Time Series Momentum

doi:10.2139/SSRN.2089463

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Time Series Momentum

Journal Article•DOI•

Time Series Momentum

Tobias J. Moskowitz¹, Tobias J. Moskowitz², Yao Hua Ooi, Lasse Heje Pedersen³•Institutions (3)

Yale University¹, National Bureau of Economic Research², Copenhagen Business School³

01 Sep 2011-Social Science Research Network-

TL;DR: In this article, the authors document significant time series momentum in equity index, currency, commodity, and bond futures for each of the 58 liquid instruments they consider, and find persistence in returns for 1 to 12 months that partially reverses over longer horizons.

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Abstract: We document significant “time series momentum” in equity index, currency, commodity, and bond futures for each of the 58 liquid instruments we consider. We find persistence in returns for 1 to 12 months that partially reverses over longer horizons, consistent with sentiment theories of initial under-reaction and delayed over-reaction. A diversified portfolio of time series momentum strategies across all asset classes delivers substantial abnormal returns with little exposure to standard asset pricing factors and performs best during extreme markets. Examining the trading activities of speculators and hedgers, we find that speculators profit from time series momentum at the expense of hedgers.

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Journal Article•DOI•

Betting Against Beta

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Andrea Frazzini, Lasse Heje Pedersen

09 Oct 2011-Social Science Research Network

TL;DR: This paper presented a model with leverage and margin constraints that vary across investors and time, and found evidence consistent with each of the model's five central predictions: constrained investors bid up high-beta assets, high beta is associated with low alpha, as they find empirically for U.S. equities, 20 international equity markets, Treasury bonds, corporate bonds, and futures.

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Abstract: We present a model with leverage and margin constraints that vary across investors and time. We find evidence consistent with each of the model’s five central predictions: (1) Since constrained investors bid up high-beta assets, high beta is associated with low alpha, as we find empirically for U.S. equities, 20 international equity markets, Treasury bonds, corporate bonds, and futures; (2) A betting-against-beta (BAB) factor, which is long leveraged low beta assets and short high-beta assets, produces significant positive risk-adjusted returns; (3) When funding constraints tighten, the return of the BAB factor is low; (4) Increased funding liquidity risk compresses betas toward one; (5) More constrained investors hold riskier assets.

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1,431 citations

Cites background from "Time Series Momentum"

...For more details on the computation of returns and data sources, see Moskowitz, Ooi, and Pedersen (2012), Appendix A....
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Journal Article•DOI•

Value and Momentum Everywhere: Value and Momentum Everywhere

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Clifford S. Asness, Tobias J. Moskowitz, Lasse Heje Pedersen

01 Jun 2013-Journal of Finance

1,407 citations

Journal Article•DOI•

Does Academic Research Destroy Stock Return Predictability

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R. David McLean, Jeffrey Pontiff

14 Jan 2016-Journal of Finance

TL;DR: In this paper, the authors study the out-of-sample and post-publication return predictability of 97 variables shown to predict cross-sectional stock returns and find that publication-informed trading results in a lower return.

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Abstract: We study the out-of-sample and post-publication return predictability of 97 variables shown to predict cross-sectional stock returns. Portfolio returns are 26% lower out-of-sample and 58% lower post-publication. The out-of-sample decline is an upper bound estimate of data mining effects. We estimate a 32% (58%–26%) lower return from publication-informed trading. Post-publication declines are greater for predictors with higher in-sample returns, and returns are higher for portfolios concentrated in stocks with high idiosyncratic risk and low liquidity. Predictor portfolios exhibit post-publication increases in correlations with other published-predictor portfolios. Our findings suggest that investors learn about mispricing from academic publications.

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993 citations

Journal Article•DOI•

International Momentum Strategies

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Susan M. Mangiero

01 Aug 1998-Cfa Digest

753 citations

Journal Article•DOI•

Investor sentiment aligned: : A powerful predictor of stock returns

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Dashan Huang, Fuwei Jiang, Jun Tu, Guofu Zhou¹•Institutions (1)

Washington University in St. Louis¹

01 Mar 2015-Review of Financial Studies

TL;DR: This article proposed a new investor sentiment index that is aligned with the purpose of predicting the aggregate stock market by eliminating a common noise component in sentiment proxies, the new index has much greater predictive power than existing sentiment indices have both in and out of sample, and the predictability becomes both statistically and economically significant.

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Abstract: We propose a new investor sentiment index that is aligned with the purpose of predicting the aggregate stock market. By eliminating a common noise component in sentiment proxies, the new index has much greater predictive power than existing sentiment indices have both in and out of sample, and the predictability becomes both statistically and economically significant. In addition, it outperforms well-recognized macroeconomic variables and can also predict cross-sectional stock returns sorted by industry, size, value, and momentum. The driving force of the predictive power appears to stem from investors' biased beliefs about future cash flows.

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684 citations

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References

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Book•

Judgment Under Uncertainty: Heuristics and Biases

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Amos Tversky¹, Daniel Kahneman¹•Institutions (1)

Hebrew University of Jerusalem¹

01 Jan 1974

TL;DR: The authors described three heuristics that are employed in making judgements under uncertainty: representativeness, availability of instances or scenarios, and adjustment from an anchor, which is usually employed in numerical prediction when a relevant value is available.

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Abstract: This article described three heuristics that are employed in making judgements under uncertainty: (i) representativeness, which is usually employed when people are asked to judge the probability that an object or event A belongs to class or process B; (ii) availability of instances or scenarios, which is often employed when people are asked to assess the frequency of a class or the plausibility of a particular development; and (iii) adjustment from an anchor, which is usually employed in numerical prediction when a relevant value is available. These heuristics are highly economical and usually effective, but they lead to systematic and predictable errors. A better understanding of these heuristics and of the biases to which they lead could improve judgements and decisions in situations of uncertainty.

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31,082 citations

Journal Article•DOI•

Common risk factors in the returns on stocks and bonds

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Eugene F. Fama¹, Kenneth R. French¹•Institutions (1)

University of Chicago¹

01 Feb 1993-Journal of Financial Economics

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.

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24,874 citations

"Time Series Momentum" refers background in this paper

...…volatility of 12% per year over the sample period 1985–2009, which is roughly the level of volatility exhibited by other factors such as those of Fama and French (1993) and Asness, Moskowitz, and Pedersen (2010).8 The TSMOM return for any instrument s at time t is therefore: rTSMOM,st,tþ1 ¼…...
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Journal Article•DOI•

Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency

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Narasimhan Jegadeesh, Sheridan Titman

01 Mar 1993-Journal of Finance

TL;DR: In this article, the authors show that strategies that buy stocks that have performed well in the past and sell stocks that had performed poorly in past years generate significant positive returns over 3- to 12-month holding periods.

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Abstract: This paper documents that strategies which buy stocks that have performed well in the past and sell stocks that have performed poorly in the past generate significant positive returns over 3- to 12-month holding periods. We find that the profitability of these strategies are not due to their systematic risk or to delayed stock price reactions to common factors. However, part of the abnormal returns generated in the first year after portfolio formation dissipates in the following two years. A similar pattern of returns around the earnings announcements of past winners and losers is also documented

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10,806 citations

"Time Series Momentum" refers background or methods in this paper

...…their underlying drivers, and relation to theory, we decompose the returns to a 1 Cross-sectional momentum has been documented in US equities (Jegadeesh and Titman, 1993; Asness, 1994), other equity markets (Rouwenhorst, 1998), industries (Moskowitz and Grinblatt, 1999), equity indexes…...
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...(Jegadeesh and Titman, 1993; Asness, 1994), other equity markets (Rouwenhorst, 1998), industries (Moskowitz and Grinblatt, 1999), equity indexes (Asness, Liew, and Stevens, 1997; Bhojraj and Swaminathan, 2006), currencies (Shleifer and Summers, 1990), commodities (Erb and Harvey, 2006; Gorton, Hayashi, and Rouwenhorst, 2008), and global bond futures (Asness, Moskowitz, and Pedersen, 2010)....
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...We derive this single time series of returns following the methodology used by Jegadeesh and Titman (1993): The return at time t represents the average return across all portfolios at that time, namely the return on the portfolio that was constructed last month, the month before that (and still held if the holding period h is greater than two), and so on for all currently ‘‘active’’ portfolios....
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...We derive this single time series of returns following the methodology used by Jegadeesh and Titman (1993): The return at time t represents the average return across all portfolios at that time, namely the return on the portfolio that was constructed last month, the month before that (and still…...
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Posted Content•

A Theory of Fads, Fashion, Custom, and Cultural Change as Informational Cascades

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Sushil Bikhchandani¹, Ivo Welch¹, Ivo Welch², David Hirshleifer³, David Hirshleifer² - Show less +1 more•Institutions (3)

University of California, Los Angeles¹, National Bureau of Economic Research², University of California, Irvine³

01 Mar 1992-Social Science Research Network

TL;DR: It is argued that localized conformity of behavior and the fragility of mass behaviors can be explained by informational cascades.

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Abstract: An informational cascade occurs when it is optimal for an individual, having observed the actions of those ahead of him, to follow the behavior of the preceding individual without regard to his own information. We argue that localized conformity of behavior and the fragility of mass behaviors can be explained by informational cascades.

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5,412 citations

"Time Series Momentum" refers background or methods in this paper

...…Hirshleifer, and Subrahmanyam, 1998), the representativeness heuristic (Barberis, Shleifer, and Vishny, 1998; Tversky and Kahneman, 1974), herding (Bikhchandani, Hirshleifer, and Welch, 1992), or general sentiment (Baker and Wurgler, 2006, 2007). time series and cross-sectional momentum strategy…...
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...The second row of Panel C of Table 3 reports results using the Treasury Eurodollar (TED) spread, a proxy for funding liquidity as suggested by Brunnermeier and Pedersen (2009), Asness, Moskowitz, and Pedersen (2010), and Garleanu and Pedersen (2011), and the top 20% most extreme realizations of the TED spread to capture the most illiquid funding environments....
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Posted Content•

The Limits of Arbitrage

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Andrei Shleifer¹, Andrei Shleifer², Robert W. Vishny³, Robert W. Vishny¹•Institutions (3)

National Bureau of Economic Research¹, Harvard University², University of Chicago³

01 Jul 1995-Social Science Research Network

TL;DR: In this paper, the authors show that arbitrage is performed by a relatively small number of highly specialized investors who take large positions using other people's money, which has a number of interesting implications for security pricing.

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Abstract: In traditional models, arbitrage in a given security is performed by a large number of diversified investors taking small positions against its mispricing. In reality, however, arbitrage is conducted by a relatively small number of highly specialized investors who take large positions using other people's money. Such professional arbitrage has a number of interesting implications for security pricing, including the possibility that arbitrage becomes ineffective in extreme circumstances, when prices diverge far from fundamental values. The model also suggests where anomalies in financial markets are likely to appear, and why arbitrage fails to eliminate them.

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3,997 citations