Low-volatility Strategies Research Articles

Combining low-volatility and momentum: recent evidence from the Nordic equities

ABSTRACT This paper investigates the profitability of combined low-volatility and momentum investment strategies in the Nordic stock markets from January 1999 to September 2022. Confirming earlier studies, our results first indicate that both the volatility and momentum effects persist as pure-play strategies. Further, we explore combined strategies using 50/50, double screening, and ranking strategies. Among the long-only portfolios, the momentum-first strategy generates the best Sharpe ratio using the double screening method−slightly outperforming the ranking method. Additionally, all long-only combination portfolios outperform the market in terms of risk-adjusted returns. Combination long-short strategies produce significantly higher risk-adjusted returns than pure-play strategies. Surprisingly, novel evidence suggests that none of the combination long-short strategies outperforms the pure momentum strategy after risk-adjusting the returns using the Fama and French five-factor model, implying that while momentum may enhance the returns from the low-volatility strategy, the reverse is not true for the Nordic stock markets.

Toward Tax-Efficient Low-Volatility Investing

This article reevaluates low-volatility investing strategies—in particular, their tax efficiency. Low-volatility strategies intend to help investors achieve market-like equity returns, but with less risk than the broader market. Among the low-volatility strategies, those with lower volatility carry lower returns but incur higher turnover and tax burdens. Explicit tax management can greatly improve the strategy’s performance. Tax management, however, is no easy task in practice due to burdensome record-keeping. This article proposes two sets of tax-managed low-volatility investing strategies that require different amounts of record-keeping. Both strategies can significantly improve the after-tax strategy returns while maintaining the compelling risk and pre-tax return profile. Specifically, accounting for net taxable gains alone can harvest most tax alphas, and accounting for the timing of tax lots accurately can further improve the strategy’s performance.

Volatility Timing under Low-Volatility Strategy

The authors show that the slope of the volatility decile portfolio’s return profile contains valuable information that can be used to time volatility under different market conditions in the United States. During good (bad) market conditions, the high- (low-) volatility portfolio produces the highest return. The authors proceed to devise a volatility timing strategy based on statistical tests on the slope of the volatility decile portfolio’s return profile. Volatility timing is achieved by being aggressive during strong growth periods and conservative during market downturns. Superior performance is obtained, with an additional return of 4.1% observed in the volatility timing strategy, resulting in a fivefold improvement on accumulated wealth, along with statistically significant improvement in the Sortini ratio and the information ratio. The authors also demonstrate that stocks in the high-volatility portfolio are more strongly correlated compared to stocks in the low-volatility portfolio. Hence, the profitability of the volatility timing strategy can be attributed to successfully holding a diversified portfolio during bear markets and holding a concentrated growth portfolio during bull markets. <b>Key Findings</b> ▪ The return profile of the volatility decile portfolio is time-varying. Its slope contains vital information on market condition—high-volatility portfolio outperforms low-volatility portfolio during good market condition, but underperforms during bad market condition. Since market regime and asset price behaviors are persistent, the slope parameter can be used to time volatility exposure. ▪ Holding the low-volatility portfolio benefits from the higher risk-adjusted return during general market condition. However, when the slope parameter is positive and statistically significant, it is optimal to hold the high-volatility portfolio for the subsequent period. This will ride on the higher return of high-volatility portfolio during strong growth periods. This leads to higher return and increased volatility, but both Sortini ratio and Information ratio exhibit statistically significant improvement. ▪ Stocks in the low-volatility portfolio are less correlated than stocks in the high-volatility portfolio. The outperformance of the volatility timing strategy formulated in this article can be attributed to holding a concentrated growth portfolio during good market conditions, and holding a diversified portfolio during bad market conditions, thus connecting the literature on low-volatility portfolio with studies on correlation structure and diversification.

Low-volatility strategies for highly liquid cryptocurrencies

Managing extreme price fluctuations in cryptocurrency markets are of central importance for investors in this market segment. Using a sample of highly liquid cryptocurrencies from January 2017 to June 2021, this paper proposes a dynamic investment strategy that selects cryptocurrencies based on their historical volatility and is complemented by a simple stop-loss rule. Our results reveal that investing in highly concentrated low volatility cryptocurrency portfolios with six to twelve months volatility look-back and holding period generate statistically significant excess returns. By including a simple stop-loss rule, the downside risk of cryptocurrency portfolios is reduced markedly, and the Sharpe ratios are improved significantly.

Financial Globalization and Its Implications for Diversification of Portfolio Risk

Trade disputes and the impact of the COVID-19 pandemic on global supply chains have drawn much attention to the notion of “deglobalization.” The common concern is that the steady trend of globalization and its many benefits may reverse. But the globalization trend is not a monolith. In this article, we show that although <i>trade</i> globalization has stalled since the Global Financial Crisis (GFC), <i>financial</i> globalization has continued to increase. We further show that financial globalization has a much more significant impact on portfolios than trade globalization. The primary mechanism of this impact, US dollar hegemony, impacts portfolios primarily through increased spillover of US monetary policy shocks. The two implications for investors are: (1) global equity markets have become increasingly correlated and are likely to stay that way, and (2) this increased correlation reduces the benefits of portfolio diversification and leads to a more concentrated exposure to US monetary policy shocks. <b>Key Findings</b> ▪ Financial globalization has increased steadily even as trade globalization has stalled over the past decade. ▪ Increased financial globalization amplified by increased US Dollar hegemony raises the correlation of global equity markets, reduces efficacy of geographic diversification, and concentrates exposure to US monetary policy shocks. ▪ Therefore, investors must step beyond the simple equity/fixed income paradigm to consider other opportunities to diversify equity risk such as commodities, currencies, alternatives, or low volatility strategies.

Trend Momentum II: Driving Forces of Low Volatility and Momentum

In discussions and critiques on the validity of the Efficient Market Hypothesis, there are two important research focuses: statistical analyses showing that the basic assumption of statistical independence in price series is violated and empirical findings that show that significant market anomalies exist. In this paper, we combine both viewpoints by analyzing two important mathematical factor anomalies: low volatility and momentum. By applying an explicit trend model, we show that both anomalies require trending. Additionally, we show that the trend model exhibits lognormal trend characteristics. Furthermore, the model allows us to describe how low volatility uses implicitly asymmetric trend characteristics while momentum directly exploits trends. Using Mandelbrot’s model of fractional Brownian Motions, we can finally link statistical analyses (measuring the Hurst exponent and persistence in returns) to the empirically observed momentum factor. Experimentally, the Hurst exponent in itself allows for a momentum strategy, and can be utilized to significantly improve low volatility strategies. In contrast to Mandelbrot’s approach, we offer a non-stationary view that allows us to describe both investment strategies using the trend model.

Toward Tax Efficient Low Volatility Investing

This paper reevaluates the low volatility investing strategies and, in particular, their tax efficiency. Low volatility strategies intend to help investors achieve market-like equity returns, but with less risk than that of the broader market. Among the low volatility strategies, those with lower volatility carry lower returns, but incur higher turnover and tax burdens. Explicit tax management can greatly improve the strategy performance. Tax management, nevertheless, is no easy task in practice due to the burdensome record keeping. This paper proposes two sets of tax managed low volatility investing strategies that require different amount of record keeping. Both strategies can significantly improve the after-tax strategy returns, while maintaining the compelling risk and pre-tax return profile. Specifically, accounting for net taxable gains alone can harvest most tax alphas, and accounting for the timing of tax lots accurately in addition can further improve the strategy performance.

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

Low-Volatility Strategy: Can We Time the Factor?

We show that the slope of the volatility decile portfolio's return profile contains valuable information that can be used to time volatility under different market conditions. During good (bad) market condition, the high- (low-) volatility portfolio produces the highest return. We proceed to devise a volatility timing strategy based on statistical tests on the slope of the volatility decile portfolio's return profile. Volatility timing is achieved by being aggressive during strong growth periods, while being conservative during market downturns. Superior performance is obtained, with an additional return of 4.1% observed in the volatility timing strategy, resulting in a five-fold improvement on accumulated wealth, along with statistically significant improvement in the Sortini ratio and the Information ratio. The authors also demonstrate that stocks in the high-volatility portfolio are more strongly correlated compared to stocks in the low-volatility portfolio. Hence the profitability of the volatility timing strategy can be attributed to successfully holding a diversified portfolio during bear markets, while holding a concentrated growth portfolio during bull markets.

Risk Budgeting and Longevity Insurance: Strategies for Sustainable Defined Benefit Pension Funds

Today, many defined benefit pension funds across the world are closing in response to twelve years of intense market volatility and dramatic increases in life expectancy. To the casual observer, it must seem as though the risk of maintaining a defined benefit pension fund has contributed to its rapid decline. Certainly, a defined benefit pension is a very significant promise for the plan sponsor, who has pledged to pay the plan participants for as long as they live and no matter what happens to the assets. The key question today is whether the defined benefit plans that remain open and accruing benefits for employees can be sustained. In fact, a sustainability model may be emerging in the best practices of a few pension plans. These plans generally have three things in common: 1. They have engaged in a rigorous risk budgeting process, involving an analysis of their risk, an estimation of the potential losses in their pension funds and a decision regarding how much they can afford to lose. 2. They have dramatically reduced their asset risk in an effort to keep pension losses within the risk budget and they may have two-thirds or more of their assets invested in a low volatility strategy such as fixed income or total return. 3. They have a strategy for longevity risk, which may involve longevity insurance to ensure that the quantum of their liability is known and knowable so that funding and investing activities can be carried out with certainty as to the ultimate liability. While these strategies may seem less exciting than using risky assets to reach for high returns, they are rooted in the premise that investing in equities, private equity, commodities, property and other risky assets actually involves risk and to the extent that those strategies expose the plan sponsor to more risk than the sponsor can afford, too much risk is likely to lead to the closure of the pension fund and the elimination of the defined benefit from the employees' future retirement security. Perhaps risk budgeting and disciplined risk management, combined with new techniques to insure longevity risk can be used to sustain more pension funds and safeguard the health of the plan sponsors.

A Multi-Factor Strategy for Index Alpha Enhancement

Empirical studies suggest that the so-called “low volatility anomaly” is actually an artifact of skewness preferences—a tendency of investors to prefer stocks offering upside potential with low likelihood of loss. The authors argue that if skewness preferences underlie the low volatility anomaly, then a naive low volatility strategy should be dominated by one that explicitly targets expected return skew. This article provides empirical evidence to that effect. It recommends a multi-factor alpha strategy that is based on avoiding index constituents that are perceived to have ex ante high relative skew. These findings have important implications for investors. Specifically, the authors demonstrate that portfolios constructed to avoid high expected skew stocks outperform both low volatility strategies and several widely used US and global capitalization-weighted indices.

Momentum and Low-Volatility Effects in Country-Level Stock Market Anomalies

The aim of this paper is to present the low-volatility and momentum effects across country-level anomalies in global equity markets. By using a sample of 78 countries for the period from 1995 to 2015, a set of potential 40 cross-sectional inter-market anomalies was tested, some of which had never been examined before. Based on the findings, according to which half of the abovementioned anomalies serve as reliable and robust sources of returns, we provide convincing evidence that the anomalies with low volatility over the past 12-48 months or good performance over the past 6-12 months tend to outperform in the future. Furthermore, the study showed that returns on individual country-level strategies are weakly correlated. Consequently, developing a portfolio consisting of past top-performing or low-volatility strategies may constitute a valuable approach for international investors.

Trend Momentum II: Driving Forces of Low Volatility and Momentum

In discussions and critiques on the validity of the Efficient Market Hypothesis, there are two important research focuses: statistical analyses showing that the basic assumption of statistical independence in price series is violated and empirical findings that show that significant market anomalies exist. In this work, we combine both viewpoints by analyzing two important mathematical factor anomalies: low volatility and momentum. By applying an explicit trend model, we show that both anomalies require trending. Additionally, we show that the trend model used exhibits log-normal trend characteristics. Furthermore, the model allows us to describe how low volatility uses implicitly asymmetric trend characteristics while momentum directly exploits trends. Using Mandelbrot’s model of fractional Brownian Motions, we can finally link statistical analyses (measuring the Hurst exponent and persistence in returns) to the empirically observed momentum factor. Experimentally, the Hurst exponent in itself allows for a momentum strategy, and it can also be utilized to significantly improve low volatility strategies. In contrast to Mandelbrot’s approach, we offer a non-stationary view that allows us to describe both investment strategies using the trend model.

Low Volatility Needs Little Trading

The author shows that an efficient low-volatility strategy only needs a little amount of trading. The empirical literature on low-volatility investing reveals a concave relation between the amount of trading and the risk reduction. Portfolio simulations confirm this non-linear pattern in which each increase in turnover results in smaller marginal reductions in volatility. In general a moderate trading level of 30% is enough to reduce portfolio volatility by 25% compared with the market index. In addition, low-volatility stocks are relatively liquid and cheap to trade, primarily because they are much larger than the average stock. The law of diminishing returns also applies to other alpha factors such as value and momentum and integrating them into a multi-factor low-volatility strategy is an efficient way to increase factor exposure at low trading costs. <b>TOPIC:</b>Analysis of individual factors/risk premia

Analysing the Exposure of Low-Volatility Equity Strategies to Interest Rates

At the dawn of a potential rise in rates triggered by Central Banks in both Europe and the United States, doubts are being raised about the ability of low-volatility portfolios to continue to deliver robust performance. We quantify this latent performance lag and provide empirical explanations, distinguishing parallel moves from non-parallel distortions in the yield curve. More specifically, we evaluate the implications from the low-volatility screening on the portfolio’s industrial breakdown. The conclusion shows that the overweighting of defensive industries is the main source of underperformance in a risk-on environment. However, these bets happen to be the ones that allow the strategy to outperform over a full economic cycle. Therefore, we propose a method to control the low-volatility exposure to changes in interest rates, which should be of interest to benchmarked portfolio managers.

The Incredible Shrinking Factor Return

This is the first in a series of papers we will publish in 2017 that demonstrate factor tilts generally deliver far less alpha in live portfolios than they do on paper, or put another way, investment managers generally fail to capture the returns that would be expected based on their factor tilts. We break our research into four parts. In this paper we show that the factor returns realized by fund managers differ starkly from the theoretical factor returns constructed from long–short paper portfolios. Notably, the market, value, and momentum factors are far less rewarding in live fund management than their theoretical long–short paper portfolio returns. In the second paper of the series, we challenge the idea that factor tilts — portfolios combining several theoretical factor portfolios — are the same as smart beta strategies. We show, using Fundamental Index™, equal-weight, and low-volatility strategies as illustrative examples, that factor tilts cannot successfully replicate smart beta strategies. Although the factor tilts of these strategies are easy to replicate, the resulting portfolios look very different from the originals, with the replication portfolios having far higher turnover, lower performance, and smaller capacity. In a third paper of the series, we show that the relative valuations of factor loadings can give us the courage to buy mutual funds when factor tilts are at their cheapest, hence, the most out of favor. Along with fees, turnover, and past performance — where low fees, low turnover, and low (yes, low!) past performance are predictive of better future returns — factor loadings can help us improve our forecasts of fund returns. We find the best predictor is prior three-year performance, but with the wrong sign: buying the losers is the winningest strategy. Finally, a fourth paper will take a closer look at momentum, for which we find the realized alpha in live portfolios is essentially zero compared to a theoretical alpha of around 6% a year. We show why momentum doesn’t work in live portfolios, and also show how momentum can be saved as a useful source of alpha.

Systematic Diversification Using Beta

Higher-risk investments deserve higher expected returns to compensate for the extra risk, or so theory tells us. Historically, this has not always been the case for U.S. and other developed-market stocks. This “beta anomaly,” which is now well established by academics, has started to gain traction with investors. This is demonstrated by the large flows into the numerous low-beta and low-volatility strategies that were established in the wake of the Global Financial Crisis. The authors examine the beta anomaly in the academic literature and provide an empirical analysis for stocks in the U.S., developed, and emerging markets. Their primary finding is that beta is not a strong predictor of expected returns, but it is useful when used to help reduce risk in a portfolio. The authors present results for an investment strategy that filters out the highest-beta stocks while controlling for concentration risks by country and sector. The study finds mixed results for beta as an anomaly: Low beta outperforms for international markets, underperforms in emerging markets, and is flat in the U.S. markets. None of these differences appears to be statistically significant. The authors find beta is very useful, however, as a tool for controlling risk, especially in the context of strategies that diversify across countries and sectors.

U.S. Low and Minimum Volatility Indexes: An EmpiricalAnalysis of Factor Exposure

This study investigates the major low and minimum volatility indexes used as benchmarks for the largest ETFs in the space. The authors contend that the findings on low and minimum volatility investing in the academic literature are captured by the indexes. Specifically, they find that the indexes examined provide superior risk-adjusted performance relative to a market capitalization benchmark. They also find that factor exposures are significant, on average, and explain away the significant alphas in the large-/mid-cap but not small-cap indexes examined. Further, they find that factor exposures, particularly value and momentum, are time-varying and can be of either sign, whereas investment and profitability exposure are generally positive and consistent. Smart-beta and factor investors should be aware of the nature of the factor exposures that low and minimum volatility strategies contribute to a portfolio.

The timing of low-volatility strategy

This study explores the role of investor attention impact on low-volatility strategy. Our evidence suggests that a low-volatility strategy for high investor attention stocks is more profitable than low investor attention stocks. Conditioned on high investor attention, the profitability of a low-volatility strategy significantly increases due to lower returns on higher idiosyncratic volatility stocks. Consistent with recent optimal beliefs theory, investors’ propensity for gambling-type strategies leads to negative returns with high idiosyncratic volatility stocks. Our results provide a behavioral support to explain the low-volatility strategy based on investors’ propensity to gamble.

Defensive Portfolio Construction Based on ExtremeValue at Risk

In this article, the authors compare the characteristics of a low-volatility strategy and low-risk strategies based on extreme Value at Risk (eVaR) constructed from a large universe of international stocks in the period from 1995 to 2015. They find that low-eVaR strategies have better risk and performance statistics exhibiting a less-pronounced sector concentration, a common problem in low-volatility portfolios. Although they find no significant differences in the structure of the risk premium conditioned on periods of economic expansions, the authors do find some differences conditioned on times of recessions. However, their analysis cannot explain the improved downside risk profile that remains a strategy-specific characteristic, caused most likely by the more diversified sector allocation. Their findings are robust to variations in the methodology of eVaR calculation.