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Noah Beck

Bio: Noah Beck is an academic researcher. The author has contributed to research in topics: Valuation (finance) & Smart beta. The author has an hindex of 4, co-authored 8 publications receiving 131 citations.

Papers
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Journal ArticleDOI
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.

51 citations

Journal ArticleDOI
TL;DR: In this paper, the authors find that many of the documented factors lack robustness, whereas value, momentum, illiquidity, and low beta are more robust than other factors, and they find that investors may be better off accessing these factors through active management rather than indexation.
Abstract: The multifactor investing framework has become very popular in the indexing community. Both academic and practitioner researchers have documented hundreds of equity factors. But which of these factors are likely to profit investors once implemented? We find that many of the documented factors lack robustness. Size and quality, two of the more prominent factors, show weak robustness, whereas value, momentum, illiquidity, and low beta are more robust. Further examining implementation characteristics, we find that liquidity-demanding factors, such as illiquidity and momentum, are associated with significantly higher trading costs than are other factors. Investors may be better off accessing these factors through active management rather than indexation.

31 citations

Journal ArticleDOI
TL;DR: In this article, the authors show that valuations are predictive of future returns and that performance can be improved by emphasizing the factors or strategies that are trading cheap relative to their historical norms and by deemphasizing the more expensive factor or strategies.
Abstract: In our paper — “How Can ‘Smart Beta’ Go Horribly Wrong?” — we show that performance chasing can be as dangerous in smart beta as it is in stock selection, fund selection, or asset allocation. We differentiate between “revaluation alpha” and “structural alpha.” The former is the part of the past return that came from rising valuations. Revaluation alpha is nonrecurring, and is at least as likely to reverse as to persist. Rising valuations create an illusion of alpha and encourage performance chasing. Structural alpha is the part of the past return that was delivered net of any impact from rising valuations. Why do we emphasize rising valuations? Because factors and strategies with tumbling valuations are rarely noticed in the data mining so pervasive throughout the finance community. For some factors, such as low beta, we show that most or all past performance was revaluation alpha, which could easily reverse from current valuation levels. For smart beta strategies, the picture is a bit better: most established products have respectable structural alpha. In our paper “To Win with ‘Smart Beta’ Ask If the Price Is Right,” we show that valuations are predictive of future returns. We demonstrate that this result is robust across time, in international and emerging markets, and holds for various metrics used to measure valuations. We also point out that — for the moment, at least — many so-called smart beta strategies are trading in the top quartile, and even top decile, of historical valuations. We caution those who believe past is prologue and are tempted to extrapolate past “alpha” into expected future returns without regard to current valuation levels. In this paper we explore whether active timing of smart beta strategies and/or factor tilts can benefit investors. We find that performance can easily be improved by emphasizing the factors or strategies that are trading cheap relative to their historical norms and by deemphasizing the more expensive factors or strategies. We also observe that aggressive bets (favoring only the cheapest factor or smart beta strategy) can severely erode Sharpe ratios, so that gentle or moderate tilts toward that factor or strategy would seem to be a sensible compromise. Finally, we note that both factor and smart beta strategies have typically been identified and accepted as potentially alpha generating by the finance and investing communities after a period of impressive success — indeed, many of our own tests include a span that predates their discovery. We show that out-of-sample tests, after a strategy or factor has been discovered, are often far less impressive.

29 citations

Journal ArticleDOI
TL;DR: In this paper, the authors show that the relative valuation of a strategy (in comparison with its own historical norms) is correlated with the strategy's subsequent return at a five-year horizon.
Abstract: In our paper — “How Can ‘Smart Beta’ Go Horribly Wrong?” — we show, using U.S. data, that the relative valuation of a strategy (in comparison with its own historical norms) is correlated with the strategy’s subsequent return at a five-year horizon. The high past performance of many of the increasingly popular factor tilt and so-called smart beta2 strategies came, in large measure, from rising valuations. These excess returns are an “alpha mirage” attributable to the strategies’ becoming more expensive relative to the market. Even the statistical significance of past performance was an illusion driven by rising relative valuation levels! Today, only the value category shows some degree of relative cheapness, precisely because its recent performance has been weak. Over the past half-century, almost all of the eight factors and eight smart beta strategies we study exhibit a negative relationship between starting valuation and subsequent five-year performance. Today, valuations of many of the most popular factors and smart beta strategies are well above their historical norms, forecasting lower future returns. Our findings are robust for both factors and smart beta strategies across horizons out to five years, using both a simple price-to-book ratio and an aggregate valuation measure, in U.S., developed ex U.S., and emerging markets.

19 citations

Journal ArticleDOI
TL;DR: This paper showed that relative valuations predict subsequent returns for both factors and smart beta strategies in exactly the same way price matters in stock selection and asset allocation, and that any mean reversion toward the smart beta strategy's historical normal relative valuation could transform lofty historical alpha into negative future alpha.
Abstract: In a series of papers we published in 2016, we show that relative valuations predict subsequent returns for both factors and smart beta strategies in exactly the same way price matters in stock selection and asset allocation. To many, one surprising revelation in that series is that a number of “smart beta” strategies are expensive today relative to their historical valuations. The fact they are expensive has two uncomfortable implications. The first is that the past success of a smart beta strategy—often only a simulated past performance—is partly a consequence of “revaluation alpha” arising because many of these strategies enjoy a tailwind as they become more expensive. We, as investors, extrapolate that part of the historical alpha at our peril. The second implication is that any mean reversion toward the smart beta strategy’s historical normal relative valuation could transform lofty historical alpha into negative future alpha. As with asset allocation and stock selection, relative valuations can predict the long-term future returns of strategies and factors—not precisely, nor with any meaningful short-term timing efficacy, but well enough to add material value. These findings are robust to variations in valuation metrics, geographies, and time periods used for estimation.

4 citations


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Abstract: investigation, verification or monitoring by us of any content or information contained within or accessible from the linked sites. If you choose to visit the linked sites, you do so at your own risk, and you will be subject to such sites' terms of use and privacy policies, over which AQR.com has no control. In no event will AQR be responsible for any information or content within the linked sites or your use of the linked sites.

211 citations

Repository
Fotios Petropoulos, Daniele Apiletti1, Vassilios Assimakopoulos2, Mohamed Zied Babai3, Devon K. Barrow4, Souhaib Ben Taieb5, Christoph Bergmeir6, Ricardo J. Bessa, Jakub Bijak7, John E. Boylan8, Jethro Browell9, Claudio Carnevale10, Jennifer L. Castle11, Pasquale Cirillo12, Michael P. Clements13, Clara Cordeiro14, Clara Cordeiro15, Fernando Luiz Cyrino Oliveira16, Shari De Baets17, Alexander Dokumentov, Joanne Ellison7, Piotr Fiszeder18, Philip Hans Franses19, David T. Frazier6, Michael Gilliland20, M. Sinan Gönül, Paul Goodwin21, Luigi Grossi22, Yael Grushka-Cockayne23, Mariangela Guidolin22, Massimo Guidolin24, Ulrich Gunter25, Xiaojia Guo26, Renato Guseo22, Nigel Harvey27, David F. Hendry11, Ross Hollyman21, Tim Januschowski28, Jooyoung Jeon29, Victor Richmond R. Jose30, Yanfei Kang31, Anne B. Koehler32, Stephan Kolassa8, Nikolaos Kourentzes33, Nikolaos Kourentzes8, Sonia Leva, Feng Li34, Konstantia Litsiou35, Spyros Makridakis36, Gael M. Martin6, Andrew B. Martinez37, Andrew B. Martinez38, Sheik Meeran, Theodore Modis, Konstantinos Nikolopoulos39, Dilek Önkal, Alessia Paccagnini40, Alessia Paccagnini41, Anastasios Panagiotelis42, Ioannis P. Panapakidis43, Jose M. Pavía44, Manuela Pedio45, Manuela Pedio24, Diego J. Pedregal46, Pierre Pinson47, Patrícia Ramos48, David E. Rapach49, J. James Reade13, Bahman Rostami-Tabar50, Michał Rubaszek51, Georgios Sermpinis9, Han Lin Shang52, Evangelos Spiliotis2, Aris A. Syntetos50, Priyanga Dilini Talagala53, Thiyanga S. Talagala54, Len Tashman55, Dimitrios D. Thomakos56, Thordis L. Thorarinsdottir57, Ezio Todini58, Juan Ramón Trapero Arenas46, Xiaoqian Wang31, Robert L. Winkler59, Alisa Yusupova8, Florian Ziel60 
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

01 Jan 2014
TL;DR: In this article, the authors prove that high performance is easily achievable after backtesting a relatively small number of alternative strategy con-gurations, a practice they denote "backtest over-tting."
Abstract: Recent computational advances allow investment managers to methodically search through thousands or even millions of potential options for a pro�table investment strategy. In many instances, the resulting strategy involves a pseudo-mathematical argument, which is spuriously validated through a simulation of its historical performance (also called backtest). We prove that high performance is easily achievable after backtesting a relatively small number of alternative strategy con�gurations, a practice we denote \backtest over�tting." The higher the number of con�gurations tried, the greater is the probability that the backtest is over�t. Because �nancial analysts rarely report the number of con�gurations tried for a given backtest, investors cannot evaluate the degree of over�tting in most investment claims and analysis. The implication is that investors can be easily misled into allocating capital to strategies that appear to be mathematically sound and empirically supported by a backtest. This practice is particularly pernicious, because due to the nature of �nancial time series, backtest over�tting has a detrimental e�ect on the strategy's future performance.

102 citations

Journal ArticleDOI
TL;DR: There are two primary factors that affect expected returns for companies with high ESG (environmental, social and governance) ratings: investor preferences and risk as mentioned in this paper. But, the jury remains out on whether there is an ESG-related risk factor.
Abstract: There are two primary factors that affect expected returns for companies with high ESG (environmental, social and governance) ratings—investor preferences and risk. Although investor preferences for highly rated ESG companies can lower the cost of capital, the flip side of the coin is lower expected returns for investors. Regarding risk, the jury remains out on whether there is an ESG‐related risk factor. However, to the extent, ESG is a risk factor it also points towards lower expected returns for investments in highly rated companies. Though ESG investing may have social benefits, higher expected returns for investors are not among them.

75 citations

Journal ArticleDOI
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