This paper is motivated by the interest in finding significant movements in financial stock prices. However, when the number of profitable opportunities is scarce, the prediction of these cases is difficult. In a prev...This paper is motivated by the interest in finding significant movements in financial stock prices. However, when the number of profitable opportunities is scarce, the prediction of these cases is difficult. In a previous work, we have introduced evolving decision rules （EDR） to detect financial opportunities. The objective of EDR is to classify the minority class （positive eases） in imbalaneed environments. EDR provides a range of classifications to find the best balance between not making mistakes and not missing opportunities. The goals of this paper are： 1） to show that EDR produces a range of solutions to suit the investor＇s preferences and 2） to analyze the factors that benefit the performance of EDR. A series of experiments was performed. EDR was tested using a data set from the London Financial Market. To analyze the EDR behaviour, another experiment was carried out using three artificial data sets, whose solutions have different levels of complexity. Finally, an illustrative example was provided to show how a bigger collection of rules is able to classify more positive eases in imbalanced data sets. Experimental results show that： 1） EDR offers a range of solutions to fit the risk guidelines of different types of investors, and 2） a bigger collection of rules is able to classify more positive eases in imbalanced environments.展开更多
文摘This paper is motivated by the interest in finding significant movements in financial stock prices. However, when the number of profitable opportunities is scarce, the prediction of these cases is difficult. In a previous work, we have introduced evolving decision rules （EDR） to detect financial opportunities. The objective of EDR is to classify the minority class （positive eases） in imbalaneed environments. EDR provides a range of classifications to find the best balance between not making mistakes and not missing opportunities. The goals of this paper are： 1） to show that EDR produces a range of solutions to suit the investor＇s preferences and 2） to analyze the factors that benefit the performance of EDR. A series of experiments was performed. EDR was tested using a data set from the London Financial Market. To analyze the EDR behaviour, another experiment was carried out using three artificial data sets, whose solutions have different levels of complexity. Finally, an illustrative example was provided to show how a bigger collection of rules is able to classify more positive eases in imbalanced data sets. Experimental results show that： 1） EDR offers a range of solutions to fit the risk guidelines of different types of investors, and 2） a bigger collection of rules is able to classify more positive eases in imbalanced environments.
