Investigating the low-risk anomaly in South Africa. (1st January 2021)

• Record Type: Journal Article
• Authors: Seetharam, Yudhvir
• Is Part Of: Review of behavioral finance. Volume 14:Number 2(2022)
• Page Start: 277
• Page End: 295
• Publication Date: 2021-01-01
• Subjects: Low-risk anomaly -- Kalman filter beta -- Downside risk -- South Africa -- Non-linearity of risk and return -- Hurst exponent; Investments -- Psychological aspects -- Periodicals; Investments -- Decision making -- Periodicals; 332.605
• DOI: 10.1108/RBF-07-2020-0167
• Languages: English
• ISSNs: 1940-5979

Abstract

Abstract : Purpose: Recent studies have shown that low-volatility shares outperform high-volatility shares. Given the conventional finance theory that risk drives return, this study aims to investigate and attempt to explain the presence of the low-risk anomaly (LRA) in South Africa. Design/methodology/approach: Using share prices from 1990 to 2016, various buy-and-hold strategies are constructed to determine the return to an investor attempting to capitalise on such an anomaly. These strategies involve combinations relating to a price filter, the calculation of risk and volatility, value-weighting or equal-weighting of portfolios and the window period to construct said portfolios. Findings: It was found that the LRA exists on the Johannesburg Stock Exchange (JSE_=) when using univariate sorts, without controlling for the size or value effect. When using multivariate portfolio sorts (size and volatility or value and volatility), it was found that the LRA does not exist on the JSE under the majority of risk proxies, but particularly prevalent when downside risk is used. This loosely points towards a potential "inverse momentum" effect where low-return portfolios outperform their counterparts. Originality/value: In general, it is established that the risk–return relationship is non-linear and deterministic under traditional proxies, but improves to being somewhat, but not completely, linear under a Kalman filter. The Kalman filter, which can be considered a proxy for learning, does not remove the anomaly in its entirety, indicating that behavioural approaches are needed to explain such phenomena.