Performance enhancement through portfolio optimization of delayed insider information: An analysis and implementation study

This paper addresses the classical problem of portfolio optimization in a financial market where an insider trader with privileged information exists, along with a delay in the information flow. The paper calculates the wealth evolution process and determines the optimal portfolio that maximizes the expected final wealth for various well-known financial models, including Black–Scholes-Merton, Heston, Vasicek, Hull–White, and Cox-Ingersoll-Ross. The proposed approach employs anticipating stochastic calculus and white noise theory. A comparison is made between the performance of the insider trader and the honest trader. Our findings suggest that, in general, the insider trader achieves greater wealth than the honest trader despite the delay in the insider information, and this trend holds true across all models. Furthermore, realistic financial market simulations are implemented to substantiate and showcase the findings.