A hybrid procedure with feature selection for resolving stock/futures price forecasting problems

Neural Computing and Applications

Volumn 22, Issue 3-4, 2013, Pages 651-671

  Hsu, Chih Ming ^a  

^a MINGHSIN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Taiwan)

Author keywords

Backpropagation neural network; Feature selection; Genetic programming; Stock futures price forecasting

Indexed keywords

BACKPROPAGATION; COSTS; ELECTRONIC TRADING; FEATURE EXTRACTION; GENETIC ALGORITHMS; GENETIC PROGRAMMING; NEURAL NETWORKS; SOFT COMPUTING;

BACK PROPAGATION NEURAL NETWORKS; FEATURE SELECTION METHODS; FORECASTING PROCEDURES; INHERENT CHARACTERISTICS; PRICE FORECASTING; SELECTION TECHNIQUES; SOFTCOMPUTING TECHNIQUES; TAIWAN STOCK EXCHANGES;

FORECASTING;

EID: 84874018551     PISSN: 09410643     EISSN: None     Source Type: Journal    
DOI: 10.1007/s00521-011-0721-4     Document Type: Article

References (39)

1. Zarandi MHF, Rezaee B, Turksen IB, Neshat E (2009) A type-2 fuzzy rule-based expert system model for stock price analysis. Expert Syst Appl 36(1): 139-154. doi: 10. 1016/j. eswa. 2007. 09. 034.
2. Thomsett MC (1998) Mastering fundamental analysis. Dearborn Finanical Publishing, Chicago.
3. Thomsett MC (1999) Mastering technical analysis. Dearborn Finanical Publishing, Chicago.
4. Box GEP, Jenkins GM (1970) Time series analysis, forecasting and control. Holden-Day, San Francisco.
5. Bollerslev T (2001) Generalized autoregressive conditional heteroskedasticity. J Econ 100(1): A307-A327.
6. Pai PF, Lin CS (2005) A hybrid ARIMA and support vector machines model in stock price forecasting. Omega-Int J Manag Sci 33(6): 497-505. doi: 10. 1016/j. omega. 2004. 07. 024.
7. Chang PC, Liu CH (2008) A TSK type fuzzy rule based system for stock price prediction. Expert Syst Appl 34(1): 135-144. doi: 10. 1016/j. eswa. 2006. 08. 020.
8. Ince H, Trafalis TB (2008) Short term forecasting with support vector machines and application to stock price prediction. Int J Gen Syst 37(6): 677-687. doi: 10. 1080/03081070601068595.
9. Huang CL, Tsai CY (2009) A hybrid SOFM-SVR with a filter-based feature selection for stock market forecasting. Expert Syst Appl 36(2): 1529-1539. doi: 10. 1016/j. eswa. 2007. 11. 062.
10. Liang X, Zhang HS, Mao JG, Chen Y (2009) Improving option price forecasts with neural networks and support vector regressions. Neurocomputing 72(13-15): 3055-3065. doi: 10. 1016/j. neucom. 2009. 03. 015.
11. Hadavandi E, Shavandi H, Ghanbari A (2010) Integration of genetic fuzzy systems and artificial neural networks for stock price forecasting. Knowl Based Syst 23(8): 800-808. doi: 10. 1016/j. knosys. 2010. 05. 004.
12. Lu CJ (2010) Integrating independent component analysis-based denoising scheme with neural network for stock price prediction. Expert Syst Appl 37(10): 7056-7064. doi: 10. 1016/j. eswa. 2010. 03. 012.
13. Cheng CH, Chen TL, Wei LY (2010) A hybrid model based on rough sets theory and genetic algorithms for stock price forecasting. Inf Sci 180(9): 1610-1629. doi: 10. 1016/j. ins. 2010. 01. 014.
14. Fausett L (1994) Fundamentals of neural networks: architectures, algorithms, and applications. Prentice-Hall, Englewood Cliffs.
15. Patterson DW (1996) Artificial neural networks: theory and applications. Prentice Hall, Singapore.
16. Lin CT, Yeh HY (2009) Empirical of the Taiwan stock index option price forecasting model-applied artificial neural network. Appl Econ 41(15): 1965-1972. doi: 10. 1080/00036840601131672.
17. Yazici B, Memmedli M, Aslanargun A, Asma S (2010) Analysis of international debt problem using artificial neural networks and statistical methods. Neural Comput Appl 19(8): 1207-1216. doi: 10. 1007/s00521-010-0422-4.
18. Bilgili M, Sahin B (2010) Comparative analysis of regression and artificial neural network models for wind speed prediction. Meteorol Atmos Phys 109(1-2): 61-72. doi: 10. 1007/s00703-010-0093-9.
19. Hashemi MR, Ghadampour Z, Neill SP (2010) Using an artificial neural network to model seasonal changes in beach profiles. Ocean Eng 37(14-15): 1345-1356. doi: 10. 1016/j. oceaneng. 2010. 07. 004.
20. Liu H, Motoda H (1998) Feature selection for knowledge discovery and data mining. Kluwer, Boston.
21. Piramuthu S (2004) Evaluating feature selection methods for learning in data mining applications. Eur J Oper Res 156(2): 483-494. doi: 10. 1016/s0377-2217(02)00911-6.
22. Kohavi R, John GH (1997) Wrappers for feature subset selection. Artif Intell 97(1-2): 273-324. doi: 10. 1016/s0004-3702(97)00043-x.
23. Battiti R (1994) Using mutual information for selecting features in supervised neural-net learning. IEEE Trans Neural Netw 5(4): 537-550.
24. Jensen R, Shen Q (2005) Fuzzy-rough data reduction with ant colony optimization. Fuzzy Sets Syst 149(1): 5-20. doi: 10. 1016/j. fss. 2004. 07. 014.
25. Song XP, Ding YS, Huang JW, Ge Y (2010) Feature selection for support vector machine in financial crisis prediction: a case study in China. Expert Syst 27(4): 299-310. doi: 10. 1111/j. 1468-0394. 2010. 00546. x.
26. Bradley PS, Mangasarian OL, Street WN (1998) Feature selection via mathematical programming. INFORMS J Comput 10(2): 209-217.
27. Mladenic D (2006) Feature selection for dimensionality reduction. In: Saunders G, Grobelnik M, Gunn S, ShaweTaylor J (eds) Subspace, latent structure and feature selection, vol 3940. Lecture notes in computer science, pp 84-102.
28. Sivanandam SN, Deepa SN (2008) Introduction to genetic algorithms. Springer, Berlin.
29. Koza JR, Keane MA, Streeter MJ, Mydlowec W, Yu J, Lanza G (2005) Genetic programming IV: routine human-competitive machine intelligence.
30. Koza JR, Streeter MJ, Keane MA (2008) Routine high-return human-competitive automated problem-solving by means of genetic programming. Inf Sci 178(23): 4434-4452. doi: 10. 1016/j. ins. 2008. 07. 028.
31. Ciglaric I, Kidric A (2006) Computer-aided derivation of the optimal mathematical models to study gear-pair dynamic by using genetic programming. Struct Multidiscip Optim 32(2): 153-160. doi: 10. 1007/s00158-006-0004-3.
32. Etemadi H, Rostamy AAA, Dehkordi HF (2009) A genetic programming model for bankruptcy prediction: empirical evidence from Iran. Expert Syst Appl 36(2): 3199-3207. doi: 10. 1016/j. eswa. 2008. 01. 012.
33. Hwang TM, Oh H, Choung YK, Oh S, Jeon M, Kim JH, Nam SH, Lee S (2009) Prediction of membrane fouling in the pilot-scale microfiltration system using genetic programming. Desalination 247(1-3): 285-294. doi: 10. 1016/j. desal. 2008. 12. 031.
34. Bae H, Jeon TR, Kim S, Kim HS, Kim D, Han SS, May GS (2010) Optimization of silicon solar cell fabrication based on neural network and genetic programming modeling. Soft Comput 14(2): 161-169. doi: 10. 1007/s00500-009-0438-9.
35. Langdon WB, Poli R (2002) Foundations of genetic programming. Springer, New York.
36. Kim KJ, Han I (2000) Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index. Expert Syst Appl 19(2): 125-132.
37. Kim KJ, Lee WB (2004) Stock market prediction using artificial neural networks with optimal feature transformation. Neural Comput Appl 13(3): 255-260. doi: 10. 1007/s00521-004-0428-x.
38. Tsang PM, Kwok P, Choy SO, Kwan R, Ng SC, Mak J, Tsang J, Koong K, Wong TL (2007) Design and implementation of NN5 for Hong Kong stock price forecasting. Eng Appl Artif Intell 20(4): 453-461. doi: 10. 1016/j. engappai. 2006. 10. 002.
39. Lai RK, Fan CY, Huang WH, Chang PC (2009) Evolving and clustering fuzzy decision tree for financial time series data forecasting. Expert Syst Appl 36(2): 3761-3773. doi: 10. 1016/j. eswa. 2008. 02. 025.