Skip to main content
SpringerLink
Log in

Aggregating expert advice strategy for online portfolio selection with side information

  • Methodologies and Application
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

Online portfolio selection is an important fundamental problem in computational finance, which has been further developed in recent years. As the financial market changes rapidly, investors need to dynamically adjust asset positions according to various financial market information. However, existing online portfolio strategies are always designed without considering this information, which limits their practicability to some extent. To overcome this limitation, this paper exploits the available side information and presents a novel online portfolio strategy named “WAACS”. Specifically, all the constant rebalanced portfolio strategies are considered as experts and the weak aggregating algorithm is applied to aggregate all the expert advice according to their previous cumulative returns under the same side information state as the current period. Furthermore, WAACS is theoretically proved to be a universal portfolio, i.e., its growth rate is asymptotically the same as that of the best state constant rebalanced portfolio, which is a benchmark strategy considering side information. Numerical experiments show that WAACS achieves significant performance and demonstrate that considering side information improves the performance of the proposed strategy.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  • Albeverio S, Lao LJ, Zhao XL (2001) On-line portfolio selection strategy with prediction in the presence of transaction costs. Math Methods Oper Res 54:133–161

    Article  MathSciNet  Google Scholar 

  • Blum A, Kalai A (1999) Universal portfolios with and without transaction costs. Mach Learn 35:193–205

    Article  Google Scholar 

  • Borodin A, El-Yaniv R, Gogan V (2004) Can we learn to beat the best stock. J Artif Intell Res 21:579–594

    Article  MathSciNet  Google Scholar 

  • Cover TM (1991) Universal portfolios. Math Finance 1:1–29

    Article  MathSciNet  Google Scholar 

  • Cover TM, Ordentlich E (1996) Universal portfolio with side information. IEEE Trans Inf Theory 42:348–363

    Article  MathSciNet  Google Scholar 

  • Cui XY, Li D, Wang SY, Zhu SS (2012) Better than dynamic mean-variance: time inconsistency and free cash flow stream. Math Finance 22:346–378

    Article  MathSciNet  Google Scholar 

  • Fagiuoli E, Stella F, Ventura A (2007) Constant rebalanced portfolios and side-information. Quant Finance 7:161–173

    Article  MathSciNet  Google Scholar 

  • Gaivoronski AA, Stella F (2000) Stochastic nonstationary optimization for finding universal portfolios. Ann Oper Res 100:165–188

    Article  MathSciNet  Google Scholar 

  • Györfi L, Vajda I (2008) Growth optimal investment with transaction costs. In: International conference on algorithmic learning theory, pp 108–122

  • Hazan E, Kale S (2015) An online portfolio selection algorithm with regret logarithmic in price variation. Math Finance 25:288–310

    Article  MathSciNet  Google Scholar 

  • Helmbold DP, Schapire RE, Singer Y, Warmuth MK (1998) On-line portfolio selection using multiplicative updates. Math Finance 8:325–347

    Article  Google Scholar 

  • Huang DJ, Zhou JL, Li B, Hoi SCH, Zhou SG (2016) Robust median reversion strategy for on-line portfolio selection. IEEE Trans Knowl Data Eng 28:2480–2493

    Article  Google Scholar 

  • Huang DJ, Yu SC, Li B, Hoi SCH, Zhou SG (2018) Combination forecasting reversion strategy for online portfolio selection. ACM Trans Intell Syst Technol. https://doi.org/10.1145/3200692

  • Hwang I, Xu S, In F (2018) Naive versus optimal diversification: tail risk and performance. Eur J Oper Res 265:372–388

    Article  Google Scholar 

  • Kalnishkan Y, Vyugin MV (2008) The weak aggregating algorithm and weak mixability. J Comput Syst Sci 74:1228–1244

    Article  MathSciNet  Google Scholar 

  • Levina T, Levin Y, Mcgill J, Nediak M, Vovk V (2010) Weak aggregating algorithm for the distribution-free perishable inventory problem. Oper Res Lett 38:516–521

    Article  MathSciNet  Google Scholar 

  • Li B, Zhao PL, Hoi SCH, Gopalkrishnan V (2012) PAMR: passive aggressive mean reversion strategy for portfolio selection. Mach Learn 87:221–258

    Article  MathSciNet  Google Scholar 

  • Li B, Hoi SCH, Sahoo D, Liu ZY (2015) Moving average reversion strategy for on-line portfolio selection. Artif Intell 222:104–123

    Article  MathSciNet  Google Scholar 

  • Li B, Wang JL, Huang DJ, Hoi SCH (2018) Transaction cost optimization for online portfolio selection. Quant Finance 18:1411–1424

    Article  MathSciNet  Google Scholar 

  • Li B, Zhang D, Tang SH (2018) Universal portfolio selection strategy based on sub-gradient projection. J Manag Sci China 21:94–104

    Google Scholar 

  • Lin X, Zhang M, Zhang YF, Gu ZQ, Liu YQ (2017) Boosting moving average reversion strategy for online portfolio selection: a meta-learning approach. In: International conference on database systems for advanced applications, pp 494–510

  • Markowitz HM (1952) Portfolio selection. J Finance 7:77–91

    Google Scholar 

  • Nazemi A, Tahmasbi N (2014) A computational intelligence method for solving a class of portfolio optimization problems. Soft Comput 18:2101–2117

    Article  Google Scholar 

  • O’Sullivan P, Edelman D (2015) Adaptive universal portfolios. Eur J Finance 21:337–351

    Article  Google Scholar 

  • Singer Y (1997) Switching portfolios. Int J Neural Syst 8:445–455

    Article  Google Scholar 

  • Yang XY, Li HP, Zhang Y, He JA (2018) Reversion strategy for online portfolio selection with transaction costs. Int J Appl Decis Sci 11:79–99

    Google Scholar 

  • Zhang Y, Yang XY (2017) Online portfolio selection strategy based on combining experts’ advice. Comput Econ 50:141–159

    Article  Google Scholar 

  • Zhang WG, Xiao WL, Wang YL (2009) A fuzzy portfolio selection method based on possibilistic mean and variance. Soft Comput 13:627–633

    Article  Google Scholar 

  • Zhang WG, Zhang Y, Yang XY, Xu WJ (2012) A class of on-line portfolio selection algorithms based on linear learning. Appl Math Comput 218:11832–11841

    MathSciNet  MATH  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Nos. 71301029, 71501049, 71401157), the Humanities and Social Science Foundation of the Ministry of Education of China (18YJA630132), and Guangdong Province Universities and Colleges Pearl River Scholar Funded Scheme (2016).

Author information

Authors and Affiliations

  1. School of Management, Guangdong University of Technology, Guangzhou, 510520, Guangdong, People’s Republic of China

    Xingyu Yang, Jin’an He, Jiayi Xian, Hong Lin & Yong Zhang

Authors
  1. Xingyu Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jin’an He
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jiayi Xian
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hong Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yong Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yong Zhang.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Communicated by V. Loia.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yang, X., He, J., Xian, J. et al. Aggregating expert advice strategy for online portfolio selection with side information. Soft Comput 24, 2067–2081 (2020). https://doi.org/10.1007/s00500-019-04039-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-019-04039-7

Keywords

Search

Navigation