Dang, Jing

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Dang, Jing
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Dang, Jing

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  • Publication
    Swarm intelligent optimisation based stochastic programming model for dynamic asset allocation
    (IEEE Press, 2010-07)
    Dang, Jing 
    ;
    Edelman, David 
    ;
    Hochreiter, Ronald 
    ;
    Brabazon, Anthony 
    Asset allocation is critical for the portfolio management process. In this paper, we solve a dynamic asset allocation problem through a multiperiod stochastic programming model. The objective is to maximise the expected utility of wealth at the end of the planning periods. To improve the optimisation result of the model, we employ swarm intelligent optimisers, the Bacterial Foraging Optimisation (BFO) algorithm and the Particle Swarm Optimisation (PSO) algorithm. A hybrid optimiser using the Bacterial Foraging Optimisation algorithm for initialisation and the Sequential Quadratic Programming (SQP) for local search is also suggested. The results are compared with the standard-alone SQP and the canonical Genetic Algorithm. The numerical results suggest the hybrid method provides better result, with improved accuracy, stability and computing speed than using BFO, PSO, GA, or SQP alone.
      2509Scopus© Citations 3
  • Publication
    Natural computing in finance : a review
    (Springer, 2012)
    Brabazon, Anthony 
    ;
    Dang, Jing 
    ;
    Dempsey, Ian 
    ;
    O'Neill, Michael 
    ;
    Edelman, David 
    The field of Natural Computing (NC) has advanced rapidly over the past decade. One significant offshoot of this progress has been the application of NC methods in finance. This chapter provides an introduction to a wide range of financial problems to which NC methods have been usefully applied. The chapter also identifies open issues and suggests future directions for the application of NC methods in finance.
      2908Scopus© Citations 6