The Bias ratio: An effective fraud identification tool

Abstract

Financial fraud poses significant risks with far-reaching consequences, particularly in the context of growing assets under management and expanding equity markets. This thesis underscores the urgent need for robust measures to safeguard investors from fraudulent activities by exploring the consequences of notorious fraud cases such as Bernie Madoff’s Ponzi scheme. Through analyses of hedge fund, index fund and stock price return data in the US and SA, over various periods starting in 1997 to 2024, it becomes evident that tools such as the Bias ratio, kurtosis, and skewness can serve as effective mechanisms for detecting fraudulent behaviour. The Bias ratio emerges as a dual-purpose tool. Beyond its fraud detection capabilities, it functions as a performance measurement metric akin to the Sharpe ratio, offering additional value during security analysis. By highlighting suspicious historical outperformance and signalling securities with unusual performance patterns, the Bias ratio enriches the evaluation process, enabling investors to make informed decisions and avoid fraudulent investments. This thesis demonstrates the efficacy of the Bias ratio by examining its application in the notorious Madoff case, where it successfully flagged fraudulent activity that was overlooked by traditional measures like the Sharpe ratio. The findings emphasize the critical role of the Bias ratio in validating the legitimacy of returns and enhancing investor protection.