How Can 'Smart Beta' Go Horribly Wrong?
TL;DR: In this article, the authors predict a smart beta crash as a consequence of the soaring popularity of factor-tilt strategies, and the reasonable probability of such a crash is shown.
Abstract: Factor returns, net of changes in valuation levels, are much lower than recent performance suggests. Value-add can be structural, and thus reliably repeatable, or situational—a product of rising valuations—likely neither sustainable nor repeatable. Many investors are performance chasers who in pushing prices higher create valuation levels that inflate past performance, reduce potential future performance, and amplify the risk of mean reversion to historical valuation norms. We foresee the reasonable probability of a smart beta crash as a consequence of the soaring popularity of factor-tilt strategies.
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Polytechnic University of Turin1, National Technical University of Athens2, KEDGE Business School3, University of Birmingham4, University of Mons5, Monash University6, University of Southampton7, Lancaster University8, University of Glasgow9, University of Brescia10, University of Oxford11, Zürcher Fachhochschule12, University of Reading13, University of Lisbon14, University of the Algarve15, Pontifical Catholic University of Rio de Janeiro16, Ghent University17, Nicolaus Copernicus University in Toruń18, Erasmus University Rotterdam19, SAS Institute20, University of Bath21, University of Padua22, University of Virginia23, Bocconi University24, MODUL University Vienna25, University of Maryland, College Park26, University College London27, Amazon.com28, KAIST29, Georgetown University30, Beihang University31, Miami University32, University of Skövde33, Central University of Finance and Economics34, Manchester Metropolitan University35, University of Nicosia36, George Washington University37, United States Department of the Treasury38, Durham University39, University College Dublin40, Australian National University41, University of Sydney42, University of Thessaly43, University of Valencia44, University of Bristol45, University of Castilla–La Mancha46, Technical University of Denmark47, Polytechnic Institute of Porto48, Saint Louis University49, Cardiff University50, Warsaw School of Economics51, Macquarie University52, University of Moratuwa53, University of Sri Jayewardenepura54, International Institute of Minnesota55, National and Kapodistrian University of Athens56, Norwegian Computing Center57, University of Bologna58, Duke University59, University of Duisburg-Essen60
TL;DR: A non-systematic review of the theory and the practice of forecasting, offering a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts.
Abstract: Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts.
We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases.
163 citations
Cites background from "How Can 'Smart Beta' Go Horribly Wr..."
...The idea of timing exposure to styles is therefore at least superficially attractive, although the feasibility of doing so is a matter of some debate (Arnott et al., 2016; Asness, 2016; Bender et al., 2018)....
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TL;DR: In this article, the authors estimate the risk premiums earned from factor investing over very long periods (up to 117 years) and across many markets ( up to 23) and report on the long-term profitability of following strategies based on market capitalization, value versus growth, dividend yield, stock-return momentum, and low-volatility investing.
Abstract: Factor investing is popular, and its adoption is accelerating. One reason it is increasingly being embraced is that portfolio return expectations seem to be evidence based. However, much of the so-called evidence consists of repeated analysis of the very datasets used to derive an investment model in the first place. To mitigate this trap, the authors estimate the risk premiums earned from factor investing over very long periods (up to 117 years) and across many markets (up to 23). They report on the long-term profitability of following strategies based on market capitalization, value versus growth, dividend yield, stock-return momentum, and low-volatility investing.
59 citations
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TL;DR: In this paper, the authors argue that factor timing has the potential of reintroducing a type of skill-based "active management" (as timing is generally thought of this way) back into the equation.
Abstract: Everyone seems to want to time factors. Often the first question after an initial discussion of factors is “ok, what’s the current outlook?” And the common answer, “the same as usual,” is often unsatisfying. There is powerful incentive to oversell timing ability. Factor investing is often done at fees in between active management and cap-weighted indexing and these fees have been falling over time. Factor timing has the potential of reintroducing a type of skill-based “active management” (as timing is generally thought of this way) back into the equation. I think that siren song should be resisted, even if that verdict is disappointing to some. At least when using the simple “value” of the factors themselves, I find such timing strategies to be very weak historically, and some tests of their long-term power to be exaggerated and/or inapplicable.
42 citations
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TL;DR: In this article, the authors search for predictors of value, size, momentum, quality, and minimum-volatility smart beta factors under different economic regimes and market conditions, and find that combining information from several predictors such as business cycle indicators, valuation, relative strength, and dispersion metrics is more effective than using individual predictors.
Abstract: What smart beta strategy should investors use and when? The authors search for predictors of value, size, momentum, quality, and minimum-volatility smart beta factors under different economic regimes and market conditions. They find that combining information from several predictors such as business cycle indicators, valuation, relative strength, and dispersion metrics is more effective than using individual predictors.
39 citations
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TL;DR: Asness and Asness as discussed by the authors proposed a set of factors that both explain security returns and deliver a positive return premium (not necessarily the same things), and the spread between the factors is defined as the difference between the two factors.
Abstract: 1. Clifford S. Asness
Although consensus might be too strong a word, modern financial researchers have mostly coalesced behind a set of “factors” that both explain security returns and deliver a positive return premium (not necessarily the same things). A “factor” is the spread between the
38 citations
References
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TL;DR: In this article, the authors identify five common risk factors in the returns on stocks and bonds, including three stock-market factors: an overall market factor and factors related to firm size and book-to-market equity.
24,874 citations
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TL;DR: In this paper, the effects of stock illiquidity on stock return have been investigated and it was shown that expected market illiquidities positively affects ex ante stock excess return (usually called risk premium) over time.
Abstract: New tests are presented on the effects of stock illiquidity on stock return. Over time, expected market illiquidity positively affects ex ante stock excess return (usually called â¬Srisk premiumâ¬?). This complements the positive cross-sectional return-illiquidity relationship. The illiquidity measure here is the average daily ratio of absolute stock return to dollar volume, which is easily obtained from daily stock data for long time series in most stock markets. Illiquidity affects more strongly small firms stocks, suggesting an explanation for the changes â¬Ssmall firm effectâ¬? over time. The impact of market illiquidity on stock excess return suggests the existence of illiquidity premium and helps explain the equity premium puzzle.
5,333 citations
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TL;DR: In this article, the authors determine empirically whether the investment performance of common stocks is related to their P/E ratios, and they find that returns on stocks with low PE ratios tend to be larger than warranted by the underlying risks, even after adjusting for any additional search and transactions costs, and differential taxes.
Abstract: IN AN EFFICIENT CAPITAL MARKET, security prices fully reflect available information in a rapid and unbiased fashion and thus provide unbiased estimates of the underlying values. While there is substantial empirical evidence supporting the efficient market hypothesis,' many still question its validity. One such group believes that price-earnings (P/E) ratios are indicators of the future investment performance of a security. Proponents of this price-ratio hypothesis claim that low P/E securities will tend to outperform high P/E stocks.2 In short, prices of securities are biased, and the P/E ratio is an indicator of this bias.3 A finding that returns on stocks with low P/E ratios tends to be larger than warranted by the underlying risks, even after adjusting for any additional search and transactions costs, and differential taxes, would be inconsistent with the efficient market hypothesis.4 The purpose of this paper is to determine empirically whether the investment performance of common stocks is related to their P/E ratios. In Section II data, sample, and estimation procedures are outlined. Empirical results are discussed in Section III, and conclusions and implications are given in Section IV.
2,593 citations
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01 Jan 2012
TL;DR: In this paper, the effects of stock illiquidity on stock return have been investigated and it was shown that expected market illiquidities positively affects ex ante stock excess return (usually called risk premium) over time.
Abstract: New tests are presented on the effects of stock illiquidity on stock return. Over time, expected market illiquidity positively affects ex ante stock excess return (usually called â¬Srisk premiumâ¬?). This complements the positive cross-sectional return-illiquidity relationship. The illiquidity measure here is the average daily ratio of absolute stock return to dollar volume, which is easily obtained from daily stock data for long time series in most stock markets. Illiquidity affects more strongly small firms stocks, suggesting an explanation for the changes â¬Ssmall firm effectâ¬? over time. The impact of market illiquidity on stock excess return suggests the existence of illiquidity premium and helps explain the equity premium puzzle.
2,465 citations
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TL;DR: Discount-rate variation is the central organizing question of current asset-pricing research as discussed by the authors, and a survey of discount-rate theories and applications can be found in the survey.
Abstract: Discount-rate variation is the central organizing question of current asset-pricing research. I survey facts, theories, and applications. Previously, we thought returns were unpredictable, with variation in price-dividend ratios due to variation in expected cashflows. Now it seems all price-dividend variation corresponds to discount-rate variation. We also thought that the cross-section of expected returns came from the CAPM. Now we have a zoo of new factors. I categorize discount-rate theories based on central ingredients and data sources. Incorporating discount-rate variation affects finance applications, including portfolio theory, accounting, cost of capital, capital structure, compensation, and macroeconomics.
1,624 citations