DOI QR코드

DOI QR Code

Stock Trading Model using Portfolio Optimization and Forecasting Stock Price Movement

포트폴리오 최적화와 주가예측을 이용한 투자 모형

  • 박강희 (아주대학교 산업공학과) ;
  • 신현정 (아주대학교 산업공학과)
  • Received : 2012.12.06
  • Accepted : 2013.06.10
  • Published : 2013.12.15

Abstract

The goal of stock investment is earning high rate or return with stability. To accomplish this goal, using a portfolio that distributes stocks with high rate of return with less variability and a stock price prediction model with high accuracy is required. In this paper, three methods are suggested to require these conditions. First of all, in portfolio re-balance part, Max-Return and Min-Risk (MRMR) model is suggested to earn the largest rate of return with stability. Secondly, Entering/Leaving Rule (E/L) is suggested to upgrade portfolio when particular stock's rate of return is low. Finally, to use outstanding stock price prediction model, a model based on Semi-Supervised Learning (SSL) which was suggested in last research was applied. The suggested methods were validated and applied on stocks which are listed in KOSPI200 from January 2007 to August 2008.

Keywords

References

  1. Amilon, H. (2003), GARCH estimation and discrete stock prices : an application to low-priced Australian stocks, Economics Letters, 81(2), 215-222. doi : 10.1016/S0165-1765(03)00172-1.
  2. Andersen, E. D., Dahl, J., and Friberg, H. A. (2009), Markowitz portfolio optimization using MOSEK, MOSEK Technical Report, 2, 1-30.
  3. Barber, B., Lehavy, R., Mcnichols, M., and Trueman, B. (2001), Can Investors Profit from the Prophets? Security Analyst Recommendations and Stock Returns, The Journal of Finance, 1, 531-563.
  4. Bekhet, H. A. and Matar, A. (2012), Risk-Adjusted Performance : A two-mdel Approach Application in Amman Stock Exchage, International Journal of Business and Social Science, 3(7), 34-45.
  5. Bekiros, S. and Georgoutsos, D. (2008), Direction-of-Change Forecasting using a Volatility-Based Recurrent Neural Network, Journal of Forecasting, 27, 407-417. https://doi.org/10.1002/for.1063
  6. Belkin, M., Matveeva, I., and Niyogi, P. (2003), Regression and Regularization on Large, In : Shawe-Taylor, J., Singer, Y. (eds.) COLT 2004. LNCS (LNAI), 3120, 624-638.
  7. Belkin, M. and Niyogi, P. (2004), Semi-Supervised Learning on Riemannian Manifolds, Machine Learning, 56, 209-239. https://doi.org/10.1023/B:MACH.0000033120.25363.1e
  8. Brodie, J., Daubechies, I., Mol, C. D., Giannone, D., and Loris, I. (2008), Sparse and stable Markowitz portfolios Working paper series: European Central Bank.
  9. Hillier, F. S. and Hillier, M. S. (2008), Introduction to Management Science.
  10. Jeantheau, T. (2004), A link between complete models with stochastic volatility and ARCH models, Finance Stochast, 8, 111-131. doi : 10.1007/s00780-003-0103-6.
  11. Kanas, A. (2003), Non-linear forecasts of stock returns, Journal of Forecasting, 22(4), 299-315. doi : 10.1002/for.858.
  12. Kim, D. S. and Ryoo, H. S. (2007), Portfolio Management Using Statistical Process Control Chart, IE Interfaces, 20(2), 94-102.
  13. Kim, K.-J. (2003), Financial time series forecasting using supportn vector machines, Neurocomputing, 55, 307-319. https://doi.org/10.1016/S0925-2312(03)00372-2
  14. Kim, K.-J. (2006), Artificial neural networks with evolutionary instance selection for financial forecasting, Expert Systems with Applications, 30, 519-526. https://doi.org/10.1016/j.eswa.2005.10.007
  15. Kong, M. and Kim, J. (2012), The Study on Volatility in Stock Market, Korean Journal of Business Administration, 25(2), 953-969.
  16. Liu, Q., Sung, A. H., Chen, Z., Liu, J., Huang, X., and Deng, Y. (2009), Feature Selection and Classification of MAQC-II Breast Cancer and Multiple Myeloma Microarray Gene Expression Data, MAQC-II Gene Expression, 4(12), 1-24.
  17. M, O. C., W, R., and K, G. (2000), Does updating judgmental forecasts improve forecast accuracy?, International Journal of Forecasting, 16(1), 101-109. https://doi.org/10.1016/S0169-2070(99)00039-4
  18. Markowitz, H. (1952), Portfolio Selection, The Journal of Finance, 7(1), 77-91.
  19. Narayan, P. K. and Narayan, S. (2010), Modelling the impact of oil prices on Vietnam's stock prices, Applied Energy.
  20. Park, K., Hou, T., and Shin, H. (2011), Oil Price Forecasting Based on Machine Learning Techniques, Journal of the Korean Institute of Industrial Engineers, 37(1), 64-73. https://doi.org/10.7232/JKIIE.2011.37.1.064
  21. Park, K. and Shin, H. (2013), Stock Price Prediction based on a Complex Interrelation Network of Economic Factors, Engineering Applications of Artificial Intelligence, 26(5-6), 1550-1561. https://doi.org/10.1016/j.engappai.2013.01.009
  22. Ryoo, H. (2007), A compact mean-variance-skewness model for large-scale portfolio optimization and its application to the NYSE market, Journal of the Operational Research Society, 58, 505-515. doi : 10.1057/palgrave.jors.2602168.
  23. Salem, R., Shaher, T. A., and Khasawneh, O. (2011), International Portfolio Diversification Benefits for Middle Eastern Investors, Journal of Money, Investment and Banking, 22, 22-31.
  24. Seidl, I. (2012), Markowitz versus regime switching : an empirical approach, The review of finance and banking, 4(1), 033-043.
  25. Shin, H., Hill, N. J., Lisewski, A. M., and Park, J.-S. (2010), Graph sharpening, Expert Systems with Applications, 37(12), 7870-7879. doi : 10.1016/j.eswa.2010.04.050.
  26. Shin, H., Lisewski, A. M., and Lichtarge, O. (2007), Graph sharpening plus graph integration: a synergy that improves protein functional classification, Bioinformatics, 23, 3217-3224. doi : 10.1093/bioinformatics/btm511.
  27. Steinbach, M. C. (2001), Markowitz Revisited : Mean-Variance Models in Financial Portfolio Analysis, Society for Industrial and Applied Mathematics, 43(1), 31-85.
  28. Tay, F. E. H. and Cao, L. (2001), Application of support vector machines in financial time series forecasting, Omega, 29(4), 309-317. doi: 10.1016/S0305-0483(01)00026-3.
  29. Yang, B., Li, L. X., and Xu, J. (2001), An early warning system for loan risk assessment using artificial neural networks, Knowledge-Based Systems, 14(5-6), 303-306. https://doi.org/10.1016/S0950-7051(01)00110-1
  30. Zhou, D., Bousquet, O., Lal, T. N., Weston, J., and Scholkopf, B. (2004), Learning with Local and Global Consistency, Advances in Neural Information Processing Systems, 16, 321-328.