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Developing a dynamic portfolio selection model with a self-adjusted rebalancing method

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Journal of the Operational Research Society

Abstract

In this paper, we propose a comprehensive investment strategy for not only selecting but also maintaining an investment portfolio that takes into account changing market conditions. First, we implement a dynamic portfolio selection model (DPSM) that uses a time-varying investment target according to market forecasts. We then develop a self-adjusted rebalancing (SAR) method to assess the portfolio’s relevance to current market conditions, and further identify the appropriate timing for rebalancing the portfolio. We then integrate the DPSM and SAR into a comprehensive investment strategy, and develop an adaptive learning heuristic for determining the parameter of the proposed investment strategy. We further evaluate the performance of the proposed investment strategy by simulating investments with historical stock return data from different markets around the world, across a period of 10 years. The SAR Portfolio, maintained according to the proposed investment strategy, showed superior performance compared with benchmarks in each of the target markets.

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References

  • Best MJ, Grauer RR (1991) On the sensitivity of mean-variance-efficient portfolios to changes in asset means: Some analytical and computational results. The Review of Financial Studies 4(2):315–342

    Article  Google Scholar 

  • Biglova A, Ortobelli S, Rachev ST, Stoyanov S (2004) Different approaches to risk estimation in portfolio theory. The Journal of Portfolio Management 31(1):103–112

    Article  Google Scholar 

  • Bodie Z, Kane A, Marcus AJ (2011) Investments, 9th edn. New York, McGraw-Hill

    Google Scholar 

  • Bonami P, Lejeune MA (2009) An exact solution approach for portfolio optimization problems under stochastic and integer constraints. Operations Research 57(3):650–670

    Article  Google Scholar 

  • Broadie M (1993) Computing efficient frontiers using estimated parameters. Annals of Operations Research 45(1):21–58

    Article  Google Scholar 

  • Brown RG (1959) Statistical Forecasting for Inventory Control. McGraw-Hill, New York

    Google Scholar 

  • Bruni R, Cesarone F, Scozzari A, Tardella F (2013) No arbitrage and a linear portfolio selection model. Economics Bulletin 33(2):1247–1258

    Google Scholar 

  • Cesarone F, Scozzari A, Tardella F (2013) A new method for mean-variance portfolio optimization with cardinality constraints. Annals of Operations Research 205(1):213–234

    Article  Google Scholar 

  • Chan L, Karceski J, Lakonishok J (1999) On portfolio optimization: Forecasting covariances and choosing the risk model. Review of Financial Studies 12(5):937–974

    Article  Google Scholar 

  • Chen AHY, Jen FC, Zionts S (1971) The optimal portfolio revision policy. The Journal of Business 44(1):51–61

    Article  Google Scholar 

  • Chincarini L, Kim D (2006) Quantitative Equity Portfolio Management: An Active Approach to Portfolio Construction and Management. McGraw-Hill, New York, pp 256–267

    Google Scholar 

  • Chopra VK, Ziemba WT (1993) The effect of errors in means, variances, and covariances on optimal portfolio choice. Journal of Portfolio Management 19(2):6–11

    Article  Google Scholar 

  • Corazza M, Favaretto D (2007) On the existence of solutions to the quadratic mixed-integer mean-variance portfolio selection problem. European Journal of Operational Research 176(3):1947–1960

    Article  Google Scholar 

  • Davis MHA, Norman AR (1990) Portfolio selection with transaction costs. Mathematics of Operations Research 15(4):676–713

    Article  Google Scholar 

  • DeMiguel V, Garlappi L, Nogales FJ, Uppal R (2009a) A generalized approach to portfolio optimization: Improving performance by constraining portfolio norms. Management Science 55(5):798–812

    Article  Google Scholar 

  • DeMiguel V, Garlappi L, Uppal R (2009b) Optimal versus naive diversification: How inefficient is the 1/N portfolio strategy? Review of Financial Studies 22(5):1915–1953

    Article  Google Scholar 

  • DeMiguel V, Nogales FJ (2009) Portfolio selection with robust estimation. Operations Research 57(3):560–577

    Article  Google Scholar 

  • Donohue C, Yip K (2003) Optimal portfolio rebalancing with transaction costs. The Journal of Portfolio Management 29(4):49–63

    Article  Google Scholar 

  • Duchin R, Levy H (2009) Markowitz versus the Talmudic portfolio diversification strategies. The Journal of Portfolio Management 35(2):71–74

    Article  Google Scholar 

  • El Ghaoui L, Oks M, Oustry F (2003) Worst-case value-at-risk and robust portfolio optimization: A conic programming approach. Operations Research 51(4):543–556

    Article  Google Scholar 

  • Elton EJ, Gruber MJ (1997) Modern portfolio theory, 1950 to date. Journal of Banking & Finance 21(11–12):1743–1759

    Article  Google Scholar 

  • Elton EJ, Gruber MJ, Padberg MW (1976) Simple criteria for optimal portfolio selection. The Journal of Finance 31(5):1341–1357

    Article  Google Scholar 

  • Fang Y, Lai KK, Wang S-Y (2006) Portfolio rebalancing model with transaction costs based on fuzzy decision theory. European Journal of Operational Research 175(2):879–893

    Article  Google Scholar 

  • Gardner ES Jr (1983) Automatic monitoring of forecast errors. Journal of Forecasting 2(1):1–21

    Article  Google Scholar 

  • Gardner ES Jr (1985) Exponential smoothing: The state of the art. Journal of Forecasting 4(1):1–28

    Article  Google Scholar 

  • Gardner ES Jr (2006) Exponential smoothing: The state of the art Part II. International Journal of Forecasting 22(4):637–666

    Article  Google Scholar 

  • Garlappi L, Uppal R, Wang T (2006) Portfolio selection with parameter and model uncertainty: A multi-prior approach. Review of Financial Studies 20(1):41–81

    Article  Google Scholar 

  • Goldfarb D, Iyengar G (2003) Robust portfolio selection problems. Mathematics of Operations Research 28(1):1–38

    Article  Google Scholar 

  • Hillier FS, Hillier MS (2013) Introduction to Management Science: A Modeling and Case Studies Approach with Spreadsheets, 5th edn. New York, McGraw-Hill

    Google Scholar 

  • Hillier FS, Lieberman GJ (2010) Introduction to Operations Research, 9th edn. New York, McGraw-Hill

    Google Scholar 

  • Hui T, Kwan EK, Lee C (1993) Optimal portfolio diversification: Empirical bayes versus classical approach. The Journal of the Operational Research Society 44(11):1155–1159

    Article  Google Scholar 

  • Interactive Brokers (2014). Commissions, http://individuals.interactive-brokers.com/en/p.php?f=commission, accessed 9 January 2013.

  • Jagannathan R, Ma T (2003) Risk reduction in large portfolios: Why imposing the wrong constraints helps. The Journal of Finance 58(4):1651–1683

    Article  Google Scholar 

  • Jung J, Kim S (2015) An adaptively managed dynamic portfolio selection model using a time-varying investment target according to the market forecast. Journal of the Operational Research Society 66(7):1115–1131

    Article  Google Scholar 

  • Kamin JH (1975) Optimal portfolio revision with a proportional transaction cost. Management Science 21(11):1263–1271

    Article  Google Scholar 

  • Konno H, Yamamoto R (2005) Integer programming approaches in mean-risk models. Computational Management Science 2(4):339–351

    Article  Google Scholar 

  • Konno H, Yamazaki H (1991) Mean-absolute deviation portfolio optimization model and its applications to Tokyo stock market. Management Science 37(5):519–531

    Article  Google Scholar 

  • Markowitz H (1952) Portfolio selection. Journal of Finance 7(1):77–91

    Google Scholar 

  • McClain JO (1988) Dominant tracking signals. International Journal of Forecasting 4(4):563–572

    Article  Google Scholar 

  • Merton RC (1980) On estimating the expected return on the market: An exploratory investigation. Journal of Financial Economics 8(4):323–361

    Article  Google Scholar 

  • Michaud RO (1989) The Markowitz optimization Enigma: Is ‘optimized’optimal? Financial Analysts Journal 45(1):31–42

    Article  Google Scholar 

  • Pantaleo E, Tumminello M, Lillo F, Mantegna RN (2011) When do improved covariance matrix estimators enhance portfolio optimization? An empirical comparative study of nine estimators. Quantitative Finance 11(7):1067–1080

    Article  Google Scholar 

  • Sharpe WF (1966) Mutual fund performance. The Journal of Business 39(1):119–138

    Google Scholar 

  • Sharpe WF (1994) The Sharpe ratio. The Journal of Portfolio Management 21(1):49–58

    Article  Google Scholar 

  • Trigg DW (1964) Monitoring a forecasting system. Operational Research Quarterly 15(3):271–274

    Article  Google Scholar 

  • Tütüncii RH, Koenig M (2004) Robust asset allocation. Annals of Operations Research 132(1):157–187

    Article  Google Scholar 

  • Winkler RL, Barry CB (1975) A Bayesian model for portfolio selection and revision. The Journal of Finance 30(1):179–192

    Article  Google Scholar 

Download references

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Correspondence to Seongmoon Kim.

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Jung, J., Kim, S. Developing a dynamic portfolio selection model with a self-adjusted rebalancing method. J Oper Res Soc 68, 766–779 (2017). https://doi.org/10.1057/jors.2016.21

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  • DOI: https://doi.org/10.1057/jors.2016.21

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