Nothing Special   »   [go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/p/nbr/nberwo/23886.html
   My bibliography  Save this paper

Predicting Relative Returns

Author

Listed:
  • Valentin Haddad
  • Serhiy Kozak
  • Shrihari Santosh
Abstract
Across a variety of asset classes, we show that relative returns are highly predictable in the time series in and out of sample, much more so than aggregate returns. For Treasuries, slope is more predictable than level. For equities, dominant principal components of anomaly long-short strategies are more predictable than the market. For foreign exchange, a carry portfolio is more predictable than a basket of all currencies against the dollar. We show the commonly used practice to predict each individual asset is often equivalent to predicting only their first principal component, the index, which obscures the predictability of relative returns. Our findings highlight that focusing on important dimensions of the cross-section allows one to uncover additional economically relevant and statistically robust patterns of predictability.

Suggested Citation

  • Valentin Haddad & Serhiy Kozak & Shrihari Santosh, 2017. "Predicting Relative Returns," NBER Working Papers 23886, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:23886
    Note: AP IFM
    as

    Download full text from publisher

    File URL: http://www.nber.org/papers/w23886.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Tuomo Vuolteenaho, 2002. "What Drives Firm‐Level Stock Returns?," Journal of Finance, American Finance Association, vol. 57(1), pages 233-264, February.
    2. Adrian, Tobias & Crump, Richard K. & Moench, Emanuel, 2015. "Regression-based estimation of dynamic asset pricing models," Journal of Financial Economics, Elsevier, vol. 118(2), pages 211-244.
    3. Estrella, Arturo & Mishkin, Frederic S., 1997. "The predictive power of the term structure of interest rates in Europe and the United States: Implications for the European Central Bank," European Economic Review, Elsevier, vol. 41(7), pages 1375-1401, July.
    4. Scott Joslin & Marcel Priebsch & Kenneth J. Singleton, 2014. "Risk Premiums in Dynamic Term Structure Models with Unspanned Macro Risks," Journal of Finance, American Finance Association, vol. 69(3), pages 1197-1233, June.
    5. Bikbov, Ruslan & Chernov, Mikhail, 2010. "No-arbitrage macroeconomic determinants of the yield curve," Journal of Econometrics, Elsevier, vol. 159(1), pages 166-182, November.
    6. Akbas, Ferhat & Armstrong, Will J. & Sorescu, Sorin & Subrahmanyam, Avanidhar, 2015. "Smart money, dumb money, and capital market anomalies," Journal of Financial Economics, Elsevier, vol. 118(2), pages 355-382.
    7. John H. Cochrane & Monika Piazzesi, 2005. "Bond Risk Premia," American Economic Review, American Economic Association, vol. 95(1), pages 138-160, March.
    8. Hanno Lustig & Nikolai Roussanov & Adrien Verdelhan, 2011. "Common Risk Factors in Currency Markets," The Review of Financial Studies, Society for Financial Studies, vol. 24(11), pages 3731-3777.
    9. Shiller, Robert J, 1981. "Do Stock Prices Move Too Much to be Justified by Subsequent Changes in Dividends?," American Economic Review, American Economic Association, vol. 71(3), pages 421-436, June.
    10. John H. Cochrane, 2008. "The Dog That Did Not Bark: A Defense of Return Predictability," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1533-1575, July.
    11. Fama, Eugene F. & French, Kenneth R., 1988. "Dividend yields and expected stock returns," Journal of Financial Economics, Elsevier, vol. 22(1), pages 3-25, October.
    12. Ralph S.J. Koijen & Stijn Van Nieuwerburgh, 2011. "Predictability of Returns and Cash Flows," Annual Review of Financial Economics, Annual Reviews, vol. 3(1), pages 467-491, December.
    13. Ang, Andrew & Piazzesi, Monika, 2003. "A no-arbitrage vector autoregression of term structure dynamics with macroeconomic and latent variables," Journal of Monetary Economics, Elsevier, vol. 50(4), pages 745-787, May.
    14. Bryan Kelly & Seth Pruitt, 2013. "Market Expectations in the Cross-Section of Present Values," Journal of Finance, American Finance Association, vol. 68(5), pages 1721-1756, October.
    15. Sydney C. Ludvigson & Serena Ng, 2009. "Macro Factors in Bond Risk Premia," The Review of Financial Studies, Society for Financial Studies, vol. 22(12), pages 5027-5067, December.
    16. Kozak, Serhiy & Nagel, Stefan & Santosh, Shrihari, 2020. "Shrinking the cross-section," Journal of Financial Economics, Elsevier, vol. 135(2), pages 271-292.
    17. R. David Mclean & Jeffrey Pontiff, 2016. "Does Academic Research Destroy Stock Return Predictability?," Journal of Finance, American Finance Association, vol. 71(1), pages 5-32, February.
    18. Hanson, Samuel G. & Stein, Jeremy C., 2015. "Monetary policy and long-term real rates," Journal of Financial Economics, Elsevier, vol. 115(3), pages 429-448.
    19. Anna Cieslak & Pavol Povala, 2015. "Expected Returns in Treasury Bonds," The Review of Financial Studies, Society for Financial Studies, vol. 28(10), pages 2859-2901.
    20. Evans, Charles L. & Marshall, David A., 1998. "Monetary policy and the term structure of nominal interest rates: Evidence and theory," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 49(1), pages 53-111, December.
    21. Stambaugh, Robert F. & Yu, Jianfeng & Yuan, Yu, 2012. "The short of it: Investor sentiment and anomalies," Journal of Financial Economics, Elsevier, vol. 104(2), pages 288-302.
    22. Lustig, Hanno & Roussanov, Nikolai & Verdelhan, Adrien, 2014. "Countercyclical currency risk premia," Journal of Financial Economics, Elsevier, vol. 111(3), pages 527-553.
    23. Duffee, Gregory R., 2013. "Bond Pricing and the Macroeconomy," Handbook of the Economics of Finance, in: G.M. Constantinides & M. Harris & R. M. Stulz (ed.), Handbook of the Economics of Finance, volume 2, chapter 0, pages 907-967, Elsevier.
    24. John H. Cochrane, 2011. "Presidential Address: Discount Rates," Journal of Finance, American Finance Association, vol. 66(4), pages 1047-1108, August.
    25. repec:hal:journl:peer-00732517 is not listed on IDEAS
    26. Stefano Giglio & Dacheng Xiu, 2017. "Inference on Risk Premia in the Presence of Omitted Factors," NBER Working Papers 23527, National Bureau of Economic Research, Inc.
    27. Serhiy Kozak & Stefan Nagel & Shrihari Santosh, 2018. "Interpreting Factor Models," Journal of Finance, American Finance Association, vol. 73(3), pages 1183-1223, June.
    28. Eugene F. Fama & Kenneth R. French, 2016. "Dissecting Anomalies with a Five-Factor Model," The Review of Financial Studies, Society for Financial Studies, vol. 29(1), pages 69-103.
    29. Fama, Eugene F & Bliss, Robert R, 1987. "The Information in Long-Maturity Forward Rates," American Economic Review, American Economic Association, vol. 77(4), pages 680-692, September.
    30. Robert Novy-Marx & Mihail Velikov, 2014. "A Taxonomy of Anomalies and their Trading Costs," NBER Working Papers 20721, National Bureau of Economic Research, Inc.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Chernov, Mikhail & Creal, Drew & Hördahl, Peter, 2023. "Sovereign credit and exchange rate risks: Evidence from Asia-Pacific local currency bonds," Journal of International Economics, Elsevier, vol. 140(C).
    2. Lars A. Lochstoer & Paul C. Tetlock, 2020. "What Drives Anomaly Returns?," Journal of Finance, American Finance Association, vol. 75(3), pages 1417-1455, June.
    3. Oleg Rytchkov & Xun Zhong, 2020. "Information Aggregation and P-Hacking," Management Science, INFORMS, vol. 66(4), pages 1605-1626, April.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Valentin Haddad & Serhiy Kozak & Shrihari Santosh & Stijn Van Nieuwerburgh, 2020. "Factor Timing," The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 1980-2018.
    2. Yung, Julieta, 2021. "Can interest rate factors explain exchange rate fluctuations?," Journal of Empirical Finance, Elsevier, vol. 61(C), pages 34-56.
    3. Bekaert, Geert & Engstrom, Eric & Ermolov, Andrey, 2021. "Macro risks and the term structure of interest rates," Journal of Financial Economics, Elsevier, vol. 141(2), pages 479-504.
    4. Doshi, Hitesh & Jacobs, Kris & Liu, Rui, 2018. "Macroeconomic determinants of the term structure: Long-run and short-run dynamics," Journal of Empirical Finance, Elsevier, vol. 48(C), pages 99-122.
    5. Stijn Claessens & M Ayhan Kose, 2018. "Frontiers of macrofinancial linkages," BIS Papers, Bank for International Settlements, number 95.
    6. Liu, Yan & Wu, Jing Cynthia, 2021. "Reconstructing the yield curve," Journal of Financial Economics, Elsevier, vol. 142(3), pages 1395-1425.
    7. Stijn Claessens & M Ayhan Kose, 2017. "Asset prices and macroeconomic outcomes: a survey," BIS Working Papers 676, Bank for International Settlements.
    8. Zhang, Han & Fan, Xiaoyun & Guo, Bin & Zhang, Wei, 2019. "Reexamining time-varying bond risk premia in the post-financial crisis era," Journal of Economic Dynamics and Control, Elsevier, vol. 109(C).
    9. Michael D. Bauer & Glenn D. Rudebusch, 2020. "Interest Rates under Falling Stars," American Economic Review, American Economic Association, vol. 110(5), pages 1316-1354, May.
    10. Qi Liu & Libin Tao & Weixing Wu & Jianfeng Yu, 2017. "Short- and Long-Run Business Conditions and Expected Returns," Management Science, INFORMS, vol. 63(12), pages 4137-4157, December.
    11. Zhang, Han & Guo, Bin & Liu, Lanbiao, 2022. "The time-varying bond risk premia in China," Journal of Empirical Finance, Elsevier, vol. 65(C), pages 51-76.
    12. Gregory Bauer & Antonio Diez de los Rios, 2012. "An International Dynamic Term Structure Model with Economic Restrictions and Unspanned Risks," Staff Working Papers 12-5, Bank of Canada.
    13. Feng Zhao & Guofu Zhou & Xiaoneng Zhu, 2021. "Unspanned Global Macro Risks in Bond Returns," Management Science, INFORMS, vol. 67(12), pages 7825-7843, December.
    14. Koijen, Ralph S.J. & Lustig, Hanno & Van Nieuwerburgh, Stijn, 2017. "The cross-section and time series of stock and bond returns," Journal of Monetary Economics, Elsevier, vol. 88(C), pages 50-69.
    15. Richard K. Crump & Stefano Eusepi & Emanuel Moench, 2016. "The term structure of expectations and bond yields," Staff Reports 775, Federal Reserve Bank of New York.
    16. Antonio Gargano & Davide Pettenuzzo & Allan Timmermann, 2019. "Bond Return Predictability: Economic Value and Links to the Macroeconomy," Management Science, INFORMS, vol. 65(2), pages 508-540, February.
    17. Mykola Babiak & Jozef Barunik, 2020. "Deep Learning, Predictability, and Optimal Portfolio Returns," CERGE-EI Working Papers wp677, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
    18. Michael D. Bauer & James D. Hamilton, 2018. "Robust Bond Risk Premia," The Review of Financial Studies, Society for Financial Studies, vol. 31(2), pages 399-448.
    19. John H. Cochrane, 2011. "Discount Rates," NBER Working Papers 16972, National Bureau of Economic Research, Inc.
    20. Wang, Yudong & Pan, Zhiyuan & Liu, Li & Wu, Chongfeng, 2019. "Oil price increases and the predictability of equity premium," Journal of Banking & Finance, Elsevier, vol. 102(C), pages 43-58.

    More about this item

    JEL classification:

    • F31 - International Economics - - International Finance - - - Foreign Exchange
    • F65 - International Economics - - Economic Impacts of Globalization - - - Finance
    • G0 - Financial Economics - - General
    • G1 - Financial Economics - - General Financial Markets
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nbr:nberwo:23886. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/nberrus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.