Time Series Momentum

By Tobias J. Moskowitz, Yao Hua Ooi, Lasse Heje Pedersen. 2012

1,358 citations recorded by the source corpus.

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 one 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.

Subfields: Economics and Econometrics, Finance

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Citations in the corpus

Enhancing Time-Series Momentum Strategies Using Deep Neural Networks (2019, methodology): “…article, we introduce a novel class of hybrid models that combines deep learning–based trading signals with the volatility scaling framework used in time-series momentum strategies (Moskowitz, Ooi, and Pedersen 2012; Baltas and Kosowski 2017), which we refer to as deep momentum networks (DMNs).”
Understanding the Momentum Risk Premium: An In-Depth Journey Through Trend-Following Strategies (2017, background): “More recently, academics have investigated momentum investing in other asset classes and also found evidence in fixed-income and currency markets (Moskowitz et al., 2012; Asness et al., 2013).”
Forecasting the Equity Risk Premium: The Role of Technical Indicators (2014, neutral): “Empirically, Moskowitz et al. (2012) recently find that pervasive price trends exist across commonly traded equity index, currency, commodity, and bond futures.”
Information Percolation, Momentum and Reversal (2014, positive): “…information percolation at a higher intensity generates short-run momentum and long-run reversals (the dashed bars), consistent with Moskowitz et al. (2012).18 [insert Figure 3 here] Novy-Marx (2012) proposes an alternative measure of momentum, which includes past returns at both…”
A Tale of Two Premiums: The Role of Hedgers and Speculators in Commodity Futures Markets (2019, positive): “follow Moskowitz et al. (2012) and construct two liquidity provision portfolios based on cross-”
Risk Everywhere: Modeling and Managing Volatility (2018, neutral): “2 See also the benefits of volatility-timed time-series momentum factors (Moskowitz et al. 2012), value and momentum factors (Asness et al.”
Does It Pay to Pay Attention (2018, neutral): “Moskowitz, Ooi, and Pedersen (2012) document time-series momentum, that is, the positive predictability from a security’s own past returns that persists for up to 12 months.”
"Momentum Strategies in Futures Markets and Trend-following Funds" (2013, positive): “In agreement with the futures literature (e.g. see de Roon et al. 2000, Pesaran et al. 2009, Moskowitz et al. 2012), there exists large cross-sectional variation in the return distributions of the different contracts in our dataset.”
DeepLOB: Deep Convolutional Neural Networks for Limit Order Books (2018, background): “Practitioners often use moving averages with different decay weights to observe time-series momentum [63].”
A Random Forests Approach to Predicting Clean Energy Stock Prices (2021, neutral): “More recently, momentum and economic or psychology behavior factors have been identiﬁed as possible sources of stock price predictability (Gray and Vogel 2016; Lo et al. 2000; Christoffersen and Diebold 2006; Moskowitz et al. 2012).”