Skip to main content
SpringerLink
Log in

Correlation-aided support vector regression for forex time series prediction

  • ISNN 2010
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Market is often found behaving surprisingly similar to history, which implies that correlation exists significant for market trend analysis. In the context of Forex market analysis, this paper proposes a correlation-aided support vector regression (cSVR) for time series application, where correlation data are extracted through a graphical channel correlation analysis, compensated by a parameterized Pearson’s correlation to exclude noise meanwhile minimize useful information lost. The effectiveness of cSVR against SVR is confirmed by experiments on 5 contracts (NZD/AUD, NZD/EUD, NZD/GBP, NZD/JPY, and NZD/USD) exchange rate prediction within the period from January 2007 to December 2008.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Kirkpatrick CD, Dahlquist JR (2006) Technical analysis: the complete resource for financial market technicians. Financial Times 704

  2. Abarbanell JS, Bushee BJ (1997) Fundamental analysis, future earnings, and stock prices. J Account Res 35(1):1–24 [Online]. Available: http://www.jstor.org/stable/2491464

    Google Scholar 

  3. Taylor MP, Allen H (1992) The use of technical analysis in the foreign exchange market. J Int Money Finance 11(3):304–314

    Article  Google Scholar 

  4. Park C, Irwin S (2004) The profitability of technical analysis: a review. AgMAS, Tech Rep, vol 4

  5. Murphy JJ (1999) Technical analysis of the financial markets. New York Institute of Finance, p 264

  6. Schwager JD (1996) Technical analysis. Wiley, New Jersey, p 545

    Google Scholar 

  7. Saacke P (2002) Technical analysis and the effectiveness of central bank intervention. J Int Money Finance 21(4):459– 479, [Online]. Available: http://www.sciencedirect.com/science/article/B6V9S-45BCP6T-5/2/6e35493047aec373a8f6612d2e4071cf

    Google Scholar 

  8. Neely CJ (1997) Technical analysis in the foreign exchange market: a layman’s guide. Review no. Sep, pp 23–38

  9. Appel G (2005) Technical analysis: power tools for active investors. FT Press, Upper Saddle River

  10. DSouza C (2002) A market microstructure analysis of foreign exchange intervention in canada. Bank of Canada Working Paper 2002–16, vol 1192–5434

  11. Lui Y-H, Mole D (1998) The use of fundamental and technical analyses by foreign exchange dealers: Hong kong evidence. J Int Money Finance 17(3):535–545 [Online]. Available: http://www.sciencedirect.com/science/article/B6V9S-3V5WNPP-10/2/9f85ed3465b1c7b757fb453c46c97531

    Google Scholar 

  12. Chou Y-l (1975) Statistical analysis. Holt International, New York

    Google Scholar 

  13. Rodgers JL, Nicewander WA (1988) Thirteen ways to look at the correlation coefficient. Am Stat 42(1):59–66

    Article  Google Scholar 

  14. Pearson K (1897) Mathematical contributions to the theory of evolution–on a form of spurious correlation which may arise when indices are used in the measurement of organs. The Royal Society, pp 489–498

  15. Corder G, Foreman D (2009) Nonparametric statistics for non-statisticians: a step-by-step approach. Wiley, New Jersey

    Book  Google Scholar 

  16. Kondratenko VV, Kuperin YA (2003) Using recurrent neural networks to forecasting of forex, [Online]. Available: http://www.citebase.org/abstract?id=oai:arXiv.org:cond-mat/0304469

  17. Kwapien J, Gworek S, Drozdz S (2009) Structure and evolution of the foreign exchange networks. Acta Phys Pol B 40:175 [Online]. Available: http://www.citebase.org/abstract?id=oai:arXiv.org:0901.4793

    Google Scholar 

  18. Plackett RL (1983) Karl pearson and the chi-squared test. Int Stat Rev 51(1):59C72

    MathSciNet  Google Scholar 

  19. Tate RF (1954) Correlation between a discrete and a continuous variable. point-biserial correlation. Ann Math Stat 25(3):603–607

    Article  MathSciNet  Google Scholar 

  20. Myers JL, Well A (2003) Research design and statistical analysis, 2 edn. Lawrence Erlbaum, Mahwah

    Google Scholar 

  21. Detsky AS, Mclaughlin JR, Baker JP, Johnston N, Whittaker S, Mendelson RA, Jeejeebhoy KN (1987) What is subjective global assessment of nutritional status? Parenter Enteral Nutr 11(1):8–13

    Article  Google Scholar 

  22. Mantel N (1963) Chi-square tests with one degree of freedom; extensions of the mantel- haenszel procedure. J Am Stat Assoc 58(303):690–700

    Article  MathSciNet  Google Scholar 

  23. Paez JG, J?nne PA, Lee JC, Tracy S, Greulich H, Gabriel S, Herman P, Kaye FJ, Lindeman N, Boggon TJ, Naoki K, Sasaki H, Fujii Y, Eck MJ, Sellers WR, Johnson BE, Meyerson M (2004) Egfr mutations in lung cancer: correlation with clinical response to gefitinib therapy. Sci Exp 304(5676):1497–1500

    Google Scholar 

  24. Yu C, Chan Y, Zhang Q, Yip G, Chan C, Kum L, Wu L, Lee A, Lam Y, Fung J (2005) Benefits of cardiac resynchronization therapy for heart failure patients with narrow qrs complexes and coexisting systolic asynchrony by echocardiography. Am Coll Cardiol 48(11):2251–2257

    Article  Google Scholar 

  25. Wray D (2004) Literacy: major themes in education. In: Major themes in education. Routledge Falmer, University of Warwick, England. ISBN 978041527709

  26. Akdede BBK, Alptekin K, Kitis A, Arkar H, Akvardar Y (2005) Effects of quetiapine on cognitive functions in schizophrenia. Prog Neuropsychopharmacol Biol Psychiatry 29(2):233–238

    Article  Google Scholar 

  27. Deberard MS, Spielmans GI, Julka DL (2004) Predictors of academic achievement and retention among college freshmen: a longitudinal study. Coll Stud J 38(1):66–80

    Google Scholar 

  28. Gelfand J, Feldman S, Stern R, Thomas J, Rolstad T, Margolis D (2004) Determinants of quality of life in patients with psoriasis: a study from the us population. Am Acad Dermatol 51(5):704–708

    Article  Google Scholar 

  29. Sutherland E, Martin R, Bowler R, Zhang Y, Rex M, Kraft M (2004) Physiologic correlates of distal lung inflammation in asthma. Allerg Clin Immunol 113(6):1046–1050

    Article  Google Scholar 

  30. Boot RG, Verhoek M, Fost Md, Hollak CEM, Maas M, Bleijlevens B, Breemen MJv, Meurs Mv, Boven LA, Laman JD, Moran MT, Cox TM, Aerts JMFG (2004) Marked elevation of the chemokine ccl18/parc in gaucher disease: a novel surrogate marker for assessing therapeutic intervention. Blood 103(1):33–39

    Article  Google Scholar 

  31. Panackal AA, Gribskov JL, Staab JF, Kirby KA, Rinaldi M, Marr KA (2006) Clinical significance of azole antifungal drug cross-resistance in candida glabrata. Clin Microbiol 44(5):1740–1743

    Article  Google Scholar 

  32. Wong KF, Selzer T, Benkovic SJ, Hammes-Schiffer S (2005) Impact of distal mutations on the network of coupled motions correlated to hydride transfer in dihydrofolate reductase. Natl Acad Sci USA 102(19):6807–6812

    Google Scholar 

  33. Lapata M (2006) Automatic evaluation of information ordering: Kendall’s tau. Comput Linguist 32(4):471–484

    Article  Google Scholar 

  34. Hilde CB, Havard H (2006) The importance of interest rates for forecasting the exchange rate. J Forecast 25(3):209–221. doi:10.1002/for.983

    Article  MathSciNet  Google Scholar 

  35. Vapnik VN (1999) An overview of statistical learning theory. IEEE Trans Neural Netw 10(5):988–999

    Article  Google Scholar 

  36. Drucker H, Burges CJC, Kaufman L, Smola A, Vapnik V (1997) Support vector regression machines. Adv Neural Inf Process Syst (9):115–161

  37. Scholkopf B, Burges CJC, Smola AJ (1999) Advances in Kernel methods–support vector learning. The MIT Press, Cambridge

    Google Scholar 

  38. Sapankevych NI, Sankar R (2009) Time series prediction using support vector machines: a survey. IEEE Comput Intell 4(2):25–37

    Google Scholar 

  39. Cheng J, Randall A, Baldi P (2006) Prediction of protein stability changes for single-site mutations using support vector machines. Proteins Struct Funct Bioinform 62(4):1125–1132

    Article  Google Scholar 

  40. Trafalis TB, Ince H (2000) Support vector machine for regression and applications to financial forecasting, pp 348–353

  41. Tay FEH, Cao LJ (2001) Application of support vector machines in financial time series forecasting. Omega 29:209–317

    Article  Google Scholar 

  42. Tay FEH, CaO LJ (2002) Modified support vector machines in financial time series forecasting. Neurocomputing 48(1-4):847–861

    Article  Google Scholar 

  43. Van Gestel EA (2001) Tony Financial time series prediction using least squares support vector machines within the evidence framework. IEEE Trans Neural Netw 12(4):809–821

    Article  Google Scholar 

  44. Cao L and Gu Q (2002) Dynamic support vector machines for non-stationary time series forecasting. Intell Data Anal 6(1):67–83

    Google Scholar 

  45. Lu C-J, Lee T-S, Chiu C-C (2009) Financial time series forecasting using independent component analysis and support vector regression. Decision Support Syst 47(2):115–125 [Online]. Available: http://www.sciencedirect.com/science/article/B6V8S-4VKXBVX-1/2/299b01b62df0f035ab42062e6ad2c22c

  46. Huang C-L, Tsai C-Y (2009) A hybrid sofm-svr with a filter-based feature selection for stock market forecasting. Expert Syst Appl 36(2), Part 1, pp 1529–1539 [Online]. Available: http://www.sciencedirect.com/science/article/B6V03-4RC2NKB-4/2/3d8820b4b07243e5914630647d8492e8

  47. Cao L (2003) Support vector machines experts for time series forecasting. Neurocomputing 51:321–339

    Article  Google Scholar 

  48. Cao LJ, Tay FEH (2003) Support vector machine with adaptive parameters in financial time series forecasting. IEEE Trans Neural Netw 14(6):1506–1518

    Article  Google Scholar 

  49. Cao L, Tay FEH (2001) Financial forecasting using support vector machines. Neural Comput Appl 10(2):184–192

    Article  Google Scholar 

  50. Min JH, Lee Y-C (2005) Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters. Expert Syst Appl 28(4):603–614

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Auckland University of Technology, Auckland, New Zealand

    Shaoning Pang, Lei Song & Nik Kasabov

Authors
  1. Shaoning Pang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lei Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nik Kasabov
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shaoning Pang.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Pang, S., Song, L. & Kasabov, N. Correlation-aided support vector regression for forex time series prediction. Neural Comput & Applic 20, 1193–1203 (2011). https://doi.org/10.1007/s00521-010-0482-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-010-0482-5

Keywords

Search

Navigation