Low Volatility Anomaly Research Articles

THE LOW-VOLATILITY ANOMALY AND THE ADAPTIVE MULTI-FACTOR MODEL

The paper provides a new explanation of the low-volatility anomaly. We use the Adaptive Multi-Factor (AMF) model estimated by the Groupwise Interpretable Basis Selection (GIBS) algorithm to find those basis assets significantly related to low and high volatility portfolios. These two portfolios load on very different basis assets, indicating that volatility is not an independent risk, but that it is related to existing risk factors. The out-performance of the low-volatility portfolio is due to the (equilibrium) performance of these loaded risk factors, specifically, the better long-term performance of the asset classes bonds and real estate as contrasted with materials, precious metals, and the healthcare industry. Our methodology is applicable to any long–short anomaly but we focus on the low-volatility anomaly since it is formed explicitly on the risk characteristic rather than on embedded risks of other anomalies. The AMF model outperforms the Fama–French 5-factor model significantly both in-sample and out-of-sample.

A closer look at the relationship between firm-specific return variation and stock returns

PurposePrior studies use two measures of firm-specific return variation (FSRV): idiosyncratic volatility in absolute and relative terms, the latter of which is also termed stock price nonsynchronicity. Whereas most research focuses on investigating the idiosyncratic volatility puzzle, the authors carry out comparison of these two measures and further investigate which of the two constituents of nonsynchronicity explain the association between FSRV and stock returns, emphasising the importance of assessing which component drives stock returns.Design/methodology/approachThe authors use the US individual stock returns from 1925 to 2016 and define the two measures of FRSV based on the Fama and French (1993) model. Specifically, the authors decompose the relative measure into two components: (i) absolute idiosyncratic volatility and (ii) systematic volatility. The authors conduct various tests based on high-minus-low, zero-investment quintile portfolio sorts and perform the Fama–MacBeth analysis by singling out each component.FindingsThe authors find a positive return on the portfolio sorted on relative idiosyncratic volatility or on systematic volatility, but find a negative return sorted on absolute idiosyncratic volatility. The results are robust after controlling for size, BM and other risk characteristics using a double-sorting approach. The Fama–MacBeth regression results show that a positive association between the relative measure and stock returns is driven primarily by the low-systematic-volatility anomaly across firms. The findings are robust to controlling for return residual momentum, skewness, jumps and information discreteness.Originality/valueExtant research posits the idiosyncratic volatility puzzle and the low-volatility anomaly. The authors emphasize the importance of integrating these two streams of research. This study enhances the understanding of the driving force underlying the relationship between FSRV and cross-sectional stock returns.

The Low-volatility Anomaly and the Adaptive Multi-Factor Model

The paper provides a new explanation of the low-volatility anomaly. We use the Adaptive Multi-Factor (AMF) model estimated by the Groupwise Interpretable Basis Selection (GIBS) algorithm to find those basis assets significantly related to low and high volatility portfolios. These two portfolios load on very different factors, indicating that volatility is not an independent risk, but that it's related to existing risk factors. The out-performance of the low-volatility portfolio is due to the (equilibrium) performance of these loaded risk factors. The AMF model outperforms the Fama-French 5-factor model both in-sample and out-of-sample.

The Low-Volatility Anomaly Revisited

Abstract The present study conducts two different strategies in order to exploit the low-volatility anomaly in the U.S., the European and the German equity market. The first strategy uses quadratic optimization to calculate optimal portfolio weights. The second strategy sorts stocks into portfolio quintiles based on past realized volatility. Our main findings show that both low-volatility strategies outperform the respective benchmark market portfolio. While the effect is strongest during bull-market periods, it gets weaker during periods of market downturns. Additional results show that in the U.S. market, the low-volatility anomaly can be explained by trading volume and operating profitability. In the German market, operating profitability and the dividend yield can explain the low-volatility effect while in the European market none of these characteristics play a role in explaining the effect. Overall, our findings provide evidence that the low-volatility anomaly still is a robust phenomenon that is inherent in mature capital markets. JEL Classification: G1

Cryptocurrencies and the Low Volatility Anomaly

Cryptocurrencies and the Low Volatility Anomaly

Funding liquidity risk and the low-volatility anomaly: Evidence from the Taiwan stock market

We investigate whether the funding liquidity risk to institutional investors influences the negative relation between expected returns and variance (the ‘‘Low-volatility anomaly’’). With the Taiwan stock market as a setting, we implement a multivariate Markov switching model and use the funding liquidity risk to model the time-varying transition probabilities of the regime-switching process to capture changes in the funding liquidity risk regime. Our evidence documents that the low-volatility anomaly is most pronounced when there is high funding liquidity risk. When there is low funding liquidity risk, however, the low-volatility anomaly has a significant reversal. These results imply that the increased funding liquidity risk due to financial shock transmitted from parent banks is associated with higher selling pressure on institutional investors’ high-volatility stocks, leading to the low-volatility anomaly.

A study of first generation commodity indices: Indices based on financial diversification

This study compares first generation, i.e. long-only passive, commodity indices based on financial diversification criteria, such as equal weight, equal risk contribution and global minimum variance, to the main first generation commodity indices used as vehicles for investment, namely the S&P GSCI (GSCI) and the BCOM. The GSCI and the BCOM are mainly computed according to the world individual commodity production. We find that commodity indices based on financial diversification offer better Sharpe ratios, lower volatilities, and lower correlation with bonds and equity. Finally, we provide evidence for a low-volatility anomaly of the commodity market.

Are Hedge Funds on the Other Side of the Low-Volatility Trade?

The low-volatility anomaly is often attributed to limits to arbitrage, such as leverage, short-selling, and benchmark constraints. One would therefore expect hedge funds, which are typically not hindered by these constraints, to be the smart money that is able to benefit from the anomaly. This article finds that the return difference between low- and high-volatility stocks is indeed a highly significant explanatory factor for aggregate hedge fund returns, but with the opposite sign, that is, hedge funds tend to bet not against the low-volatility anomaly, rather than on it. This finding suggests that limits to arbitrage are not the key driver of the low-volatility anomaly and that concerns about low-volatility having become an “overcrowded” trade may be exaggerated. Another contribution of this study is that it identifies a new, highly significant explanatory factor for hedge fund returns.

The Low-Volatility Anomaly, Interest Rates, and the Canary in a Coal Mine

Capital markets are interconnected. The low-volatility anomaly in equity markets can be partly attributed to changes in interest rates in fixed-income markets. The authors extend this contemporaneous relationship to serial relationships across time. They show that returns from the low-volatility anomaly in U.S. stocks have information about future changes in U.S. Treasury yields. Specifically, when low-volatility stocks outperform high-volatility stocks, Treasury yields tend to decline subsequently. This relationship does not seem to exist in the opposite direction. Trading strategies in U.S. Treasury note futures based on forecasting models using this relationship would have been profitable. Even though the common perception is that the bond market is more prescient than the equity market in terms of anticipating market movements and economic shocks, the authors show that in this instance, equity investors move ahead of bond investors and they are the canaries in the coal mine. <b>TOPICS:</b>Security analysis and valuation, analysis of individual factors/risk premia, fixed income and structured finance

A Continuous Return Model for the Low-Volatility and Low-Beta Anomalies

A Continuous Return Model for the Low-Volatility and Low-Beta Anomalies

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.

Further evidence in support of a low-volatility anomaly: Optimizing buy-and-hold portfolios by minimizing historical aggregate volatility

The ‘low-volatility anomaly’ is the counter-intuitive observation that portfolios of low-volatility stocks tend to yield higher risk-adjusted returns than portfolios of high-volatility stocks. In this article, we investigate if the anomaly holds, not only for portfolios consisting of individual low-volatility stocks, but for portfolios that have been optimized to minimize aggregate volatility. We exploit patterns in historical price fluctuations to identify optimized portfolios whose aggregate volatility is expected to remain low. These portfolios are evaluated by comparing them against the performance of market capitalization and low-volatility quintile benchmarks out-of-sample. The results reveal that, as well as outperforming the market, both in terms of returns and risk, optimized low-volatility strategies also outperform the S&P Low-Volatility Index. These findings provide further support for a low-volatility effect, and imply that the root of the anomaly may lie with a failure to exploit diversification opportunities.

Are Hedge Funds on the Other Side of the Low-Volatility Trade?

The low-volatility anomaly is often attributed to limits to arbitrage, such as leverage, short-selling and benchmark constraints. One would therefore expect hedge funds, which are typically not hindered by these constraints, to be the smart money that is able to benefit from the anomaly. This paper finds that the return difference between low- and high-volatility stocks is indeed a highly significant explanatory factor for aggregate hedge fund returns, but with the opposite sign, i.e. hedge funds tend to bet not on, but against the low-volatility anomaly. This finding has several important implications. First, it implies that limits to arbitrage are not the key driver of the low-volatility anomaly. Second, it argues against the notion that the anomaly may be disappearing or may have turned into an ‘overcrowded’ trade. A final implication is that the return difference between low- and high-volatility stocks should be recognized as a key explanatory factor for hedge fund returns.

Baryonic Beta Dynamics: An Econophysical Model of Systematic Risk

This essay seeks to rehabilitate the capital asset pricing model by splitting beta, the basic unit of systematic risk, into subatomic (or “baryonic”) components. By analogy to quantum chromodynamics and other aspects of the Standard Model of particle physics, this essay bifurcates beta on either side of mean returns and into distinct components reflecting relative volatility and correlation, as well as cash-flow and discount-rate effects. Splitting the atom of systematic risk answers some of the most troubling anomalies and puzzles in finance, including abnormal returns on small-cap and value stocks, the low-volatility anomaly, and the equity premium puzzle.

Leverage As A Weapon of Mass Shareholder-Value Destruction; Another Look at the Low-Beta Anomaly

The 'low-beta' or 'low-volatility anomaly' is one of the most researched in the field of 'alternative beta'. Despite strong published evidence going back to the 1970s that high beta/volatility stocks underperform relative to expectations generated by the Capital Asset Pricing Model (CAPM), the anomaly still persists. The explanations given for this are all behavioural; that investor biases lead to overpricing of high volatility stocks. This paper shows that investor biases cannot be the explanation for the anomaly. Instead, it is proposed that the anomaly stems from a destruction of shareholder value. The strong implication is that the more market leverage a firm has, the more shareholder value is destroyed. Although the prevailing view for a long time has been that adding debt is good for shareholders, making balance sheets more 'efficient', there is in fact a considerable volume of evidence that the opposite is true; evidence which has been incorrectly interpreted for many years. Some possible mechanisms for this shareholder-value destruction are proposed.

Baryonic Beta Dynamics: Splitting the Atom of Systematic Risk

Despite the rise of multi-factor models emphasizing value, firm size, and momentum, beta remains the primary measure of risk in asset pricing. Designed to define systematic risk, net of idiosyncratic risk that can be neutralized through diversification, beta combines a measure of volatility with a measure of correlation.Much of the frustration with beta stems from the failure to disaggregate beta’s discrete components. Conventional beta is often treated as if it were “atomic” in the original Greek sense: uncut and indivisible.This article rehabilitates beta by splitting the atom of systematic risk. Particle physics provides a fruitful framework for evaluating discrete components of financial risk. Quantum chromodynamics (QCD) focuses on six flavors of quarks in three matched pairs: up/down, charm/strange, and top/bottom. Baryons are subatomic particles consisting of three quarks. They include protons and neutrons, which account for most of the mass of the visible universe.By analogy to the Standard Model’s three generations of matter and the three-way interaction of quarks under QCD, I divide beta as the fundamental unit of systematic financial risk into three matching pairs of “baryonic” components:1. Up and down on either side of mean returns2. Relative volatility (σ) and correlation (ρ) between asset-specific and market-wide returns3. “Bad” cash-flow beta versus “good” discount-rate betaThe resulting econophysics of beta explains three of the most significant anomalies and puzzles in mathematical finance:1. Abnormal returns on value and small-cap stocks within the Fama-French three-factor model2. The low-volatility anomaly, also known as Bowman’s paradox3. The equity premium puzzleRemarkably, baryonic beta provides persuasive explanations for all of these anomalies strictly on the basis of fuller mathematical specification of a two-moment capital asset pricing model.

Does Interest Rate Exposure Explain the Low-Volatility Anomaly?

We show that part of the outperformance of low volatility stocks can be explained by a premium for interest rate exposure. Low volatility portfolios have negative exposure to interest rates, whereas the more volatile stocks have positive exposure. Incorporating an interest rate premium explains part of the anomaly. Dependent on the chosen methodology, interest rate exposure explains up to 80% of the unexplained excess return.

Developing an Optimal Method for Multi-asset Portfolio's Downside Protection

This qualitative research based on phenomenology might prove useful in writing the Investment Policy for Institutional Investors in nascent capital markets. The study provides insight into how institutional investors prepare for imminent occurrence of extreme market conditions and the measures they adopt to mitigate the consequences of such extreme market conditions on their investment portfolios. The responses were collected over nearly one and a half years, from March 2013 to September 2014, using various forums held in different cities of the world. The study adopted the methodological triangulation approach to reinforce the findings and build credibility whilst enhancing the usefulness of the findings. This was done by interacting with different groups of respondents and different forums to confirm the findings. All of the respondents were senior decision makers from a wide variety of Institutional Investment entities. From the detailed analysis of the responses emerged concise but dominant topics and strategies. Some of these include risk-based diversification, volatility regimes and risk indicators, low volatility anomalies, rebalancing, going long volatility, longer horizon, rule-based decision making and corporate governance, counter-cyclicality, benchmark agnostic strategies and valuation 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-Volatility Anomaly: Market Evidence on Systematic Risk vs. Mispricing

The authors explored whether the well-publicized anomalous returns associated with low-volatility stocks can be attributed to market mispricing or to compensation for higher systematic factor risk. The results of their study, covering a 46-year period, indicate that the relatively high returns of low-volatility portfolios cannot be viewed solely as compensation for systematic factor risk. The results from their cross-sectional analyses indicate that average returns to low-volatility portfolios are determined by common variations associated with the idiosyncratic-volatility characteristic rather than factor loadings. This finding suggests that the excess returns are more likely driven by market mispricing connected with volatility as a stock characteristic.