Journal of the Korean Data and Information Science Society

The Korean Data and Information Science Society (한국데이터정보과학회)
권호 표지
DOI QR코드

DOI QR Code

Using rough set to develop the optimization strategy of evolving time-division trading in the futures market

러프집합을 활용한 캔들스틱 트레이딩 최적화 전략

  • Kim, Hyun-Ho (Department of Information and Industrial Engineering, Yonsei University) ;
  • Oh, Kyong-Joo (Department of Information and Industrial Engineering, Yonsei University)
  • 김현호 (연세대학교 정보산업공학과) ;
  • 오경주 (연세대학교 정보산업공학과)
  • Received : 2012.05.31
  • Accepted : 2012.07.27
  • Published : 2012.09.30
Download PDF
⟨ Previous Next ⟩

Abstract

This paper proposes to develop system trading strategy using rough set, decision tree in futures market. While there is a great deal of literature about the analysis of data mining, there is relatively little work on developing trading strategies in futures markets. There are three objectives in this paper. The first objective is to analysis performance of decision tree in rule-based system trading. The second objective is to find proper profitable trading interval. The last objective is to find optimized training period of trading rule training. The results of this study show that proposed model is useful trading strategy in foreign exchange market and can be desirable solution which gives lots of investors an important investment information.

본 논문에서는 선물시장에서 러프집합과 의사결정나무를 이용한 매매규칙 기반의 시스템 트레이딩 전략을 제안한다. 과거 데이터마이닝 방법론을 이용한 선물시장 투자전략에 대한 많은 연구가 진행되어 왔으나 상대적으로 다양한 변수의 조합을 통한 시스템 트레이딩에 대한 연구는 거의 없었다. 본 연구는 크게 세 가지 목적을 가지고 있다. 첫 번째 목적은 매매규칙 기반 시스템 트레이딩에서 의사결정나무 방법론의 사용이 투자성과에 어떠한 영향을 미치는가를 분석하는 것이다. 두 번째 목적은 단기매매부터 장기 매매까지 중에서 적절한 매매 시간간격을 찾아내는 것이다. 세번째 목적은 매매규칙 생성 시 사용되는 최적의 트레이닝 구간을 찾는 것이다. 이 논문의 실험결과는 제안한 투자전략의 유용성을 증명할 수 있을 것이며, 또한 이를 통해 시장참여자들에게 투자결정에 있어 도움을 줄 수 있을 것이다.

Keywords

References

  1. Arie, B. D. (2008). Rule effectiveness in rule-based systems: A credit scoring case study. Expert Systems with Applications, 34, 2783-2788. https://doi.org/10.1016/j.eswa.2007.05.003
  2. Byun, H. W., Song, C. W., Han, S. K., Lee, T. K. and Oh, K. J. (2009). Using genetic algorithms to develop volatility index-assisted hierarchical portfolio optimization. Journal of the Korean Data & Information Science Society, 20, 467-478.
  3. Chavarnakul, T. and Enke, D. (2008). Intelligent technical analysis based equivolume charting for stock trading using neural networks. Expert Systems with Applications, 34, 1004-1017. https://doi.org/10.1016/j.eswa.2006.10.028
  4. Chavarnakul, T. and Enke, D. (2009). A hybrid stock trading system for intelligent technical analysis-based equivolume charting. Neurocomputing, 72, 3517-3528. https://doi.org/10.1016/j.neucom.2008.11.030
  5. Chris, F., Andros, G. and Alexandros, K. (2011). Trading frequency and asset pricing on the London Stock Exchange: Evidence from a new price impact ratio. Journal of Banking & Finance, 35, 3335-3350. https://doi.org/10.1016/j.jbankfin.2011.05.014
  6. Dymova, L., Sevastianov, P. and Bartosiewicz, P. (2010). A new approach to the rule-base evidential reasoning: Stock trading expert system application. Expert Systems with Applications, 37, 5564-5576. https://doi.org/10.1016/j.eswa.2010.02.056
  7. Gencay, R. (1999). Linear, non-linear and essential foreign exchange rate prediction with simple technical trading rules. Journal of International Economics, 47, 91-107. https://doi.org/10.1016/S0022-1996(98)00017-8
  8. Hong, J. S. and Kwon, T. W. (2010). Distribution fitting for the rate of return and value at risk. Journal of the Korean Data & Information Science Society, 21, 219-229.
  9. Horton, M. J. (2009). Stars, crows, and doji : The use of candlesticks in stock selection. The Quarterly Review of Economics and Finance, 49, 283-294. https://doi.org/10.1016/j.qref.2007.10.005
  10. Ito, A. (1999). Profits on technical trading rules and time-varying expected returns: Evidence from Pacific- Basin equity markets. Pacific-Basin Finance Journal, 7, 283-330. https://doi.org/10.1016/S0927-538X(99)00008-6
  11. Izumi, K., Toriumi, F. and Matsui, H. (2009). Evaluation of automated-trading strategies using an artificial market. Neurocomputing, 72, 3469-3476. https://doi.org/10.1016/j.neucom.2008.07.020
  12. Kho, B. C. (1996). Time-varying risk premia, volatility, and technical trading rule profits: Evidence from foreign currency futures markets. Journal of Financial Economics, 41, 249-290. https://doi.org/10.1016/0304-405X(95)00861-8
  13. Kim, M. S. and Oh, K. J. (2011). Using rough set to support arbitrage box spread strategies in KOSPI 200 option markets. Journal of the Korean Data & Information Science Society, 22, 37-47.
  14. Lee, J. H. and Jo, G. S. (1999). Exper system for predicting stock market timing using a candlestick chart. Expert Systems with Applications, 16, 357-364. https://doi.org/10.1016/S0957-4174(99)00011-1
  15. Lee, S. J. and Oh, K. J. (2011). Finding the optimal frequency for trade and development of system trading strategies in futures market using dynamic time warping. Journal of the Korean Data & Information Science Society, 22, 255-267.
  16. Marc, B. (2004). Khiops: A statistical discretization method of continuous attributes. Machine Learning, 55, 53-69. https://doi.org/10.1023/B:MACH.0000019804.29836.05
  17. Marshall, B. R., Young, M. R. and Rose, L. C. (2006). Candlestick technical trading strategies: Can they create value for investors? Journal of Banking & Finance, 30, 2303-2323. https://doi.org/10.1016/j.jbankfin.2005.08.001
  18. Murphy, J. J. (1999). Technical analysis of the financial markets, New York Institute of Finance, Paramus, NJ.
  19. Neely, C. J. (2002). The temporal pattern of trading rule returns and exchange rate intervention: intervention does not generate technical trading profits. Journal of International Economics, 58, 211-232. https://doi.org/10.1016/S0022-1996(01)00163-5
  20. Oh, K. J., Kim, T. Y., Jung, K. W. and Kim, C. H. (2011). Stock market stability index via linear and neural network autoregressive model. Journal of the Korean Data & Information Science Society, 22, 355-351.
  21. Park, B. J. (2011). Herd behavior and volatility in financial markets. Journal of the Korean Data & Information Science Society, 22, 1199-1215.
  22. Park, I. S., Han, J. T., Kang, S. B. and Ji, J. H. (2010). Developing the predictive model for stomach cancer using data mining. Journal of the Korean Data & Information Science Society, 21, 1253-1261.
  23. Pawlak, Z. (1991). Rough sets - Theoretical aspects of reasoning about data, Kluwer Academic Publishers, Dordrecht, Boston, London.
  24. Pawlak, Z. (1997). Rough set approach to knowledge-based decision support. European Journal of Operational Research, 99, 48-57. https://doi.org/10.1016/S0377-2217(96)00382-7
  25. Pawlak, Z. and Munakata, T. (1996). Rough control, application of rough set theory to control. Proceedings of the 4th European Congress on Intelligent Techniques and Soft Computing, Germany, 209-218.
  26. Quinlan, J. R. (1986). Induction of decision tree. Machine Learning, 1, 81-106.
  27. Sharpe, W. F. (1994). The Sharpe ratio. Journal of Portfolio Management, 21, 49-58. https://doi.org/10.3905/jpm.1994.409501
  28. Shen, L. and Loh, H. T. (2004). Applying rough sets to market timing decisions. Decision Support Systems, 37, 583-597. https://doi.org/10.1016/S0167-9236(03)00089-7
  29. Shin, Y. K. (2006). An empirical study on stock trading value of each investor type in the Korean stock market. Journal of the Korean Data & Information Science Society, 17, 1099-1106.
  30. Slowinski, R. and Stefanowski, J. (1994). Rough classification with valued closeness relation, Springer- Verlag, Berlin, 482-488.
  31. Song, C. W. and Oh, K. J., (2009). Study of validation process according to various option strategies in a KOSPI 200 options market. Journal of the Korean Data & Information Science Society, 20, 1061-1073.
  32. Tan, Z., Quek, C. and Cheng, P. Y. K. (2011). Stock trading with cycles: A financial application of ANFIS and reinforcement learning. Expert Systems with Applications, 38, 4741-4755. https://doi.org/10.1016/j.eswa.2010.09.001
  33. Taylor, M. P. (1992). The use of technical analysis in the foreign exchange market. Journal of International Money and Finance, 11, 304-314. https://doi.org/10.1016/0261-5606(92)90048-3
  34. Wei, J. M., Wang, S. Q., Wang, M. Y., You, J. P. and Liu, D. Y. (2007). Rough set based approach for inducing decision trees. Knowledge-Based Systems, 20, 695-702. https://doi.org/10.1016/j.knosys.2006.10.001

Cited by

  1. Using correlated volume index to support investment strategies in Kospi200 future market vol.24, pp.2, 2013, https://doi.org/10.7465/jkdi.2013.24.2.235
  2. Using genetic algorithm to optimize rough set strategy in KOSPI200 futures market vol.25, pp.2, 2014, https://doi.org/10.7465/jkdi.2014.25.2.281
  3. 경영분석지표와 의사결정나무기법을 이용한 유상증자 예측모형 개발 vol.18, pp.4, 2012, https://doi.org/10.13088/jiis.2012.18.4.059
  4. DTW를 이용한 패턴 기반 일중 price momentum 효과 분석 vol.28, pp.4, 2012, https://doi.org/10.7465/jkdi.2017.28.4.819
  5. 딥러닝을 활용한 실시간 주식거래에서의 매매 빈도 패턴과 예측 시점에 관한 연구: KOSDAQ 시장을 중심으로 vol.27, pp.3, 2018, https://doi.org/10.5859/kais.2018.27.3.123