This work is motivated by the interest in finding significant movements in financial stock prices. The detection of such movements is important because these could represent good opportunities for invest. However, when the number of profitable opportunities is very small the prediction of these cases is very difficult. In previous works, we have introduced the repository method (RM). The aim of this approach is to classify financial data sets in extreme imbalanced environments. When opportunities are extremely rare, the investor needs a sharper balance between not making mistakes and not missing opportunities. RM offers a range of solutions to suit the risk guidelines of the investor. The aims of this paper are 1) to show that RM can produce a range of solutions to suit the investor's preferences and 2) to analyze the impact of the evolutionary process to RM's performance. Three series of experiments were performed, RM was tested using two artificial data sets whose solutions have different level of complexity. Finally RM was tested in a data set from the London stock market. Experimental results show that: 1) RM offers a range of solutions to fit the risk guidelines of the investor and 2) the contribution of the evolutionary process is very valuable to the performance of RM and 3) RM is able to extract predictive rules even from earliest stages of the evolutionary process.
['Alma Lilia García-Almanza', 'Edward P. K. Tsang']
Repository method to suit different investment strategies