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Non-Parametric Regression Methods

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Abstract

The nature of the financial time series is complex, continuous interchange of stochastic and deterministic regimes. Therefore, it is difficult to forecast with parametric techniques. Instead of parametric models, we propose three techniques and compare with each other. Neural networks and support vector regression (SVR) are two universally approximators. They are data-driven non parametric models. ARCH/GARCH models are also investigated. Our assumption is that the future value of Istanbul Stock Exchange 100 index daily return depends on the financial indicators although there is no known parametric model to explain this relationship. This relationship comes from the technical analysis. Comparison shows that the multi layer perceptron networks overperform the SVR and time series model (GARCH).

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References

  • Ancona N (1999) Classification properties of support vector machines for regression. Technical report Istituto Elaborazioni Segnali ed Immagini – Consiglio Nazionale delle Ricerche, Bari, Italy. RI-IESI/CNR- Nr 02/99

  • Bollerslev T (1986) Generalized autoregressive conditional heteroskedasticity. J Econ 31:307–327

    Google Scholar 

  • Chen A, Leung MT, Daouk H (2003) Application of neural networks to an emerging market: forecasting and trading the taiwan stock index. Comput Oper Res 30:901–923

    Article  Google Scholar 

  • Chong CW, Ahmad MI, Abdullah MY (1999) Performance of garch models in forecasting stock market volatility. J Forecast 18:333–343

    Article  Google Scholar 

  • Collobert R, Bengio S (2001) Svmtorch: Support vector machines for large-scale regression problems. J Mach Learning Res 1:143–160

    Article  Google Scholar 

  • Cortes C, Vapnik V (1995) Support vector networks. Mach Learn 20:273–297

    Google Scholar 

  • Dourra H, Siy P (2002) Investment using technical analysis and fuzzy logic. Fuzzy Sets Syst 127:221–240

    Article  Google Scholar 

  • Engle RF (1982) Autoregressive conditional heteroscedasticity with estimates of the variance of the united kingdom inflation. Econometrica 50:987–1008

    Article  Google Scholar 

  • Engle RF, Bollerslev T (1986) Modelling the persistence of conditional variances. Econ Rev 5:1–87

    Article  Google Scholar 

  • Engle RF, Lilien DM, Robins RP (1987) Estimating time varying risk premia in the term structure: The arch-m model. Econometrica 55:391–407

    Article  Google Scholar 

  • Galindo J (1998) A framework for comperative analysis of statistical and machine learning methods: An application to the black scholes option pricing equations. Techincal report Banco de Mexico, Mexico, DF, 04930

  • Gestel TV, Suykens J, Baesens B, Viaene S, Vanthienen J, Dedene G, Moor BD, Vandewalle J (2004) Benchmarking least squares support vector machine classifiers. Mach Learn 54:5–32

    Article  Google Scholar 

  • Haykin S (1994) Neural networks: a comprehensive foundation. MacMillan, New York

    Google Scholar 

  • Herwartz H (2000) Weekday dependence of german stock market. Applied Stochastic Models in Busines and Industry 16:47–71

    Article  Google Scholar 

  • Hutchinson JM, Lo AW, Poggio T (1994) A nonparametic approach to pricing and hedging derivative securities via learning networks. J Finance XLIX:851–889

    Article  Google Scholar 

  • Joachims T (1999) Making large-scale SVM learning practical. pp 169–184, MIT Press, Cambridge

    Google Scholar 

  • Kim K-J (2004) Artificial neural networks with feature transformation based on domain knowledge for the prediction of stock index futures. Intell Syst Account Finance Manage 12:167–176

    Article  Google Scholar 

  • Lamoureux CG, Lastrapes WD (1990) Persistence in variance, structural change and the garch model. J Bus Econ Stat 8:225–234

    Article  Google Scholar 

  • Lee Y-J, Mangasarian O (2001) Rsvm: Reduced support vector machines. First SIAM International Conference on Data Mining

  • Leigh W, Paz M, Purvis R (2002) An analysis of a hybrid neural network and pattern recognition technique for predicting short-term increases in the nyse. Int J Manage Sci 30:69–76

    Google Scholar 

  • Letamendia LN (2002) Trading systems designed by genetic algorithms. Manager Finance 28:87–106

    Article  Google Scholar 

  • Lim G, McNelisb P (1998) The effect of the nikkei and the S and P on the all-ordinaries: A comparison of three models. Int J Finance Econ 3:217–228

    Article  Google Scholar 

  • Nelson DB (1990) Arch models as diffusion approximations. J Econometrics 45:7–38

    Article  Google Scholar 

  • Nelson DB, Cao CQ (1992) Inequality constraints in the univariate garch model. J Bus Econ Stat 10:229–235

    Article  Google Scholar 

  • Osuna E, Freund R, Girosi F (1997) Training support vector machines: an application to face detection. In: Proceedings of IEEE CVPR, pp 130–136

  • Pontil M, Verri A (1997) Properties of support vector machines. Technical report Massachusetts Institute of Technology, Artificial Intelligence Laboratory

  • Rifkin R (2000) Svmfu a support vector machine package. Technical report http://five-percentnation.mit.edu/PersonalPages/rif/SvmFu

  • Smola A, Schölkopf B (2004) A tutorial on support vector regression. Statistics and Computing 14:192–222

    Article  Google Scholar 

  • Steiner M, Wittkemper H (1997) Portfolio optimization with a neural network implementation of the coherent market hypothesis. Eur J Oper Res 100:27–40

    Article  Google Scholar 

  • Suykens JAK (2000) Least squares support vector machines for classification and nonlinear modelling. Neural Network World 10:29–48

    Google Scholar 

  • Tay FEH, Cao L (2002) Modified support vector machines in financial time series forecasting. Neurocomputing 48:847–861

    Article  Google Scholar 

  • Trafalis T (1999) Artificial Neural Networks Applied to Financial Forecasting. In: Dagli, Buczak, Ghosh, Embrechts and Ersoy (eds). Smart engineering system design: neural networks, fuzzy logic, evolutionary programming, data Mining and Complex Systems. ASME Press, New York, pp 1049–1054

    Google Scholar 

  • Trafalis TB, Ince H (2002) Benders decomposition technique for support vector regression. In: Neural networks, IJCNN ’02. Proceedings of the 2002 International Joint Conference, IEEE, vol. 3, pp 2767–2772

  • Trafalis TB, Ince H, Mishina T (2003) Support vector regression in option pricing. In: Procedings of Conference on Computational Intelligence and Financial Engineering (CIFer 2003), Hong Kong

  • Tsaih R (1999) Sensitivity analysis, neural networks and, the finance. In: IICNN’99 pp 3830–3835. Piscataway

  • Vapnik V (1998) Statistical learning theory. Wiley, New York

    Google Scholar 

  • Wilder JW (1978) New concepts in technical trading systems. Trend Research, Greensboro, NC

    Google Scholar 

  • Yao J, Tan CL (2000) A case study on using neural networks to perform technical forecasting of forex. Neurocomputing 34:79–98

    Article  Google Scholar 

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Authors and Affiliations

  1. Faculty of Business Administration, Gebze Institute of Technology, Cayirova Fab. Yolu No:101 P.K:141, 41400, Gebze, Kocaeli, Turkey

    Huseyin Ince

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  1. Huseyin Ince
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Ince, H. Non-Parametric Regression Methods. CMS 3, 161–174 (2006). https://doi.org/10.1007/s10287-005-0006-4

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