Low-risk Anomaly Research Articles

What Can Betting Markets Tell Us About Investor Preferences and Beliefs? Implications for Low Risk Anomalies

An empirical puzzle in financial markets, known as the low-risk anomaly, is that riskier assets earn lower risk-adjusted returns than less risky assets. Theories for this phenomenon focus either on market frictions, such as leverage costs, or non-traditional preferences for lottery-like payoffs. We relate the low risk anomaly to the Favorite-Longshot Bias in betting markets, where returns to betting on riskier longshots are lower than returns to betting on favorites, and provide novel evidence to both anomalies. Synthesizing the evidence, we study the joint implications from the two settings for a unifying explanation. Rational theories of risk-averse investors with homogeneous beliefs cannot explain the cross-sectional relationship between diversifiable risk and return in betting markets. Rather, we appeal to models of non-traditional preferences or heterogeneous beliefs. We find that a model with reference-dependent preferences, featuring probability weighting and diminishing sensitivity, two features of Cumulative Prospect Theory, best fits the data, and is able to capture the choice and the amount to bet. Calibrated parameter values for probability weighting and diminishing sensitivity that capture financial markets facts can simultaneously explain the betting market facts. However, explaining the choice to bet is at odds with loss-aversion, a feature of Cumulative Prospect Theory often used in financial markets applications.

Investigating the low-risk anomaly in South Africa

PurposeRecent 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/approachUsing 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.FindingsIt 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/valueIn 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.

The volatility puzzle of the low-risk anomaly

This paper shows that leading theories of the low-risk anomaly fail to provide a complete explanation for the anomaly’s conditional performance. Sharpe ratios and abnormal returns of betting-against-beta (BAB) and -idiosyncratic volatility (BAV) factors rise significantly following low volatility months, even when controlling for lottery preferences, and in the case of BAB, also controlling for mispricing and limits to arbitrage. Moreover, the leverage constraints theory counterfactually predicts that market and BAB Sharpe ratios increase with volatility. We also show that highly active institutions shift from high- to low-beta stocks as volatility increases, suggesting their demand contributes to the performance of BAB.

The Alphas of Beta and Idiosyncratic Volatility

We find that the relation between the idiosyncratic volatility (IVOL) anomaly and the beta anomaly is quite different at long horizons than at short horizons. IVOL has a significantly negative relation with subsequent stock returns at the short horizon of up to six months and beta does not predict stock returns at any horizon. However, both IVOL and beta significantly negatively predict stock alphas over horizons from a few months to beyond one year. At short horizons, neither anomaly can fully explain the other. At long horizons of beyond six months, the IVOL-alpha relation is explained by the beta-alpha relation. A measure of idiosyncratic volatility over a long window, popularly used by the investments industry to construct low-volatility portfolios, behaves similarly to beta in predicting returns and alphas at various horizons, and its predictive power is mostly explained by beta. Overall, IVOL and beta each has unique short-term information, but at long horizons the two anomalies appear to be the same. Our findings help reconcile a perceptional gap between academic studies and the investment industry on low volatility investing, and enrich the debate about the relation between the two low-risk anomalies.

The Low-Risk Anomaly: How Much Is a Good Risk Estimate Worth?

Many academics and practitioners rely on standard, relatively basic methods to estimate and manage portfolio risk. This can affect an investment manager’s ability to accurately target lower volatility stocks designed to exploit the well-documented low-risk anomaly. This article finds a hybrid risk estimate that mixes short-, medium-, and long-term variances, leads to superior ex post information ratios and alphas by properly aligning securities in the correct order (low risk to high risk). This risk estimate may be worth between $420 million and $1.9 billion annually, calculated from the overall size ($75 billion) of the market. The significance of this estimate survives transaction costs, various time periods, and risk factor exposures. TOPICS:Factors, risk premia, factor-based models, passive strategies, portfolio construction, risk management Key Findings • We propose an alternative way to built portfolios designed to exploit the low-risk anomaly. • The alpha due to this alternative method alone is anywhere from $420 million and $1.9 billion annually, given the current size ($75 billion) of this market. • The significance of this approach survives transaction costs, various time periods, and risk factor exposures.

Are Index Option Returns Priced In The Stock Market? — New Insights From Factor Decompositions

This paper develops a new approach to better understand why risk factors constructed from index option returns appear to be priced in the stock market. We decompose an option-based factor into three components—an implied-volatility- driven component, a linear index-driven component, and a nonlinear index-driven component—and identify the component responsible for the observed beta-return relationship. Applying this method to the bear risk factor developed by Lu and Murray (2019), we show that the authors’ interpretation of the findings is incorrect, and the negative relationship between bear betas and expected stock returns should not be viewed as an instance of systematic risk premia. Instead, it poses a new asset pricing puzzle that is close in spirit to the previously documented low-risk anomalies. Together with the recent critique of Levi and Welch (2019) concerning the evidence on the downside risk premium, our findings suggest that the evidence is still missing on asymmetric market risk being priced in the cross-section of stocks.

Do the Rich Gamble in the Stock Market? Low Risk Anomalies and Wealthy Households

Contrary to the theoretical principle that higher risk is compensated with higher expected return, the literature shows that low-risk stocks outperform high-risk stocks. Using a large-scale household dataset, we provide an explanation for this puzzling result that the anomalous negative risk-return relation is only confined to those stocks held by rich households, whereas the anomaly disappears for stocks held by non-rich households and institutional investors. We find that skewness preference and optimism of rich households as well as their attention/attraction to lottery stocks explain wealthy investors' demand for high-risk stocks, leading to overpricing and low future returns for such stocks.

Investment Factor Timing: Harvesting The Low-Risk Anomaly Using Artificial Neural Networks

We perform investment factor timing based on risk forecasts exploiting the low-risk anomaly. Among various risk measures, we find downside deviation most suited for this task. We apply Long Short Term Memory Artificial Neural Networks (LSTM ANNs) to model the relationship between macro-economic as well as financial market data and the downside deviation of factors. The LSTM ANNs allow for complex, non-linear long-term dependencies. We use LSTM-based forecasts to select high- and low-risk factors in setting up an investment strategy. The strategy succeeds in differentiating positive from negative yielding factor investments, and an accordingly constructed investment strategy outperforms every factor individually as well as LASSO and Multilayer Perceptron neural network benchmark models.

Impact of IFRS 9 on the Cost of Funding of Banks in Europe

On implementation, IFRS 9 increases credit loss (impairment) charges and reduces after-tax profits of banks. This makes retained earnings and hence capital resources lower than what they would be under IAS 39. To maintain their capital ratios under IFRS 9, banks could elect to hold higher levels of equity capital. This paper uses a modified version of CAPM, which accounts for the low-risk anomaly (as suggested by Baker and Wurgler (2015)), to estimate the impact of this potential increase in capital levels on the cost of funding of banks in six European countries, the UK, Germany, France, Italy, Spain and Switzerland. We confirm the existence of low-risk anomaly for banks’ equity in the six countries, except France. The magnitude of the anomaly varies across countries, but is generally low relative to the long-run cost of equity for banks. Our results show that, on day 1, the implementation of IFRS 9 has minor impact on the cost of funding of banks in the six countries.

Impact of cochlear abnormalities on hearing outcomes for children with cochlear implants.

Evaluate the impact of cochlear anomalies on hearing outcomes for pediatric patients with cochlear implants. Retrospective chart review. Tertiary care center. Charts were retrospectively reviewed for cases where pediatric cochlear implant surgery was performed between 2002 and 2018 at a single, tertiary care institution. Patients were divided into groups based on the presence or absence of radiological cochlear abnormalities, which were further classified as low or high risk anomalies. Hearing outcomes were evaluated by measuring pure tone averages and word recognition scores preoperatively, 3 and 12months postoperatively, in addition to the most recent test results. There were 154 ears implanted in our cohort of 100 patients. 107 ears had normal cochlear anatomy, 31 had low risk, and 16 had high risk abnormalities. The most common modality of preoperative imaging was CT scan. Postoperative mean pure tone average (PTA) was significantly higher in patients with inner ear anomalies compared to those with normal anatomy. No significant difference in PTA was noted between low versus high risk patients. <50% of patients had word recognition scores available within the first year following surgery. Abnormalities of the inner ear significantly influenced hearing outcomes over time following cochlear implant surgery when compared to pediatric patients with normal anatomy. Obtaining hearing testing can be difficult in very young children and therefore future studies are warranted to further investigate the impact that cochlear abnormalities may have on hearing outcomes following cochlear implant surgery.

The Volatility Effect Revisited

High-risk stocks do not have higher returns than low-risk stocks in all major stock markets. This article provides a comprehensive overview of this low-risk effect, from the earliest asset pricing studies in the 1970s to the most recent empirical findings and interpretations. Volatility appears to be the main driver of the anomaly, which is highly persistent over time and across markets and which cannot be explained by other factors such as value, profitability, or exposure to interest rate changes. From a practical perspective, low-risk investing requires little turnover, volatilities are more important than correlations, low-risk indexes are suboptimal and vulnerable to overcrowding, and other factors can be efficiently integrated into a low-risk strategy. Finally, there is little evidence that the low-risk effect is being arbitraged away because many investors are either neutrally positioned or even on the other side of the low-risk trade. TOPICS:Volatility measures, exchanges/markets/clearinghouses, risk management Key Findings • This article provides a comprehensive overview of the low-risk anomaly, from the earliest asset pricing studies in the 1970s to the most recent empirical findings and interpretations. • The main driver of the anomaly appears to be volatility. The alpha is highly persistent over time and across markets and cannot be explained by other factors such as value, profitability, or exposure to interest rate changes. • There is little evidence that the low-risk effect is being arbitraged away because many investors are either neutrally positioned or even on the other side of the low-risk trade. Low-risk indexes, however, are vulnerable to overcrowded positions.

Are South African equity investors rewarded for taking on more risk?

Orientation: It is rational for investors to expect additional compensation for an increased risk exposure. This positive risk–return relationship is in line with traditional financial theory; however, this relationship does not always hold in empirical research.Research purpose: The aim of this article was to investigate the prevalence of the low-risk anomaly in the South African equity market.Motivation for the study: If there is evidence of a low-risk anomaly, where low-risk shares outperform high-risk shares, then the additional return expectation of investors may be misplaced.Research design/approach and method: A unique sampling procedure and an extended time frame were employed in a quintile portfolio analysis methodology.Main findings: The article presents evidence that South African listed shares with low historical volatility earned higher risk-adjusted returns over the period July 2004 to September 2018. Low-volatility shares delivered a Sharpe ratio of 1.10 compared to 0.65 produced by the Financial Times Stock Exchange / Johannesburg Stock Exchange Shareholder Weighted Index over the same period.Practical/managerial implications: The assumption that return in an investment portfolio could be enhanced by taking on more risk could be wrong. It seems that fund managers could potentially enhance returns and decrease risk in their portfolios by focussing on shares with low historical volatility.Contribution/value-add: The negative relationship observed between volatility and return is inconsistent with theoretical expectations. Therefore, the results of this article suggest that investors are not rewarded for assuming higher levels of risk.

Is there a low-risk anomaly in the UAE stock market?

We investigate the low-risk anomaly in the United Arab Emirates stock market. Using a sample of stocks listed on the DFM and ADX, we examine the performance of portfolios from one-way sorts on several prominent measures of risk: total volatility, beta, idiosyncratic risk, and value at risk. We find no significant relationship between these measures of risk and future returns – either positive or negative. In consequence, our results do not support the hypothesis that the low-risk anomaly is present in the UAE stock market. Low-risk stocks do not significantly outperform high-risk securities.

Practical Applications of Oasis or Mirage: Assessing Low-Risk Investing from a Global Perspective

Practical Applications Summary In Oasis or Mirage: Assessing Low-Risk Investing from a Global Perspective , in the Summer 2018 issue of The Journal of Investing , Edward Aw, John Jiang, Gregory Sivin (all of Bessemer Trust ), and Aye Soe (of S&P Dow Jones Indices ) discuss why low-risk strategies generally outperform market-cap-weighted indices. Such outperformance, the authors state, “gives rise to … low-risk anomalies” that contravene the assumption that a market-weighted portfolio is the optimal way to balance investment risk and return. To verify the existence of a low-risk anomaly, the authors build a low-risk portfolio of stocks (LRPS) and a low-risk stock portfolio (LRSP), each designed to implement a low-volatility global equity strategy. The authors find that the two portfolios are equally effective in reducing realized volatility relative to a global market-cap-weighted benchmark but that their monthly excess returns over that benchmark are not statistically significant. However, by adding expected returns to the scheme for constructing the low-risk portfolios, the authors find that the modified portfolios outperform to a statistically significant degree.

Exploiting the low-risk anomaly using machine learning to enhance the Black–Litterman framework: Evidence from South Korea

Many studies have revealed that global financial markets are experiencing low-risk anomalies. In the Korean market, for example, even the portfolios of high-risk stocks recorded a loss of about 70% between 2000 and 2016. In this study, we construct a low-risk portfolio that responds to low-risk anomalies in the Korean market using the Black–Litterman framework. We use three machine-learning predictive and traditional time-series models to predict the volatility of assets listed in the Korean Stock Price Index 200 (KOSPI 200) and select the best-performing one. Then, we use the model to classify assets into high- and low-risk groups and create a Black–Litterman portfolio that reflects the investor's view where low-risk stocks outperform high-risk stocks. The experiment shows that reflecting the low-risk view in the market equilibrium portfolio improves profitability and that this view dominates the market portfolio.

Risk-Based Investing in the German Stock Market

The article shows properties of risk-based portfolios in the German stock market. Those systematic strategies use different approaches to weight stocks in portfolios. We present theoretical and empirical characteristics of five risk-based equity investments: the equal-weighted, minimum variance, maximum diversification and risk parity (equal risk budgeting and equal risk contribution) portfolios. Risk-based portfolios outperformed the market-cap weighted CDAX index with a lower level of risk in the period 2002–2015. Their excess returns relative to the CDAX index can be explained with Scherer’s five-factor model; with Fama-French and low-risk anomaly factors. R2s of different strategies range from 77% to 92%.

Do Lower Returns on Bank Stocks Suggest Lower Cost of Capital? An Explanation for the Low Risk Anomaly and the Loan Growth Effect

Banks with higher equity risk and faster loan growth have lower abnormal stock returns. By disentangling ex ante cost of capital from cash flow and discount rate news in bank stock returns, we show that the lower returns do not suggest lower cost of capital. The underperformance of banks with higher equity risk is explained by the poorer cash flow news. The underperformance of banks with faster loan growth is due to both the cash flow and the discount rate news components. Overall, the evidence points to the nontrivial role of investors’ inefficient forecasts of expected bank risk and fundamentals.

The profitability of low-volatility

Low-risk stocks exhibit higher returns than predicted by established asset pricing models, but this anomaly seems to be explained by the new Fama-French five-factor model, which includes a profitability factor. We argue that this conclusion is premature given the lack of empirical evidence for a positive relation between risk and return. We find that exposure to market beta in the cross-section is not rewarded with a positive premium, regardless of whether we control for the new factors in the five-factor model. We also observe stronger mispricing for volatility than for beta, which suggests that the low-volatility anomaly is the dominant phenomenon. We conclude that the low-risk anomaly is not explained by the five-factor model.

The Low-Risk Anomaly: Evidence from The Thai Stock Market

In many developed countries, low-risk stocks tend to earn superior risk-adjusted returns compared to high-risk stock. Using data on the Stock Exchange of Thailand between 2004 and 2015, this paper shows that the abnormal returns associated with investing in low-beta stocks are significant and robust. The zero-cost portfolio that longs low-beta stocks and shorts high-beta stocks delivers monthly four-factor alpha of 1.26%. This paper provides suggestive evidence that, in addition to leverage constraints, the low-risk anomaly can be caused by institutional designs that favour stocks that are index constituents.

The Mixed vs the Integrated Approach to Style Investing: Much Ado About Nothing?

We study the difference between the returns to the integrated approach to investing and those to the mixed approach to investing. Unlike the mixed approach, the integrated approach aggregates factor characteristics at the security level. Recent literature finds that the integrated approach dominates the mixed approach. Using statistical tools for robust performance testing, we demystify these findings as a statistical fluke. We do not find any evidence in favor of the integrated approach. What we do find is that the integrated approach exhibits a higher sensitivity to the low-risk anomaly. However, this reduction in risk does not lead to an improvement in performance.