Evolving decision rules to predict investment opportunities

Abstract

Abstract-This work 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 cases) in imbalanced environments. EDR provides a range of classifications in order 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. In order to analyse the EDR behaviour, another experiment was carried out using three artificial data sets, whose solutions have different level of complexity. Finally, an illustrative example was provided in order to show how a bigger collection of rules is able to classify more positive cases 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 cases in imbalanced environments.

A Compact Evolutionary Interval-Valued Fuzzy Rule-Based Classification System for the Modeling and Prediction of Real-World Financial Applications With Imbalanced Data

“…Table 1 shows a confusion matrix for a two-class problem. From this table it is possible to extract a number of widely used metrics to measure the performance of learning systems, such as error rate defined in Equation (11) and accuracy defined in Equation (12) The accuracy is the most commonly used metric for empirical evaluations but for classification in this framework this metric might lead to erroneous conclusions since the minority class has little impact on accuracy compared to the majority class [42]. Therefore, in the framework of imbalanced problems there are more accurate metrics.…”

“…• Arbitrage (Arb) Data Set: is used for spotting arbitrage opportunities in the London International Financial Futures Exchange (LIFFE) market [10][11][12]. The data reported in this paper was developed in [12] to identify arbitrage situations by analyzing option and futures prices in the LIFFE market.…”

A Genetic Type-2 fuzzy logic based system for financial applications modelling and prediction

“…In financial applications, we cannot discharge a given input pattern because there are no matching rules in the rule base. A technique to resolve this problem was proposed in [12], [13] and [14], this technique keeps in a rule repository all the rules for the minority class in unbalanced data sets. All the inputs that do not match any rule in the repository are considered belonging to the majority class.…”

“…Various works have been reported using decision trees in financial applications such as [12], [13] and [14].…”

An interval type-2 Fuzzy Logic based system for model generation and summarization of arbitrage opportunities in stock markets

“…The proposed system avoids the drawbacks of the existing type-2 fuzzy classification systems where the proposed system is able to carry prediction based on a relativity small pre-specified rule base size even if the incoming data vector does not match any rules in the FLS rule base. We have compared the proposed type-2 fuzzy logic based system with the Evolving Decision Rule (EDR) procedure [17] which is based on Genetic Programming and decision trees models and which represent some sort of a white box model. We will show through experiments on the LIFFE market how the proposed type-2 FLS has outperformed the EDR procedure while the type-2 FLS is capable of providing a white box model which could be easily understood and analyzed by financial experts.…”