Momentum has its moments

By Pedro Barroso, Pedro Santa-clara. 2015

618 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

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Citations in the corpus

Enhancing Time-Series Momentum Strategies Using Deep Neural Networks (2019, background): “This is particularly relevant as raw momentum strategies without adequate risk adjustments, such as volatility scaling [7], are susceptible to large crashes during periods of market panic [28, 29].”
Two centuries of trend following (2014, background): “As mentioned in the introduction, trending behaviour is also observed in the idiosyncratic component of individual stocks [24, 25, 26, 10].”
Volatility‐Managed Portfolios (2017, background): “Barroso and Santa-Clara (2015) and Daniel and Moskowitz (2015) study volatility timing related to momentum crashes.”
Cross-Sectional and Time-Series Tests of Return Predictability: What Is the Difference? (2018, background): “Such time variation is an incidental feature of TS strategies, but Barroso and Santa-Clara (2015) and Moreira and Muir (2017) show that inverse volatility scaling increases the Sharpe ratios of CS momentum strategies and other anomalies.”
Tree-Based Conditional Portfolio Sorts: The Relation between Past and Future Stock Returns (2016, neutral): “Section 5.2 focuses on the year 2009, when momentum strategies did not perform well (see Daniel and Moskowitz (2015) or Barroso and Santa-Clara (2015)).”
Forecasting: theory and practice (2020, background): “time exposure to momentum factor portfolios risks unwittingly compounding exposure. A related strand of research relates (own) factor volatility to future returns, in particular for momentum factors (Barroso, 2015; Daniel and Moskowitz, 2016). 2.Valuation Spreads: Using value signals (aggregated fro”
The Impact of Volatility Targeting (2018, neutral): “Barroso and Santa-Clara (2015) and Daniel and Moskowitz (2016) studied risk-managed momentum strategies.”
Demystifying Time‐Series Momentum Strategies: Volatility Estimators, Trading Rules and Pairwise Correlations (2020, neutral): “Fleming, Kirby and Ostdiek (2001), Ilmanen and Kizer (2012), Kirby and Ostdiek (2012) and Hallerbach (2012) highlight the benefits of volatility-timing, while Barroso and Santa-Clara (2015) and Daniel and Moskowitz (2015) examine its effect on the performance of cross-sectional equity momentum strat”
Enhancing the momentum strategy through deep regression (2019, methodology): “However, it was observed in Barroso and Santa-Clara (2015) and Daniel and Moskowitz (2013) that the WML momentum strategy can occasionally experience devastating ‘market crashes’ that wipe out returns accumulated over multiple number of years prior to the crash. Specifically, it was reported in Dani”