The Sharpe ratio is a common financial performance measure that represents the optimal risk versus return of an investment portfolio, also defined as the slope of the capital market line within the mean-variance Markowitz efficient frontier. Obtaining sample point and confidence interval estimates for this metric is challenging due to both its dynamic nature and issues surrounding its statistical properties. Given the importance of obtaining robust determinations of risk versus return within financial portfolios, the purpose of the current research was to improve the statistical estimation error associated with Sharpe’s ratio, offering an approach to point and confidence interval estimation which employs bootstrap resampling and computational intelligence. | An estimation error corrected sharpe ratio using Bootstrap resampling
