investigate whether or not next day's stock values can be forecasted using text mining of the daily sector news. In our method, sentences in the sector news are resolved into the morphemes by morpheme analysis and the co-occurrence frequency are counted. Then, the derived frequency data are transformed to principal components. Finally, the relationships between stock value fluctuations and the principal components in the multiple linear regression are examined. To clarify the effectiveness of our method, we compare the forecating obtained by the daily sector news with those done by daily whole news. The experimental results shows that the accuracy of the forecasts with daily sector news is higher than that with daily whole news in the period of the big fluctuation in the sector.
