Advertisement

Multimedia Portfolio Behavior Analysis Based on Bayesian Game Algorithm

  • Hongxia LuoEmail author
Article
  • 8 Downloads

Abstract

A portfolio prediction model based on Bayesian game algorithm is proposed to improve the accuracy of correlation analysis model in portfolio application. Firstly, the portfolio problem is analyzed. The market value constraint and the upper bound constraint are combined considering the general portfolio model according to Markowitz theory, it improves the portfolio model and obtains the portfolio model with mixed constraints. Secondly, a probabilistic portfolio monitoring strategy based on incomplete information non-cooperative Bayesian game is proposed. The framework combines the enterprise investment behavior detection module with the specified rules to identify the portfolio pattern, the interaction between portfolio selection and monitored investment behavior is modeled as a two-person non-cooperative Bayesian game, which allows portfolio selection to adopt a probability monitoring strategy based on game Bayesian Nash equilibrium, thereby reducing the computational complexity of the portfolio approach introduced into the game algorithm. Finally, the effectiveness of the proposed algorithm is verified by simulation experiments.

Keywords

Bayesian Game algorithms Enterprise investment Non-cooperation Investment portfolio 

Notes

References

  1. 1.
    Goswami M, Pratap S, Kumar SK (2016) An integrated Bayesian-Game theoretic approach for product portfolio planning of a multi-attributed product in a duopolistic market[J]. Int J Prod Res 54(23):6997–7013CrossRefGoogle Scholar
  2. 2.
    Guan DJ, Guo P (2014) Constructing interdependent risks network of project portfolio based on bayesian network[C]// International Conference on Management Science & EngineeringGoogle Scholar
  3. 3.
    Hill A, Mesens N, Steemans M et al (2013) Comparisons between in vitro whole cell imaging and in vivo zebrafish-based approaches for identifying potential human hepatotoxicants earlier in pharmaceutical development[J]. Drug Metab Rev 39(4):372–376Google Scholar
  4. 4.
    Hung JC, Weng JD, Chen YH (2016) A recommendation system based on mining human portfolio for museum navigation[J]. Evol Syst 7(2):145–158CrossRefGoogle Scholar
  5. 5.
    Peña D, Poncela P (2006) Nonstationary dynamic factor analysis[J]. Journal of Statistical Planning & Inference 136(4):1237–1257MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Piotroski JD, So EC (2012) Identifying Expectation Errors in Value/Glamour Strategies: A Fundamental Analysis Approach[J]. Rev Financ Stud 25(9):2841–2875CrossRefGoogle Scholar
  7. 7.
    Rajendra Achary U, Hagiwara Y, Deshpande SN, Suren S, Koh JEW, Oh SL, Arunkumar N, Ciaccio EJ, Lim CM (Feb 2019) Characterization of Focal EEG Signals: A Review. Futur Gener Comput Syst 91:290–299CrossRefGoogle Scholar
  8. 8.
    Smith JE, Winkler RL (2006) The Optimizer's Curse: Skepticism and Postdecision Surprise in Decision Analysis[J]. Manag Sci 52(3):311–322CrossRefGoogle Scholar
  9. 9.
    Smith JE, Winterfeldt DV (2004) Decision Analysis in "Management Science"[J]. Manag Sci 50(5):561–574CrossRefGoogle Scholar
  10. 10.
    Stanhouse B (1986) Commercial Bank Portfolio Behavior and Endogenous Uncertainty[J]. J Financ 41(5):1103–1114MathSciNetCrossRefGoogle Scholar
  11. 11.
    Wang G, Simpson T (2013) Fuzzy Clustering Based Hierarchical Metamodeling for Design Optimization[J]. Eng Optim 36(3):313–335CrossRefGoogle Scholar
  12. 12.
    Zhang C, Chaudhuri K (2014) Beyond Disagreement-based Agnostic Active Learning[J]. Adv Neural Inf Proces Syst 1:442–450Google Scholar
  13. 13.
    Zhou X, Nakajima J, West M (2014) Bayesian forecasting and portfolio decisions using dynamic dependent sparse factor models[J]. Int J Forecast 30(4):963–980CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of AccountingZhejiang University of Finance and EconomicsHangzhouChina

Personalised recommendations