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Yungho Leu and Tzu-I Chiu National Taiwan University of Science and Technology Taiwan, Taipei

An Effective Stock Portfolio Trading Strategy using Genetic Algorithms and Weighted Fuzzy Time Series. Yungho Leu and Tzu-I Chiu National Taiwan University of Science and Technology Taiwan, Taipei. Outline. Problem definition Related work Our approach Experiments Conclusion.

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Yungho Leu and Tzu-I Chiu National Taiwan University of Science and Technology Taiwan, Taipei

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  1. An Effective Stock Portfolio Trading Strategy using Genetic Algorithms and Weighted Fuzzy Time Series Yungho Leu and Tzu-I Chiu National Taiwan University of Science and Technology Taiwan, Taipei

  2. Outline • Problem definition • Related work • Our approach • Experiments • Conclusion NASNIT, October 24-26, 2011, Maca

  3. Problem Definition • Portfolios: • A portfolio is a linear (weighted) combination of a set of securities aiming at minimizing risk with a given level of return rate. NASNIT, October 24-26, 2011, Maca

  4. Problem Definition • Markowitz mean-variance Portfolio model: , Risk Return Where ri is the return rate of security i, ij is the covarance of ri and rj; w is called the risk aversion factor NASNIT, October 24-26, 2011, Maca

  5. Problem withMarkowitz model • Need to consider minimal transaction lots. • Does not consider when to re-construct (to sell one and to buy a new one) a new portfolio. • Many methods have been proposed for porfolio construction. • The minimal transaction lots problem is receiving more attention recently. NASNIT, October 24-26, 2011, Maca

  6. Our approach • We propose a method to re-construct (to sell one and to buy a new one) portfolio. • We use Genetic Algorithm to construct new portfolios. • We use Fuzzy Time Seriesto predict the return rate of a portfolio. • We incorporate a stop-lose point policy in the method. NASNIT, October 24-26, 2011, Maca

  7. Some related works • Constructing investment strategy portfolios by combination genetic algorithms (Chen et al. ,2009). • Automatic stock decision support system based on box theory and SVM algorithm(Wen et al. ,2010). NASNIT, October 24-26, 2011, Maca

  8. Predict Stock Return rate • Construct Fuzzy Logical Relationship database. • Find the most recent FLRs. • Construct FLR group. • Assign weight to each FLR in FLR group. • Predict the stock price. NASNIT, October 24-26, 2011, Maca

  9. An Illustrative Example 1.The most recent FLRs : (t=1) A1 →A1 , (t=2) A1 →A2 , (t=3) A2 →A1 , (t=4) A1 →A1 , (t=5) A1 →A1 ,  2. Assign a weight to FLR： (t=1) A1 →A1 with weight 1, (t=2) A1→A2 with weight 2, (t=4) A1 →A1 with weight 3, (t=5) A1 →A1 with weight 4, Construct FLR Group(FLRG ) (Left-hand-side of day 5 is A1) ： A1 →A1, A2 , A1, A Defuzzify FLRG: Ai’ is the corresponding value of fuzzy set Ai Predicted value of day 6=

  10. The Genetic Algorithm • Encoding: • Stock numbering:1~50 • Allocation number：1~100 NASNIT, October 24-26, 2011, Maca

  11. The Genetic Algorithm • Single-point Crossover: parents Offsprings NASNIT, October 24-26, 2011, Maca

  12. The Genetic Algorithm • Single-point mutation NASNIT, October 24-26, 2011, Maca

  13. The Genetic Algorithm • Fitness function: Fi(t+n): the predicted closing price of stock i at day t+n Ni(t): theclosing price of stock i at day t Ci : theweight of stock i NASNIT, October 24-26, 2011, Maca

  14. Portfolio trading • Trade the current portfolio when a new portfolio has a higher expected return rate. Hold for 5 days 5days later Dayt Day t+5 Predict the return rate of S and the return rate of the new portfolio S’ from GA at day t+10 Buy portfolio S If the return rate of S is greater than that of S’ then hold S else sell S and buy S’ NASNIT, October 24-26, 2011, Maca

  15. Stop-lose point policy • Keep tracking the return rate of the portfolio, when it reaches the stop-lose point, sell it. Buy a new portfolio at date t+5 Periodically Check stop-lose point within 5 days t t+3 t+4 t+5 If the return rate is less than the stop-lose point (-7%), sell the portfolio Buy a stock portfolio Buy a new portfolio NASNIT, October 24-26, 2011, Maca

  16. Experiment setting NASNIT, October 24-26, 2011, Maca

  17. Experiment results • The return rates of the proposed methods are higher than the benchmarking indices, with or without considering transaction cost. *-denotes with transaction cost NASNIT, October 24-26, 2011, Maca

  18. Experiment results • The return rate of the proposed method with transaction cost is better than that of the buy and hold. NASNIT, October 24-26, 2011, Maca

  19. Conclusion • We propose an effective method to trade security portfolios using GA and Fuzzy Time Series. • The future work is to consider limitations such as transaction lots and the risks of portfolios. NASNIT, October 24-26, 2011, Maca

  20. Q&A NASNIT, October 24-26, 2011, Maca

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