In this paper, we propose a real world application which is an automated foreign exchange trading system using Differential Evolution(DE). There are many studies which have focused on trading system. In recent years, such studies have attracted attention because system can catch movement of market price accurately and quickly. DE is a simple yet powerful evolutionary algorithm for global numerical optimization. To verify the effectiveness of the method, we performed simulations using real historical trading data. DE was found to be superior compared to other previous methods in terms of precision and reliability.
In this research, we propose a method of construction of trading rules based on evolutionary computation. In our proposed method, trading rules are expressed by binary trees(so-called strategy trees). They include both technical indicators and their parameters. We tried to optimize the strategy trees using Genetic Programming. Then, we simulated using real foreign exchange market data. As a result, it showed high win probability.
In financial markets, sudden unexpected changes occur frequently. We propose a new forecast method based on paired evaluators consisting of the stable evaluator and the reactive evaluator that is good at detecting and adapting to the consecutive market changes. We conduct a back-testing using financial data in US. The experimental results show that our method is effective and robust even against late-2000s recessions.
In this study, we proposed a new text-mining method for stock price indexes using newspaper articles. Using this method, we conducted extrapolation tests to evaluate the prediction accuracy for the year 2009. As a result, 11 sectors in 19 sectors (57.8 percent) showed over 52% accuracy. The prediction accuracy showed seasonality in some sectors. This is expected to be a measure of prediction confidence of text mining.
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.
In this study, we applied the newly developed text-mining methods to English texts for the long-term market analyses. We analyzed monthly price data of foreign financial markets, in particular, the interest swap markets. Several extensions of the original method were suggested in order to extract English feature vectors from minutes of the monetary policy committee of The Bank of England. Trends of interest rates were estimated by using the regression analysis with the feature vectors. As a result, determination coefficients were found around 75%, and market trends were explained well. Using the predicted interest rates, we also simulated several implementation tests, which demonstrate the effectiveness of our extensions of the original method to English texts.
This article develops a methodology to compare market performances under different trading mechanisms for equities implemented in TokyoStock Exchange(TSE): countinuous double auction and call market. Our results show that patience on time-to-execution of traders has a significant impact on market performances. Hence fast moving equities tend to shrink their liquidity under call market having a long intervel on executions.