Abstract
Absolute deviation is a commonly used risk measure, which has attracted more attentions in portfolio optimization. Most of existing mean–absolute deviation models are devoted to stochastic single-period portfolio optimization. However, practical investment decision problems often involve the uncertain dynamic information. Considering transaction costs, borrowing constraints, threshold constraints, cardinality constraints and risk control, we present a novel multiperiod mean absolute deviation uncertain portfolio selection model, which an optimal investment policy can be generated to help investors not only achieve an optimal return, but also have a good risk control. In proposed model, the return rate of asset and the risk are quantified by uncertain expected value and uncertain absolute deviation, respectively. Cardinality constraints limit the number of risky assets in the optimal portfolio. Threshold constraints limit the amount of capital to be invested in each asset and prevent very small investments in any asset. Based on uncertainty theories, the model is transformed into a crisp dynamic optimization problem. Because of the transaction costs and cardinality constraints, the multiperiod portfolio selection is a mix integer dynamic optimization problem with path dependence, which is “NP hard” problem that is very difficult to solve. The proposed model is approximated to a mix integer dynamic programming model. A novel discrete iteration method is designed to obtain the optimal portfolio strategy and is proved linearly convergent. Finally, an example is given to illustrate the behavior of the proposed model and the designed algorithm using real data from the Shanghai Stock Exchange.
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References
Anagnostopoulos KP, Mamanis G (2011) The mean-variance cardinality constrained portfolio optimization problem: an experimental evaluation of five multiobjective evolutionary algorithms. Expert Syst Appl 38:14208–14217
Bertsimas D, Shioda R (2009) Algorithms for cardinality-constrained quadratic optimization. Comput Optim Appl 43:1–22
Cesarone F, Scozzari A, Tardella F (2013) A new method for mean–variance portfolio optimization with cardinality constraints. Ann Oper Res 205:213–234
Chen Z, Li G, Zhao Y (2014) Time-consistent investment policies in Markovian markets: a case of mean–variance analysis. J Econ Dyn Control 40(1):293–316
Chen Z, Liu J, Li G, Yan Z (2016) Composite time-consistent multi-period risk measure and its application in optimal portfolio selection. TOP 24(3):515–540
Cui XY, Li D, Wang SY, Zhu SS (2012) Better than dynamic mean–variance: time inconsistency and free cash flow stream. Math Finance 22(2):346–378
Cui XT, Zheng XJ, Zhu SS, Sun XL (2013) Convex relaxations and MIQCQP reformulations for a class of cardinality-constrained portfolio selection problems. J Global Optim 56:1409–1423
Cui XY, Li X, Li D (2014) Unified framework of mean-field formulations for optimal multi-period mean–variance portfolio selection. IEEE Trans Autom Control 59(7):1833–1844
Cui X, Li D, Li X (2017) Mean variance policy for discrete time cone-constrained markets: time consistency in efficiency and the minimum-variance signed supermartingale measure. Math Finance 27(2):471–504
Deng GF, Lin WT, Lo CC (2012) Markowitz-based portfolio selection with cardinality constraints using improved particle swarm optimization. Expert Syst Appl 39:4558–4566
Fernández A, Gómez S (2007) Portfolio selection using neural networks. Comput Oper Res 34:1177–1191
Gao JJ, Li D, Cui XY, Wang SY (2015) Time cardinality constrained mean–variance dynamic portfolio selection and market timing: a stochastic control approach. Automatica 54(C):91–99
Gülpınar N, Rustem B (2007) Worst-case robust decisions for multi-period mean–variance portfolio optimization. Eur J Oper Res 183(3):981–1000
Huang X (2008) Mean–semivariance models for fuzzy portfolio selection. J Comput Appl Math 217:1–8
Huang X (2012) A risk index model for portfolio selection with returns subject to experts’ estimations. Fuzzy Optim Decis Mak 11(4):451–463
Huang X, Qiao L (2012) A risk index model for multi-period uncertain portfolio selection. Inf Sci 217:108–116
Heidergott B, Olsder GJ, Woude JV (2006) Max plus at work-modeling and analysis of synchronized systems: a course on max-plus algebra and its applications. Princeton University Press, Princeton
Konno H, Yamazaki H (1991) Mean absolute portfolio optimization model and its application to Tokyo stock market. Manag Sci 37(5):519–531
Köksalan M, Şakar CT (2016) An interactive approach to stochastic programming-based portfolio optimization. Ann Oper Res 245(2):47–66
Le Thi HA, Moeini M (2014) Long-short portfolio optimization under cardinality constraints by difference of convex functions algorithm. J Optim Theory Appl 161:199–224
Le Thi HA, Moeini M, Dinh TP (2009) Portfolio selection under downside risk measures and cardinality constraints based on DC programming and DCA. CMS 6:459–475
Li CJ, Li ZF (2012) Multi-period portfolio optimization for asset-liability management with bankrupt control. Appl Math Comput 218:11196–11208
Li D, Ng WL (2000) Optimal dynamic portfolio selection: multiperiod mean–variance formulation. Math Finance 10(3):387–406
Li D, Sun X, Wang J (2006) Optimal lot solution to cardinality constrained mean–variance formulation for portfolio selection. Math Finance 16:83–101
Li X, Qin Z, Kar S (2010) Mean–variance–skewness model for portfolio selection with fuzzy returns. Eur J Oper Res 202:239–247
Liu B (2007) Uncertainty theory, 2nd edn. Springer, Berlin
Liu B (2009) Some research problems in uncertainty theory. J Uncertain Syst 3(1):3–10
Liu YJ, Zhang WG, Xu WJ (2012) Fuzzy multi-period portfolio selection optimization models using multiple criteria. Automatica 48:3042–3053
Liu YJ, Zhang WG, Zhang P (2013) A multi-period portfolio selection optimization model by using interval analysis. Econ Model 33:113–119
Mansini R, Ogryczak W, Speranza MG (2007) Conditional value at risk and related linear programming models for portfolio optimization. Ann Oper Res 152:227–256
Markowitz HM (1952) Portfolio selection. J Finance 7:77–91
Markowitz HM (1959) Portfolio selection: efficient diversification of investments. Wiley, New York
Mehlawat MK (2016) Credibilistic mean-entropy models for multi-period portfolio selection with multi-choice aspiration levels. Inf Sci 345:9–26
Murray W, Shek H (2012) A local relaxation method for the cardinality constrained portfolio optimization problem. Comput Optim Appl 53:681–709
Qin Z (2017) Random fuzzy mean-absolute deviation models for portfolio optimization problem with hybrid uncertainty. Appl Soft Comput 56:597–603
Qin Z, Kar S (2013) Single-period inventory problem under uncertain environment. J Appl Math Comput 219(18):9630–9638
Qin Z, Wen M, Gu C (2011) Mean-absolute deviation portfolio selection model with fuzzy returns. Iran J Fuzzy Syst 8:61–75
Ruiz-Torrubiano R, Suarez A (2010) Hybrid approaches and dimensionality reduction for portfolio selection with cardinality constrains. IEEE Comput Intell Mag 5:92–107
Sadjadi SJ, Seyedhosseini SM, Hassanlou K (2011) Fuzzy multi period portfolio selection with different rates for borrowing and lending. Appl Soft Comput 11:3821–3826
Shaw DX, Liu S, Kopman L (2008) Lagrangian relaxation procedure for cardinality-constrained portfolio optimization. Optim Methods Softw 23:411–420
Speranza MG (1993) Linear programming model for portfolio optimization. Finance 14:107–123
Sun XL, Zheng XJ, Li D (2013) Recent advances in mathematical programming with semi-continuous variables and cardinality constraint. J Oper Res Soc China 1:55–77
van Binsbergen JH, Brandt M (2007) Solving dynamic portfolio choice problems by recursing on optimized portfolio weights or on the value function? Comput Econ 29:355–367
Vercher E, Bermudez J, Segura J (2007) Fuzzy portfolio optimization under downside risk measures. Fuzzy Sets Syst 158:769–782
Woodside-Oriakhi M, Lucas C, Beasley JE (2011) Heuristic algorithms for the cardinality constrained efficient frontier. Eur J Oper Res 213:538–550
Wu HL, Li ZF (2012) Multi-period mean–variance portfolio selection with regime switching and a stochastic cash flow. Insur Math Econ 50:371–384
Wu H, Zeng Y (2015) Equilibrium investment strategy for defined-contribution pension schemes with generalized mean–variance criterion and mortality risk. Insur Math Econ 64:396–408
Yan W, Li SR (2009) A class of multi-period semi-variance portfolio selection with a four-factor futures price model. J Appl Math Comput 29:19–34
Yan W, Miao R, Li SR (2007) Multi-period semi-variance portfolio selection: model and numerical solution. Appl Math Comput 194:128–134
Yao K, Ji X (2014) Uncertain decision making and its application to portfolio selection problem. Int J Uncertain Fuzziness Knowl-Based Syst 22(1):113–123
Yu M, Takahashi S, Inoue H, Wang SY (2010) Dynamic portfolio optimization with risk control for absolute deviation model. Eur J Oper Res 201(2):349–364
Yu M, Wang SY (2012) Dynamic optimal portfolio with maximum absolute deviation model. J Global Optim 53:363–380
Zadeh LA (1965) Fuzzy sets. Inf Control 8:338–353
Zhang WG, Liu YJ (2014) Credibilitic mean-variance model for multi-period portfolio selection problem with risk control. OR Spectrum 36:113–132
Zhang P, Zhang WG (2014) Multiperiod mean absolute deviation fuzzy portfolio selection model with risk control and cardinality constraints. Fuzzy Sets Syst 255:74–91
Zhang WG, Liu YJ, Xu WJ (2012) A possibilistic mean–semivariance-entropy model for multi-period portfolio selection with transaction costs. Eur J Oper Res 222:41–349
Zhang WG, Liu YJ, Xu WJ (2014) A new fuzzy programming approach for multi-period portfolio optimization with return demand and risk control. Fuzzy Sets Syst 246:107–126
Zhou Z, Xiao H, Yin J, Zeng X, Lin L (2016) Pre-commitment vs. time-consistent strategies for the generalized multi-period portfolio optimization with stochastic cash flows. Insur Math Econ 68:187–202
Zhu Y (2010) Uncertain optimal control with application to a portfolio selection model. Cybern Syst 41(7):535–547
Zhu SS, Li D, Wang SY (2004) Risk control over bankruptcy in dynamic portfolio selection: a generalized mean–variance formulation. IEEE Trans Autom Control 49(3):447–457
Acknowledgements
This research was supported by the National Natural Science Foundation of China (No. 71271161).
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Appendices
Appendix A
The codes of thirty stocks are, respectively, \(\hbox {S}_{1}\) (600,000), \(\hbox {S}_{2}\) (600,005), \(\hbox {S}_{3}\) (600,015), \(\hbox {S}_{4}\) (600,016), \(\hbox {S}_{5}\) (600,019), \(\hbox {S}_{6}\) (600,028), \(\hbox {S}_{7}\) (600,030), \(\hbox {S}_{8}\) (600,036), \(\hbox {S}_{9}\) (600,048), \(\hbox {S}_{10}\) (600,050), \(\hbox {S}_{11}\) (600,104), \(\hbox {S}_{12}\) (600,362), \(\hbox {S}_{13}\) (600,519), \(\hbox {S}_{14}\) (600,900), \(\hbox {S}_{15}\) (601,088), \(\hbox {S}_{16}\) (601,111), \(\hbox {S}_{17}\) (601,166), \(\hbox {S}_{18}\) (601,168), \(\hbox {S}_{19}\) (601,318), \(\hbox {S}_{20}\) (601,328), \(\hbox {S}_{21}\) (601,390), \(\hbox {S}_{22}\) (601,398), \(\hbox {S}_{23}\) (601,600), \(\hbox {S}_{24}\) (601,601), \(\hbox {S}_{25}\) (601,628), \(\hbox {S}_{26}\) (601,857), \(\hbox {S}_{27}\) (601,919), \(\hbox {S}_{28}\) (601,939), \(\hbox {S}_{29}\) (601,988), \(\hbox {S}_{30}\) (601,998). The triangle uncertain distributions, \(\xi _{it }=(a_{it}, \alpha _{it}, \beta _{it})\), of the return rates of assets at each period can be obtained as shown in Tables 5, 6, 7, 8, 9, 10, 11, 12, 13 and 14.
Appendix B
According Tables 5, 6, 7, 8, 9, 10, 11, 12, 13 and 14, and Eq. (25), \(\mathrm{AD}_{t}(R_{it}) (i = 1,{\ldots },30; {t} = 1,{\ldots },5)\) can be obtained as shown in Tables 15, 16, 17 and Table 18.
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Zhang, P. Multiperiod mean absolute deviation uncertain portfolio selection with real constraints. Soft Comput 23, 5081–5098 (2019). https://doi.org/10.1007/s00500-018-3176-z
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DOI: https://doi.org/10.1007/s00500-018-3176-z