Abstract
The global economic meltdown of the late 2000s exposed many organisations around the world, this drove the need to build robust frameworks for predicting and assessing risks in financial applications. Such predictive frameworks helped organisations to increase the quality and quantity of their transactions hence increasing the revenues and reducing the risks. Many organisations around the World still use statistical regression techniques which are well established for many problems such as fraud detection or risk analysis. However, recent years have seen the application of computational intelligence techniques to develop predictive models for financial applications. Some of the computational intelligence techniques like neural networks provide good predictive models, nevertheless they are considered as black box models which do not provide an easy to understand reasoning about a given decision or even a summary of the generated model. However, in the current economic situation, transparency became an important factor where there is a need to fully understand and analyze a given financial model. In this paper, we will present a Genetic Type-2 Fuzzy Logic System (FLS) for the modeling and prediction of financial applications. The proposed system is capable of generating summarized models from a specified number of linguistic rules, which enables the user to understand the generated financial model. The system is able to use this summarized model for prediction within financial applications. We have performed several evaluations in two distinctive financial domains, one for the prediction of good/bad customers in a financial real-world lending application and the other domain was in the prediction of arbitrage opportunities in the stock markets. The proposed Genetic type-2 FLS has outperformed white box models like the Evolving Decision Rule procedure (which is a white based on Genetic Programming and decision trees) and gave a comparable performance to black box models like neural networks while the proposed genetic type-2 FLS provided a white box model which is easy to understand and analyse by the lay user.
Similar content being viewed by others
References
Ahmad S, Jahormi M (2007) Construction accurate fuzzy classification systems: a new approach using weighted fuzzy rules. Computer graphics, imaging and visualisation, pp 408–413
Casillas J, Cordon O, Herrera F, Magdalena L (2003a) Accuracy improvements in linguistic fuzzy modeling. Springer, Berlin
Casillas J, Cordon O, Herrera F, Magdalena L (2003b) Interpretability issues in fuzzy modeling. Springer, Berlin
Cohen J, Cohen P, West S, Aiken L (2003) Applied multiple regression/correlation analysis for the behavioral sciences, 2nd edn. Lawrence Erlbaum Associates, Hillsdale
Deaton A (1992) Understanding consumption. Oxford University Press, Oxford
Ehrenberg A, Smith (2008) Modern labor economics. HarperCollins
Fawcett T (2006) An introduction to ROC analysis. Pattern Recognit Lett 27:861–874
Garcia-Almanza A (2008) New classification methods for gathering patterns in the context of genetic programming, PhD Thesis. Department of Computing and Electronic Systems, University of Essex, Essex
Garcia-Almanza A, Tsang E (January 2008) Evolving decision rules to predict investment opportunities. Int J Autom Comput 5(1):22–31
Garcia-Almanza A, Tsang E (2006) Forecasting stock prices using genetic programming and chance discovery. 12th International conference on computing in economics and, finance
Giacomini E (2003) Neural networks in quantitative finance. Master Thesis. Humboldt-Universit atzu Berlin, Berlin
Hagras H (2004) A hierarchical type-2 fuzzy logic control architecture for autonomous mobile robots. IEEE Trans Fuzzy Syst 12(4):524–539
Hanley J, McNeil B (1983) A method of comparing the areas under receiver operating characteristic curves derived from the same cases. Radiology 3:148
Ishibuchi H (2001a) Effect of rule weights in fuzzy rule-based classification system. IEEE Trans Fuzzy Syst 9(4):506–515
Ishibuchi H (2001b) Three-objective genetic-based machine learning for linguistic rule extraction. Inf Sci 136(1–4):109–133
Ishibuchi H, Nojima Y (2007) Analysis of interpretability-accuracy tradeoff of fuzzy systems by multiobjective fuzzy genetics-based machine learning. Int J Approx Reason 44(1):4–31
Ishibuchi H, Nakashima T, Murata T (1999) Performance evaluation of fuzzy classifier systems for multi-dimensional pattern classification problems. IEEE Trans Syst Man Cybern Part B Cybern 29(5):601–618
Ishibuchi H, Yamamoto T (2004) Fuzzy rule selection by multy-objective genetic local search algorithms and rule evaluation measures in data mining. Inf Sci 141(1):59–88
Ishibuchi H, Yamamoto T (2005) Rule weight specification in fuzzy rule-based classification systems. IEEE Trans Fuzzy Syst 13(4):428–435
Ishibuchi H, Yamamoto T, Nakashima T (2006) An approach to fuzzy default reasoning for function approximation. Soft Comput 10(9):850–864
Kassem S (2012) A type2 fuzzy logic system for workforce management in the telecommunications domain, MSc, Thesis. University of Essex, Essex
Kim K (2003) Financial time series forecasting using support vector machines. Neurocomputing 55(1–2):307–319
Kim HS, Sohn SY (2010) Support vector machines for default prediction of SMEs based on technology credit. Eur J Oper Res 3:838–846
Kohavi R, Provost F (1998) Glossary of terms. Edited for the special issue on applications of machine ;earning and the knowledge discovery process, vol 30
Krugman P, Obstfeld M (1988) International economics: theory and policy. Scott, Foresman
Kwong K (2001) Financial forecasting using neural network or machine learning techniques. University of Queensland, Queensland
Laidler D (1993) The demand for money: theories, evidence, and problems
Lawrence R (1997) Using neural networks to forecast stock market prices. University of Manitoba, Manitoba
Levinson M (2006) Guide to financial markets. The Economist. Profile Books, London, pp 145–6
Mansoori E, ZolGhadri M, Katebi D (2006) Using distribution of data to enhance performance of fuzzy classification system. Iran J Fuzzy Syst 4(1)
Mendel J (2001) Uncertain rule-based fuzzy logic systems: introduction and new directions. Prentice-Hall, New Jersey
Sanz J, Fernandez A, Bustince H, Herrera F (2010) Improving the performance of fuzzy rule-based classification systems with interval-valued fuzzy sets and genetic amplitude tuning. Inf Sci 180:3674–3685
Sanz J, Fernandez A, Bustince H, Herrera F (2011) A genetic tuning to improve the performance of fuzzy rule-based classification systems with interval-valued fuzzy sets: degree of ignorance and lateral position. Int J Approx Reason 52:751–766
Shakya S (2004) Markov random field modeling of genetic algorithms. Progress report submitted to The Robert Gordon University to make the case for transfer from MPhil to PhD, The Robert Gordon University
Shigeo A, Lan M (1995) A method for fuzzy rules extraction directly from numerical data and its application in pattern classification. IEEE Trans Fuzzy Syst 3(1):18–28
Swets J (1996) Signal detection theory and ROC analysis in psychology and diagnostics: collected papers. Lawrence Erlbaum Associates, Mahwah
Tufféry S (2011) Data mining and statistics for decision making, Chichester
Wang L (2003) The WM method completed: a flexible fuzzy system approach to data mining. IEEE Trans Fuzzy Syst 11(6):768–782
Author information
Authors and Affiliations
Corresponding author
Additional information
Communicated by G. Acampora.
Rights and permissions
About this article
Cite this article
Bernardo, D., Hagras, H. & Tsang, E. A genetic type-2 fuzzy logic based system for the generation of summarised linguistic predictive models for financial applications. Soft Comput 17, 2185–2201 (2013). https://doi.org/10.1007/s00500-013-1102-y
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-013-1102-y