Trends everywhere? The case of hedge fund styles


This paper investigates empirically whether time-series momentum returns can explain the performance of hedge funds in the cross section. Relying on the trend-following literature, a volatility-adjusted time-series momentum signal is applied on a daily basis across a large set of futures, covering the major asset classes. We build a hierarchical set of trend factors: the full version TREND can be split in summable factors across two dimensions: the horizon of the signals and the traded asset class. We show that Managed Futures, Global Macro and Fund of Hedge Funds strategies can be partly explained by a TREND exposure. Moreover, a TREND exposure is a significant determinant of hedge funds returns at the fund level, for Managed Futures and Global Macro but also, and more surprisingly, for the other styles.

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  1. 1.

    Backwardation and contango refer to the two possible shapes of a futures curve, the first relates to when the futures price is below the expected spot price and the opposite for the latter.

  2. 2.

  3. 3.

    Our definition of sub-periods is based on Edelman et al. (2012), who identify March 2009 as a structural break point associated with the end of credit crisis.

  4. 4.

    Instead of using the volatility, we can also build the factor by considering the risk contribution of each market to the final portfolio. In this case, we also include in the factor construction some information on correlation between markets. It could be a solution to get at the end of the process a more balanced factor, when the number of markets across asset classes is heterogeneous.

  5. 5.

    Performances of our factor are gross of fees (management, performance and expense fees) as well as gross of transaction costs.

  6. 6.

    We also modify the specification of Eq. (8) to compare the results we obtain when we replace in this equation the TREND factor by the cross-sectional equity momentum factor (WML). We obtain evidence that the TREND factor brings something else than the standard WML. The same conclusion occurs when we include both WML and TREND in the specification. All these results are available upon request.


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We thank Pedro Barroso, Paul Karehnke, Guillaume Monarcha and the people at KeyQuant, for various useful comments and suggestions. In addition, we are grateful to the participants of: the DRM Finance PhD Seminar, the Quantitative Finance and Financial Econometrics 2018 Conference, the Econometric Research in Finance 2018 Workshop, the 10th French Econometrics Conference, the 12th International Conference on Computational and Financial Econometrics, the 11th Annual Hedge Funds and Private Equity Research Conference, the 12th Financial Risks International Forum, the ANR MultiRisk 2019 Workshop and the 36th International Conference of the French Finance Association. We gratefully acknowledge the financial support of the chair QuantValley/Risk Foundation “Quantitative Management Initiative.” We are also grateful to the Agence Nationale de la Recherche (ANR), which supported this work via the Project MultiRisk (ANR-16-CE26-0015-02).

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Correspondence to Charles Chevalier.

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See Tables 20, 21.

Table 20 Description of the HFRI database
Table 21 Pearson correlation of F&H factors and TREND on a selection of 6 futures (S&P500, US10Y T-note, EUR/USD, Corn, Gold and Crude Oil), on the FH sub-period running from January 2010 to March 2016

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Chevalier, C., Darolles, S. Trends everywhere? The case of hedge fund styles. J Asset Manag 20, 442–468 (2019).

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  • Managed Futures
  • Time-series momentum
  • Trend following
  • Commodity Trading Advisor (CTA)
  • Hedge funds
  • Trading strategies

JEL Classification

  • G11
  • G12
  • G15
  • F37