Attention Conditions Stock Market Reaction to News
Sentiment
Peter Hafez, Junqiang Xie, “Attention Conditions Stock Market Reaction to News
Sentiment” – RavenPack Quantitative Research – March 2013. Available at: RavenPack
Abstract
We consider media coverage, sentiment momentum, price momentum and return volatility as forms of attention. Amongst other potentially profitable findings, we show that going long positive sentiment stocks with low volatility, and short negative sentiment stocks with high volatility yields an annualized spread of 16.3% over the backtesting period.
The Cross-Section of Volatility and Expected Returns
Ang, Andrew, Hodrick, Robert J., Xing, Yuhang and Zhang, Xiaoyan, “The Cross-Section of Volatility and Expected Returns” Journal of Finance, Forthcoming. Available at SSRN: http://ssrn.com/abstract
Abstract
We examine how volatility risk, both at the aggregate market and individual stock level, is priced in the cross-section of expected stock returns. Stocks that have past high sensitivities to innovations in aggregate volatility have low average returns. We also find that stocks with past high idiosyncratic volatility have abysmally low returns, but this cannot be explained by exposure to aggregate volatility risk. The low returns earned by stocks with high exposure to systematic volatility risk and the low returns of stocks with high idiosyncratic volatility cannot be explained by the standard size, book-to-market, or momentum effects, and are not subsumed by liquidity or volume effects.
High Idiosyncratic Volatility and Low Returns: International and Further U.S. Evidence
Ang, Andrew, Hodrick, Robert J., Xing, Yuhang and Zhang, Xiaoyan (2009), “High Idiosyncratic Volatility and Low Returns: International and Further U.S. Evidence” Journal of Financial Economics, Vol. 91, pp. 1-23, January 2008 . Available at SSRN: http://ssrn.com/abstract
Abstract
Stocks with recent past high idiosyncratic volatility have low future average returns around the world. Across 23 developed markets, the difference in average returns between the extreme quintile portfolios sorted on idiosyncratic volatility is -1:31% per month, after controlling for world market, size, and value factors. The effect is individually significant in each G7 country. In the United States, we rule out explanations based on trading frictions, information dissemination, and higher moments. There is strong covariation in the low returns to high-idiosyncratic-volatility stocks across countries, suggesting that broad, not easily diversifiable factors lie behind this phenomenon.
Beyond Cap-Weight: The Search for Efficient Beta
Arnott, Robert D., Vitali Kalesnik, Paul Moghtader, and Craig S. Scholl (2009), “Beyond Cap-Weight: The Search for Efficient Beta” (November 16, 2009), Journal of Indexes, January-February 2010. Available at SSRN: http://ssrn.com/abstract
Abstract
For over 40 years, our industry has relied on the Capital Asset Pricing Model (CAPM) beta and the capitalization-weighted market portfolio for asset allocation, for market representation and for our default core equity investments. This elegant world-view is now under siege from various directions.
The ‘fundamentalists’ advocate a portfolio that weights companies in accordance with the recent economic scale of their businesses, thereby resembling the composition of the economy rather than the composition of the stock market. The ‘minimum variance’ crowd points to the value of consistency between investor objectives and portfolio construction. The ‘egalitarians’ advocate equal weighting. Historically, these alternative index strategies have delivered higher return and lower CAPM beta, which can help an investor to target either more return or less risk or a bit of both. Each of these strategies – along with the ever-dominant cap weight indexes – has strengths and weaknesses, some minor and some major.
The cap-weighted standard is also facing a more subtle source of attack as investors reassess their risk budgets. Increasingly, investors are reassessing their risk budgets, usually downward, which can create pressure to move from active into passive strategies and to lower a fund’s exposure to an undiversified single-factor equity risk. But, can we lower our risk profile without abandoning our return goals? Perhaps it is time to consider a bigger tent, allowing for the merits of multiple broad-market indexes and multiple betas.
We explore the comparative merits of four major categories of quasi-passive ‘Index’ construction. We do so from a global perspective. And we explore the surprising efficacy of combining multiple index strategies into a diversified beta portfolio.
Leverage Aversion and Risk Parity
Asness, Cliff, Andrea Frazzini, and Lasse H. Pedersen (2011) “Leverage Aversion and Risk Parity”(January 2011).
Abstract
We show that leverage aversion changes the predictions of modern portfolio theory: It causes safer assets to offer higher risk-adjusted returns than riskier assets. Consuming the high risk adjusted returns offered by safer assets requires leverage, creating an opportunity for investors with the ability and willingness to borrow. A Risk Parity (RP) portfolio exploits this in a simple way, namely by equalizing the risk allocation across asset classes, thus overweighting safer assets relative to their weight in the market portfolio. Consistent with our theory of leverage aversion, we find empirically that RP has outperformed the market over the last century by a statistically and economically significant amount, and provide further evidence across and within countries and asset classes.
Benchmarks as Limits to Arbitrage: Understanding the Low Volatility Anomaly
Baker, Malcolm P., Bredan Bradley, and Jeffrey A. Wurgler (2010), “Benchmarks as Limits to Arbitrage: Understanding the Low Volatility Anomaly” (March 2010), NYU Working Paper No. FIN-10-002. Available at SSRN: http://ssrn.com/abstract
Abstract
Over the past 41 years, high volatility and high beta stocks have substantially underperformed low volatility and low beta stocks in U.S.markets. We propose an explanation that combines the average investor’s preference for risk and the typical institutional investor’s mandate to maximize the ratio of excess returns and tracking error relative to a fixed benchmark (the information ratio) without resorting to leverage. Models of delegated asset management show that such mandates discourage arbitrage activity in both high alpha, low beta stocks and low alpha, high beta stocks. This explanation is consistent with several aspects of the low volatility a nomaly including why it has strengthened in recent years even as institutional investors have becomemore dominant.
Low Risk Stocks Outperform within All Observable Markets of the World
Baker, Nardin and Robert A. Haugen (2012), “Low Risk Stocks Outperform within All Observable Markets of the World” (April 27, 2012). Available at SSRN: http://ssrn.com/abstract
Abstract
This article provides global evidence supporting the Low Volatility Anomaly: that low risk stocks consistently provide higher returns than high risk stocks. This study covers 33 different markets during the time period from 1990-2011. (Two previous studies by Haugen & Heins (1972) and Haugen & Baker(1991) show the same negative payoff to risk in time periods 1926-1970 and 1970-1990.) The procedure for our study is intentionally simple, transparent and easily replicable. Our samples include non-survivors.
We look at an international universe of stocks beginning with the first month of 1990 until December 2011; we compute the volatility of total return for each company in each country over the previous 24 months. Stocks in each country are ranked by volatility and formed into deciles. In the total universe and in each individual country low risk stocks outperform, the relationship with respect to Sharpe ratios is even more impressive.
We believe this anomaly is caused primarily by agency issues, namely the compensation structures and internal stock selection processes at asset management firms which lead institutional investors on average to hold more volatile stocks. The article also addresses the implications for how corporate finance managers make capital investment decision in light of this evidence. The evidence presented here dethrones both CAPM and the Efficient Market Hypothesis.
Is Minimum Variance Investing Worth the While?
Behr, Patrick, Andre Guttler and Felix Miebs (2008), “Is Minimum Variance Investing Worth the While?”. Available Online: http://www.top1000funds.com.pdf
Abstract
This paper examines the risk-adjusted performance of the minimum-variance equity investment strategy in the U.S. While earlier studies only relied on empirical Sharpe ratio comparisons between the (constrained) minimum-variance strategy and different benchmark portfolios, we employ bootstrap methods for statistical inference concerning the Sharpe ratio, the Sortino ratio, certainty equivalents and alpha measures based on several factor models. We confirm and provide robust inference concerning earlier findings that constrained minimum-variance portfolios do outperform a value weighted benchmark. Moreover, our findings are in line with prior research, stating that minimum-variance portfolios do not outperform a naively diversified benchmark in terms of the Sharpe ratio. Both our results are invariant to the portfolio revision frequency and may be observed in all subperiods. Nevertheless, we show the high sensitivity of the constrained minimum-variance portfolios to the revision frequency and the imposed maximum portfolio weight constraints.
The Volatility Effect: Lower Risk Without Lower Return
Blitz, David and Pim van Vliet (2007), “The Volatility Effect: Lower Risk Without Lower Return” Journal of Portfolio Management, pp. 102-113, Fall 2007; ERIM Report Series Reference No. ERS-2007-044-F&A. Available at SSRN: http://ssrn.com/
Abstract
We present empirical evidence that stocks with low volatility earn high risk-adjusted returns. The annual alpha spread of global low versus high volatility decile portfolios amounts to 12% over the 1986-2006 period. We also observe this volatility effect within the US, European and Japanese markets in isolation. Furthermore, we find that the volatility effect cannot be explained by other well-known effects such as value and size. Our results indicate that equity investors overpay for risky stocks. Possible explanations for this phenomenon include (i) leverage restrictions, (ii) inefficient two-step investment processes, and (iii) behavioral biases of private investors. In order to exploit the volatility effect in practice we argue that investors should include low risk stocks as a separate asset class in the strategic asset allocation phase of their investment process.
The Volatility Effect in Emerging Markets
Blitz, David, Juan Pang, and Pim van Vliet (2012), “The Volatility Effect in Emerging Markets” (April 10, 2012). Available at SSRN: http://ssrn.com/abstract=2050863
Abstract
In this paper we examine the empirical relation between risk and return in emerging equity markets and find that this relation is flat, or even negative. This is inconsistent with theoretical models such as the CAPM, which predict a positive relation, but consistent with the results of studies which have previously examined the empirical relation between risk and return in the U.S. and other developed equity markets. We show that our findings are robust to considering a universe of large-cap stocks only, to considering longer holding periods and to controlling for exposures to the size, value and momentum effects. We also observe that the empirical deviation from the theoretical risk-return relation appears to be growing stronger over time, which might be related to the increasing participation of benchmark-driven investors, in line with the ‘limits to arbitrage’ hypothesis. Finally, we find low correlations between the volatility effects in emerging and developed equity markets, which argues against a common-factor explanation.
Demystifying Equity Risk-Based Strategies: A Simple Alpha Plus Beta Description
Carvalho, Raul Leote de, Lu Xiao, and Pierre Moulin (2011), “Demystifying Equity Risk-Based Strategies: A Simple Alpha Plus Beta Description” (September 13, 2011), The Journal of Portfolio Management, Available at SSRN: http://ssrn.com/abstract=1949003
Abstract
We considered five risk-based strategies: equally-weighted, equal-risk budget, equal-risk contribution, minimum variance and maximum diversification. All five can be well described by exposure to the market-cap index and to four simple factors: low-beta, small-cap, low-residual volatility and value. This is, in our view, a major contribution to the understanding of such strategies and provides a simple framework to compare them. All except equally-weighted are defensive with lower volatility than the market-cap index. Equally-weighted is exposed to small-cap stocks. Equal-risk budget and equal-risk contribution are exposed to small-cap and to low-beta stocks. These three have a high correlation of excess returns and their portfolio largely overlap. They invest in all stocks available and have both a low turnover and low tracking error relative to market-cap index. Minimum variance and maximum diversification are essentially exposed to low-beta stocks. They are the most defensive, invest in much the same stocks and have high tracking error and turnover.
Risk Parity portfolio vs. Other Asset Allocation Heuristic Portfolios
Chaves, Denis, Jason Hsu, feifei Li and Omid Shakernia (2011), “Minimum-Variance Portfolios in the US Equity Market”, Journal of Investing, Spring 2011, Vol. 33, No. 1, pp. 108-118. Available at: http://www.jasonhsu.org
Abstract
Risk parity is an investment strrategy that has attracted significant attention in recent years. We show that this strategy has a higher Sharpe ratio than well-establihsed approaches like minimum variance or mean-variance optmization, but does not consistently outperform a simple equal-weighted portfolio or even a 60/40 equity/bond portfolio.
Properties of the Most Diversified Portfolio
Choueifaty, Yves, Tristan Froidure and Julien Reynier (2011) ‘Properties of the Most Diversified Portfolio” (July 6, 2011). Available at SSRN: http://ssrn.com/abstract
Abstract
This article expands upon “Toward Maximum Diversification” by Choueifaty and Coignard [2008]. We present new mathematical properties of the Diversification Ratio and Most Diversified Portfolio (MDP), and investigate the optimality of the MDP in a mean-variance framework. We also introduce a set of “Portfolio Invariance Properties,” providing the basic rules an unbiased portfolio construction process should respect. The MDP is then compared in light of these rules to popular methodologies (equal weights, equal risk contribution, minimum variance), and their performance is investigated over the past decade, using the MSCI World as reference universe. We believe that the results obtained in this article show that the MDP is a strong candidate for being the un-diversifiable portfolio, and as such delivers investors with the full benefit of the equity premium..
Minimum-Variance Portfolios in the US Equity Market
Clarke, Roger, Harindra de Silva & Steven Thorley (2006), “Minimum-Variance Portfolios in the US Equity Market”, Journal of Portfolio Management, Fall 2006, Vol. 33, No. 1, pp. 10-24. Available at Analytic Website: https://www.aninvestor.com/templates/streammarket.aspx?mm_ID=1406
Abstract
At the beginning of each month from January 1968 through December 2005 (456 months), they estimate a covariance matrix for the 1,000 largest market capitalization U.S. stocks with 60 months of historical return data. They look at long only and long short portfolio, they do additional analysis with daily optimizations.
Minimum Variance, Maximum Diversification, and Risk Parity: An Analytic Perspective
Clarke, Roger, Harindra de Silva & Steven Thorley (2011), Minimum Variance, Maximum Diversification, and Risk Parity: An Analytic Perspective”, (December 2011). http://ssrn.com/abstract
Abstract
Analytic solutions to Minimum Variance, Maximum Diversification, and Risk Parity portfolios provide helpful intuition about their properties and construction. Individual asset weights depend on systematic and idiosyncratic risk in all three risk-based portfolios, but systematic risk eliminates many investable assets in long-only constrained Minimum Variance and Maximum Diversification portfolios. On the other hand, all investable assets are included in Risk Parity portfolios, and idiosyncratic risk has little impact on the magnitude of the weights. The algebraic forms for optimal asset weights derived in this paper provide generalizable perspectives on risk-based portfolio construction, in contrast to empirical simulations that employ a specific set of historical returns, proprietary risk models, and multiple constraints.
.
Minimum-Variance Portfolio Composition
Clarke, R., H. de Silva, and S. Thorley (2011), “Minimum-Variance Portfolio Composition” The Journal of Portfolio Management, Vol. 37, No. 2 (2011) pp. 31-45. http://www.iijournals.com
Abstract
Empirical studies document that equity portfolios constructed to have the lowest possible risk have surprisingly high average returns. Clarke, de Silva, and Thorley derive an analytic solution for the long-only minimum-variance portfolio under the assumption of a single-factor covariance matrix. The equation for optimal security weights has a simple and intuitive form that provides several insights on minimum-variance portfolio composition. While high idiosyncratic risk can lead to a low security weight, high systematic risk takes the large majority of investable securities out of long-only solutions. The relatively small set of securities that remains has market betas below an analytically specified threshold beta. The ratio of portfolio beta to threshold beta dictates the portion of ex ante portfolio variance that is market-factor related. The authors verify and illustrate the portfolio mathematics using historical data on the U.S. equity market and explore how the single-factor analytic results compare to numerical optimization under a generalized covariance matrix. The analytic and empirical results of this study suggest that minimum-variance portfolio performance is largely a function of the long-standing empirical critique of the traditional CAPM that low-beta stocks have relatively high average returns.
Risk and Return in General: Theory and Evidence
Falkenstein, Eric G. (2009), “Risk and Return in General: Theory and Evidence” (June 15, 2009). Available at SSRN: http://ssrn.com/abstract
Abstract
Empirically, standard, intuitive measures of risk like volatility and beta do not generate a positive correlation with average returns in most asset classes. It is possible that risk, however defined, is not positively related to return as an equilibrium in asset markets. This paper presents a survey of data across 20 different asset classes, and presents a model highlighting the assumptions consistent with no risk premium. The key is that when agents are concerned about relative wealth, risk taking is then deviating from the consensus or market portfolio. In this environment, all risk becomes like idiosyncratic risk in the standard model, avoidable so unpriced.
Betting Against Beta
Frazzini, Andrea and Lasse H. Pedersen (2010), “Betting Against Beta” NBER working paper series. http://www.nber.org/papers/w16601.pdf
Abstract
We present a model in which some investors are prohibited from using leverage and other investors’ leverage is limited by margin requirements. The former investors bid up high-beta assets while the latter agents trade to profit from this, but must de-lever when they hit their margin constraints. We test the model’s predictions within U.S. equities, across 20 global equity markets, for Treasury bonds, corporate bonds, and futures. Consistent with the model, we find in each asset class that a betting-against-beta (BAB) factor which is long a leveraged portfolio of low-beta assets and short a portfolio of high-beta assets produces significant risk-adjusted returns. When funding constraints tighten, betas are compressed towards one, and the return of the BAB factor is low.
Relation between Time-Series and Cross-Sectional Effects of Idiosyncratic Variance on Stock Returns
Guo, Hui and Robert Savickas(2010), “Relation between Time-Series and Cross-Sectional Effects of Idiosyncratic Variance on Stock Returns. http://ssrn.com/abstract=904207
Abstract
Consistent with the post-1962 U.S. evidence by Ang, Hodrick, Xing, and Zhang [Ang, A., Hodrick, R., Xing Y., Zhang, X., 2006. The cross-section of volatility and expected returns. Journal of Finance 51, 259-299.], we find that stocks with high idiosyncratic variance (IV) have low CAPM-adjusted expected returns in both pre-1962 U.S. and modern G7 data. We also test in three ways the conjecture that IV is a proxy of systematic risk. First, the return difference between low and high IV stocks — that we dub as IVF — is a priced factor in the cross-section of stock returns. Second, loadings on lagged market variance and lagged average IV account for a significant portion of variation in average returns on portfolios sorted by IV. Third, the variance of IVF correlates closely with average IV, and the two variables have similar explanatory power for the time-series and cross-sectional stock returns.
On the Evidence Supporting the Existence of Risk Premiums in the Capital Market
Haugen, Robert A. and A. James Heins (1972), “On the Evidence Supporting the Existence of Risk Premiums in the Capital Market” (December 1, 1972). Available at SSRN: http://ssrn.com/abstract
Abstract
This paper was the basis of the paper entitled “Risk and the Rate of Return on Financial Assets: Some Old Wine in New Bottles,” which was published in the Journal of Financial and Quantitative Analysis in December 1975. Differences between the two papers result from the refereeing process. This original working paper version is clearer and more complete. These papers were the first to document the lack of positive relationship between risk and return in the empirical cross-section of stock market returns.
The Efficient Market Inefficiency of Capitalization-Weighted Stock Portfolios
Haugen, Robert and Nardin Baker (1991), “The Efficient Market Inefficiency of Capitalization-Weighted Stock Portfolios”, Journal of Portfolio Management, vol. 17, No.1, pp. 35-40.
Risk reduction in large portfolios: Why imposing the wrong constrains helps
Jagannathan R. and T. Ma (2003), “Risk reduction in large portfolios: Why imposing the wrong constrains helps” The Journal of Finance, 58(4), pp. 1651-1684. http://onlinelibrary.wiley.com/abstract
Abstract
They found both higher returns and lower realized risks for a US minimum variance portfolio versus a capitalization weighted benchmark. They show that in constructing a global minimum variance portfolio, a no-short-sales constraint actually helps out-of-sample performance because stocks that have extremely high covariances with other stocks tend to receive negative portfolio weights, and the no short-sales constraint is equivalent to capping the sample covariances at reasonable level. Hence, to the extent that high estimated covariances are more likely to be caused by upward-biased estimation error, imposing the non-negativity constraint on position weights reduces the sampling error, and so a no short-sales restriction is in practice a modest constraint in constructing a minimum variance portfolio.
The Capital Asset Pricing Model: Some Empirical Tests
Jensen, Michael C., Fischer Black, and Myron S. Scholes (1972), “The Capital Asset Pricing Model: Some Empirical Tests” Michael C. Jensen, STUDIES IN THE THEORY OF CAPITAL MARKETS, Praeger Publishers Inc., 1972. Available at SSRN: http://ssrn.com/abstract
Abstract
Considerable attention has recently been given to general equilibrium models of the pricing of capital assets. Of these, perhaps the best known is the mean-variance formulation originally developed by Sharpe (1964) and Treynor (1961), and extended and clarified by Lintner (1965a; 1965b), Mossin (1966), Fama (1968a; 1968b), and Long (1972). In addition Treynor (1965), Sharpe (1966), and Jensen (1968; 1969) have developed portfolio evaluation models which are either based on this asset pricing model or bear a close relation to it. In the development of the asset pricing model it is assumed that (1) all investors are single period risk-averse utility of terminal wealth maximizers and can choose among portfolios solely on the basis of mean and variance, (2) there are no taxes or transactions costs, (3) all investors have homogeneous views regarding the parameters of the joint probability distribution of all security returns, and (4) all investors can borrow and lend at a given riskless rate of interest. The main result of the model is a statement of the relation between the expected risk premiums on individual assets and their “systematic risk.” Our main purpose is to present some additional tests of this asset pricing model which avoid some of the problems of earlier studies and which, we believe, provide additional insights into the nature of the structure of security returns.
The evidence presented in Section II indicates the expected excess return on an asset is not strictly proportional to its B, and we believe that this evidence, coupled with that given in Section IV, is sufficiently strong to warrant rejection of the traditional form of the model given by (1). We then show in Section III how the cross-sectional tests are subject to measurement error bias, provide a solution to this problem through grouping procedures, and show how cross-sectional methods are relevant to testing the expanded two-factor form of the model. We show in Section IV that the mean of the beta factor has had a positive trend over the period 1931-65 and was on the order of 1.0 to 1.3% per month in the two sample intervals we examined in the period 1948-65. This seems to have been significantly different from the average risk-free rate and indeed is roughly the same size as the average market return of 1.3 and 1.2% per month over the two sample intervals in this period. This evidence seems to be sufficiently strong enough to warrant rejection of the traditional form of the model given by (1). In addition, the standard deviation of the beta factor over these two sample intervals was 2.0 and 2.2% per month, as compared with the standard deviation of the market factor of 3.6 and 3.8% per month. Thus the beta factor seems to be an important determinant of security returns.
On the Estimation of the Global Minimum Variance Portfolio
Kempf, Alexander and Christoph Memmel (2003), “On the Estimation of the Global Minimum Variance Portfolio”.
Available at SSRN: http://ssrn.com/abstract
Abstract
The implementation of the Markowitz optimization requires the knowledge of the parameters of the return distribution. These parameters cannot be observed, but have to be estimated. Merton (1980) and Jorion (1985) point out that especially the expected returns are hard to estimate from time series data. The estimation risk is huge. The global minimum variance portfolio is the only efficient stock portfolio whose weights do not depend on the expected returns. Therefore, one can avoid extreme estimation risk by investing into this portfolio. Nevertheless, there remains a considerable estimation risk with respect to the covariance matrix. This article deals with the estimation of the weights of the global minimum variance portfolio. The literature suggests a two-step approach to determine the optimal portfolio weights. In the first step one estimates the return distribution parameters, and in the second step one optimizes the portfolio weights using the estimated parameters. The main contribution of our paper is to suggest new one-step approaches to estimate optimal portfolio weights. Our paper has four main results: 1) Our one-step regression approach is the best unbiased weight estimator. 2) The estimation risk for this best unbiased estimator is large. 3) (Biased) shrinkage estimators lead to portfolios with smaller out-of-sample return variances. 4) Our one-step shrinkage estimator beats the two step shrinkage approach proposed by Ledoit and Wolf (2003) significantly. The results 1 and 2 are shown analytically. The results 3 and 4 are derived from an extensive simulation study.
.
On the Properties of Equally-Weighted Risk Contributions Portfolios
Maillard, Sébastien, Thierry Roncalli and Jerome Teiletche (2008) “On the Properties of Equally-Weighted Risk Contributions Portfolios” (September 22, 2008). Available at SSRN:http://ssrn.com/abstract
Abstract
Minimum variance and equally-weighted portfolios have recently prompted great interest both from academic researchers and market practitioners, as their construction does not rely on expected average returns and is therefore assumed to be robust. In this paper, we consider a related approach, where the risk contribution from each portfolio components is made equal, which maximizes diversification of risk (at least on an ex-ante basis). Roughly speaking, the resulting portfolio is similar to a minimum variance portfolio subject to a diversification constraint on the weights of its components. We derive the theoretical properties of such a portfolio and show that its volatility is located between those of minimum variance and equally-weighted portfolios. Empirical applications confirm that ranking. All in all, equally-weighted risk contributions portfolios appear to be an attractive alternative to minimum variance and equally-weighted portfolios and might be considered a good trade-off between those two approaches in terms of absolute level of risk, risk budgeting and diversification.
A Note on the Return Behavior of high risk common stocks
McEnally, Richard (1974) “A Note on the Return Behavior of high risk common stocks” Journal of Finance, (March 1974), Vol 29, No. 1, pp. 199-202. Available at JSTOR:http://www.jstor.org/discover/
Abstract
High risk common stocks, it is frequently observed, do not appear to generate returns commonsurate with the level of associated risk. This conclusion has been reached in investigations which utilize return behavior constructs of risk as well as in these which quantify risk by reference to “agency ratings” of investment quality.
Estimating the Global Minimum Variance Portfolio
Memmel, Christoph and Alexander Kempf (2006), “Estimating the Global Minimum Variance Portfolio”, Schmalenbach Business Review, Vol. 58, October 2006. Available at SSRN: http://ssrn.com/abstract
Abstract
According to standard portfolio theory, the tangency portfolio is the only efficient stock portfolio. However, empirical studies show that an investment in the global minimum variance portfolio often yields better out-of-sample results than does an investment in the tangency portfolio and suggest investing in the global minimum variance portfolio. But little is known about the distributions of the weights and return parameters of this portfolio. Our contribution is to determine these distributions. By doing so, we answer several important questions in asset management.
Risk Uncertainty and divergence of opinion
Miller, Ed (1977) “Risk Uncertainty and divergence of opinion” Journal of Finance, (September 1977), Vol 32, No. 4, pp. 1151-1168. Available at JSTOR:http://www.jstor.org/discover/
Abstract
This paper will explore some of the implications of a market with restricted short selling in which investors have differing estimates from investing in a risky security.
Why the Low Returns to Beta and Other Forms of Risk
Miller, Ed (2001), “Why the Low Returns to Beta and Other Forms of Risk”, The Journal of Portfolio Management, Winter 2001, Vol. 27, No. 2: pp. 40-55.
Available at iijournals: http://www.iijournals.com
Abstract
High–beta stocks typically fail to outperform low–beta stocks. Investors have heterogeneous opinions about value, and the difference between the return expected by the marginal investor and by the typical investor increases with the divergence of opinion. The author suggests that the divergence of opinion diminishes following an initial public offering, producing long–run underperformance of such offerings. Because divergence of opinion, uncertainty, and beta risk are correlated, according to the author, this causes an uncertainty–induced bias that increases with beta, producing a relatively flat security market line, flatter than the risk–return relationship anticipated by the typical investor. An implication of this theory is that investors can improve their return relative to risk by exploiting the flatness of the security market line.
A New Look at Minimum Variance Investing
Scherer, Bernd (2010) “A New Look at Minimum Variance Investing” (July 1, 2010). Available at SSRN: http://ssrn.com/abstract=1681306 or http://SSRN/abstract
Abstract
Disappointed with the performance of market weighted benchmark portfolios yet skeptical about the merits of active portfolio management, investors in recent years turned to alternative index definitions. Minimum variance investing is one of these popular rule driven, i.e. new passive concepts. I show in this paper theoretically and empirically that the portfolio construction process behind minimum variance investing implicitly picks up risk-based pricing anomalies. In other words the minimum variance tends to hold low beta and low residual risk stocks. Long/short portfolios based on these characteristics have been associated in the empirical literature with risk-adjusted outperformance (alpha). This paper shows that 83% of the variation of the minimum variance portfolio excess returns (relative to a capitalization weighted alternative) can be attributed to the FAMA/FRENCH factors as well as to the returns on two characteristic anomaly portfolios. All regression coefficients (factor exposures) are highly significant, stable over the estimation period and correspond remarkably well with our economic intuition.
The Volatility Effect: Lower Risk Without Lower Return
Blitz, David and Pim van Vliet (2007), “The Volatility Effect: Lower Risk Without Lower Return” Journal of Portfolio Management, pp. 102-113, Fall 2007; ERIM Report Series Reference No. ERS-2007-044-F&A. Available at SSRN: http://ssrn.com/
Abstract
We present empirical evidence that stocks with low volatility earn high risk-adjusted returns. The annual alpha spread of global low versus high volatility decile portfolios amounts to 12% over the 1986-2006 period. We also observe this volatility effect within the US, European and Japanese markets in isolation. Furthermore, we find that the volatility effect cannot be explained by other well-known effects such as value and size. Our results indicate that equity investors overpay for risky stocks. Possible explanations for this phenomenon include (i) leverage restrictions, (ii) inefficient two-step investment processes, and (iii) behavioral biases of private investors. In order to exploit the volatility effect in practice we argue that investors should include low risk stocks as a separate asset class in the strategic asset allocation phase of their investment process.
The Low Volatility Effect: A Comprehensive Look
Soe, Aye M., The Low Volatility Effect: A Comprehensive Look (August 1, 2012). Available at SSRN: http://ssrn.com/abstract=2128634
Abstract
We analyze the low volatility effect in the U.S equity market with a focus on the common properties of various low volatility strategies. We examine the two major approaches to constructing low volatility portfolios and apply them to the U.S. equity market: mean-variance optimization-based versus the rankings or quantile-based approaches. Our analysis shows that both approaches are equally effective in reducing portfolio volatility over a long-term investment horizon. We then extend our analysis to the international and emerging markets. Our findings confirm that the low volatility effect is not unique to the U.S. equity markets; it is present on a global scale.
Risk-Premium Curves for Different Classes of Long Term Securities
Soldofsky, Robert and Roger Miller(1969) “Risk-Premium Curves for Different Classes of Long Term Securities” Journal of Finance, (June 1969), Vol 24, No. 3, pp. 429-445. Available at JSTOR:http://www.jstor.org/discover/
Abstract
One of the two major objectives of this paper will be to present risk-premium curves, representing the trade-off between risk and return, for a very wide spectrum of risk classes of securities for the period, 1950-1956.
Why Low-Volatility Stocks Outperform: Market Evidence on Systematic Risk Versus Mispricing
Sullivan, Rodney and Xi Li (2010) “Why Low-Volatility Stocks Outperform: Market Evidence on Systematic Risk Versus Mispricing” (December 21, 2010). Available at SSRN:http://ssrn.com/abstract
Abstract
We explore whether the well publicized anomalous returns associated low-volatility stocks can be attributed to market mispricing or to compensation for higher systematic risk. Our results, conducted over a 46 year study period (1962-2008), indicate that the high returns related to low-volatility portfolios cannot be viewed as compensation for systematic factor risk. Instead, the excess returns are more likely to be driven by market mispricing as perhaps associated with an imperfection such as some investor irrationality connected with volatility.
The Limits to Arbitrage Revisited: The Low-Risk Anomaly
Sullivan, Rodney XI Li, and Luis García-Feijóo (2012) “The Limits to Arbitrage Revisited: The Low-Risk Anomaly” Financial Analysts Journal (February 2012). Available at SSRN:http://ssrn.com/abstract
Abstract
We show that over a long study period (1963-2010), the efficacy of trading the well-known low-volatility stock anomaly more limited than widely believed. In particular, extracting excess returns associated with a zero-cost portfolio is meaningfully hampered by high transaction costs reflecting that the abnormal returns are concentrated among low liquidity stocks. Adding to the challenge, the anomalous excess returns quickly reverse requiring traders to rebalance frequently in attempting to extract profits, thus amplifying liquidity needs. Our findings are unchanged for various approaches to measuring the low-volatility anomaly.
.
Managed Volatility: A New Approach to Equity Investing
Thomas, Ric and Robert Shapiro (2009) “Managed Volatility: A New Approach to Equity Investing” Journal of Investing, Spring 2009, Vol. 18, No. 1: pp. 15-23. Available at SSRN:http://www.iijournals.com
Abstract
In this study we argue that managed-volatility equity strategies are likely to gain greater acceptance in the marketplace over time. In particular, portfolios created with the goal of maximizing total return while controlling total volatility have historically dominated cap-weighted market portfolios in both risk and return. This dominance is due to the inability of the CAPM beta to explain returns. We conclude that insurance companies as well as other liability-focused institutional investors will increasingly allocate funds to equity strategies that target low total volatility.
Is the Relation between Volatility and Expected Stock Returns Positive, Flat or Negative?
Van Vliet, Pim, David Blitz and Bart Van der Grient (2011), “Is the Relation between Volatility and Expected Stock Returns Positive, Flat or Negative?” (July 2011). Available at SSRN: http://ssrn.com/abstract
Abstract
Theoretical models, such as the CAPM, predict a positive relation between risk and return, but the empirical evidence paints a mixed picture. Positive, flat and negative relations have been reported in various empirical studies. In this paper we reconcile these seemingly conflicting results by showing how methodological choices can lead to different, or even opposite conclusions. In our 1963-2009 U.S. sample we find that the empirical relation between historical volatility and expected returns is negative, with an average quintile return spread of -3.7%.The relation becomes 2% less negative when small caps are excluded, but 3% more negative when compounding effects are taken into account. We also argue that the positive relation between volatility and expected return reported by some studies can be attributed to various kinds of look-ahead bias. Our results provide an empirical basis for low-volatility and minimum-variance investment approaches.
Benchmarking Low-Volatility Strategies
Van Vliet, Pim and David Blitz (2011), “Benchmarking Low-Volatility Strategies” Journal of Index Investing, Vol. 2, No. 1, pp. 44-49, 2011. Available at SSRN: http://ssrn.com/abstract
Abstract
In this paper we discuss the benchmarking of low-volatility investment strategies, which are designed to benefit from the empirical result that low-risk stocks tend to earn high risk-adjusted returns. Although the minimum-variance portfolio of Markowitz is the ultimate low-volatility portfolio, we argue that it is not a suitable benchmark, as it can only be determined with hindsight. This problem is overcome by investable minimum-variance strategies, but because various approaches are equally effective at minimizing volatility it is ambiguous to elevate the status of any one particular approach to benchmark. As an example we discuss the recently introduced MSCI Minimum Volatility indices and conclude that these essentially resemble active low-volatility investment strategies themselves, rather than a natural benchmark for such strategies. In order to avoid these issues, we recommend to simply benchmark low-volatility managers against the capitalization-weighted market portfolio, using risk-adjusted performance metrics such as Sharpe ratio or Jensen’s alpha.
