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Computational Economics

, Volume 34, Issue 2, pp 173–193 | Cite as

Predicting EU Energy Industry Excess Returns on EU Market Index via a Constrained Genetic Algorithm

  • Massimiliano Kaucic
Article

Abstract

This article introduces an automated procedure to simultaneously select variables and detect outliers in a dynamic linear model using information criteria as objective functions and diagnostic tests as constraints for the distributional properties of errors. A robust scaling method is considered to take into account the sensitiveness of estimates to abnormal data. A genetic algorithm is developed to these purposes. Two examples are presented where models are designed to produce short-term forecasts for the excess returns of the MSCI Europe Energy sector on the MSCI Europe index and a recursive estimation-window is used to shed light on their predictability performances. In the first application the data-set is obtained by a reduction procedure from a very large number of leading macro indicators and financial variables stacked at various lags, while in the second the complete set of 1-month lagged variables is considered. Results show a promising capability to predict excess sector returns through the selection, using the proposed methodology, of most valuable predictors.

Keywords

Genetic algorithm Penalty function method Model selection Excess return Information criteria 

JEL Classification

C32 C52 C53 C61 C63 

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Supplementary material

10614_2009_9176_MOESM1_ESM.pdf (112 kb)
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References

  1. Ahumada H.A. (1985) An encompassing test of two models of the balance of trade for Argentina. Oxford Bulletin of Economics and Statistics 47(1): 51–70CrossRefGoogle Scholar
  2. Akaike, H. (1973). Information theory and an extension of the maximum likelihood principle. In B. N. Petrox & F. Caski (Eds.), Second international symposium on information theory Academial Kiado, Budapest, (pp. 267–281).Google Scholar
  3. Avramov D. (2002) Stock return predictability and model uncertainty. Journal of Financial Economics 64: 423–458CrossRefGoogle Scholar
  4. Balcombe K.G. (2005) Model selection using information criteria and genetic algorithms. Computational Economics 25: 207–228CrossRefGoogle Scholar
  5. Bossaerts P., Hillion P. (1999) Implementing statistical criteria to select return forecasting models: What do we learn?. The Review of Financial Studies 12(2): 405–428CrossRefGoogle Scholar
  6. Bozdogan, H. (1988). ICOMP: A new model selection criterion. In Classification and related methods in data analysis (pp. 599–608). Amsterdam: North-Holland.Google Scholar
  7. Bozdogan H. (2000) Akaike’s information criterion and recent developments in information complexity. Journal of Mathematical Psychology 44: 62–91CrossRefGoogle Scholar
  8. Campos J., Ericsson N.R. (1999) Constructive data mining: modeling consumers’ expenditure in Venezuela. Econometrics Journal 2(2): 226–240CrossRefGoogle Scholar
  9. Campos J., Ericsson N.R., Hendry D.F. (2005a) General-to-specific modeling. Cheltenham, Edward ElgarGoogle Scholar
  10. Campos, J., Ericsson, N. R., & Hendry, D. F. (2005b). General-to-specific modeling: An overview and selected bibliography. International finance discussion papers 838, Board of Governors of the Federal Reserve System.Google Scholar
  11. Chiang L.H., Pell R.J., Seasholtz M.B. (2003) Exploring process data with the use of robust outlier detection algorithms. Journal of Process Control 13: 437–449CrossRefGoogle Scholar
  12. Costantini M., Pappalardo C. (2008) Combination of forecast methods using an algorithm-based procedure. Economic series 228. Institute for Advanced Studies, ViennaGoogle Scholar
  13. Cremers K.J.M. (2002) Stock return predictability: a Bayesian model selection perspective. The Review of Financial Studies 15(4): 1223–1249CrossRefGoogle Scholar
  14. Deb K. (2001) Multi-objective optimization using evolutionary algorithms. Wiley, New YorkGoogle Scholar
  15. Dorsey R.E., Mayer W. (1995) Genetic algorithms for estimation problems with multiple optima, nondifferentiability, and other irregular features. Journal of Business and Economic Statistics 13: 53–66CrossRefGoogle Scholar
  16. Dunis, C. L., & Williams, M. (2005). Applications of advanced regression analysis for trading and investment. In Applied quantitative methods for trading and investment (pp. 1–40). Wiley Finance Series, Wiley.Google Scholar
  17. George E.I. (2000) The variable selection problem. University of Texas at Austin, AustinGoogle Scholar
  18. George E.I., McCulloch R.E. (1993) Variable selection via Gibbs sampling. Journal of the American Statistical Association 88(423): 881–889CrossRefGoogle Scholar
  19. George E.I., McCulloch R.E. (1997) Approaches for Bayesian variable selection. Statistica sinica 7: 339–373Google Scholar
  20. Gilbert C.L. (1986) Professor Hendry’s econometric methodology. Oxford Bulletin of Economics and Statistics 48(3): 283–307Google Scholar
  21. Goldberg D.E. (1989) Genetic algorithms in search, optimization and machine learning. Addison-Wesley, New YorkGoogle Scholar
  22. Grenouilleau, D. (2004). A sorted leading indicators dynamic (SLID) factor model for short-run euro-area GDP forecasting, Economic Paper 219, Directorate-General for Economic and Financial Affairs, European Economy.Google Scholar
  23. Grenouilleau, D. (2006). The stacked leading indicators dynamic factor model: A sensitivity analysis of forecast accuracy using bootstrapping, Economic Paper 249, Directorate-General for Economic and Financial Affairs, European Economy.Google Scholar
  24. Hady A., Siminoff J. (1992) Comments on Paul and Fung (1991). Technometrics 34(3): 373–374CrossRefGoogle Scholar
  25. Harvey D.I., Leybourne S., Newbold P. (1998) Tests for forecast encompassing. Journal of Business and Economic Statistics 26: 254–259CrossRefGoogle Scholar
  26. Hasheminia H., Niaki S.T.A. (2006) A genetic algorithm approach to find the best regression/econometric model among the candidates. Applied Mathematics and Computation 183: 337–349CrossRefGoogle Scholar
  27. Hendry D.F. (1983) Econometric modelling: The ‘Consumption Function’ in retrospect. Scottish Journal of Political Economy 30(3): 193–220CrossRefGoogle Scholar
  28. Hoeting J.A., Raftery A.E., Madigan D. (1996) A method for simultaneous variable selection and outlier identification in linear regression. Journal of Computational Statistics 22: 251–271Google Scholar
  29. Kaboudan M.A. (2000) Genetic programming prediction of stock prices. Computational Economics 16: 207–236CrossRefGoogle Scholar
  30. Koza J. (1992) Genetic programming: On the programming of computers by means of natural selection. The MIT Press, Cambridge, MAGoogle Scholar
  31. Leontitsis, A., & Pange, J. (2004). WSMA: In between weighted and simple average. In 17th annual pan-hellenic conference on statistics, Leukada, Greece, (pp. 519–526).Google Scholar
  32. MacDonald R., Taylor M.P. (1992) A stable us money demand function, 1874–1975. Economics Letters 39(2): 191–198CrossRefGoogle Scholar
  33. Michalewitz Z. (1994) Genetic algorithms + data structure = evolution program. Springer-Verlang, New YorkGoogle Scholar
  34. Miller A.J. (1990) Subset selection in regression. Chapman and Hall, New YorkGoogle Scholar
  35. Pesaran M.H., Pesaran B. (1997) Working with Microfit 4.0. Oxford University Press. Oxford, OxfordGoogle Scholar
  36. Pesaran M.H., Timmermann A. (1995) Predictability of stock returns: Robustness and economic significance. The Journal of Finance 50(4): 1201–1228CrossRefGoogle Scholar
  37. Pesaran M.H., Timmermann A. (2000) A recursive modelling approach to predicting UK stock returns. The Economic Journal 110: 159–191CrossRefGoogle Scholar
  38. Pynnnen, S. (1992). Detection of outliers in regression analysis by information criteria. Discussion paper 146. University of Vaasa.Google Scholar
  39. Rapach D.E., Strauss J.K. (2008) Forecasting US employment growth using forecast combining methods. Journal of Forecasting 27: 75–93CrossRefGoogle Scholar
  40. Runarsson, T. P., & Yao, X. (2002). Constrained evolutionary optimization—the penalty function approach. In Evolutionary optimization (Vol. 48, pp. 87–113). USA: Kluwer Academic Publisher.Google Scholar
  41. Schwarz G. (1978) Estimating the dimension of a model. Annals of Statistics 6: 461–464CrossRefGoogle Scholar
  42. Smith, A. E., & Coit, D. W. (1997). Penalty functions. In: Handbook of evolutionary computation (pp. C5.2:1–6). Oxford: Oxford University Press.Google Scholar
  43. Stock, J. H., & Watson, M. W. (2006). In: Forecasting with many predictors. In Handbook of economic forecasting (Vol. 1, pp. 515–554). Amsterdam: North-Holland.Google Scholar
  44. Tolvi J. (2004) Genetic algorithms for outlier detection and variable selection in linear regression models. Soft Computing 8: 527–533CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC. 2009

Authors and Affiliations

  1. 1.Dipartimento di Economia e Tecnica AziendaleUniversity of TriesteTriesteItaly

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