Dynamic Portfolio Speculation via an Informationally More Structured Ito Process

Although theories over portfolio speculation have made remarkable progress so far, the performance of its proposed portfolios depends largely on the degree of accuracy in predicting future stocks prices dynamics. This study focuses on improving the performance of optimal portfolios by modeling the stocks prices dynamics through a time-inconsistent multivariate diffusion specification with drift vector. To this end, the share prices are simulated by means of a semi-martingale process with time inconsistent (local) martingale and information drift parts over the entire optimization horizon. Then, using the results of price simulation, we have looked into its consequences for constructing the portfolio of assets in the framework of Sharpe ratio maximization method and mean-variance analysis. Findings indicate that for the stock market under study (Tehran) within the trading dates spanning the interval 24-Mar-2001 to 19-Sep-2020, return and risk (standard deviation) of the portfolios obtained from applying this simulation scheme for mean-variance analysis and maximization of Sharpe ratio are both respectively higher and lower than those realized by the conventional methods. Additionally, a comparison of the simulation approach with performance of the actual market portfolios indicates that the Sharpe ratios of the simulation method is higher than those resulting from the market portfolios.