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Is Momentum Due to Data-Snooping?

  • Ericsson, Johan

    ()

    (Dept. of Economic Statistics, Stockholm School of Economics)

  • González, Andrés

    ()

    (Dept. of Economic Statistics, Stockholm School of Economics)

This paper explores the profitability of portfolio-based momentum strategies. The data consists of all NYSE, AMEX, and NASDAQ stocks on the CRSP database. The analysis considers the period July 1963 to December 2002 and the tests are performed on portfolios formed on industry, size and book-to-market. The departure from earlier studies lies in the way we test for profitability. To avoid the serious problem of data-snooping we apply the procedure provided by White (2000). Overall, we find strong evidence of a momentum effect where an investor takes a long position on the winner portfolio and a short position on the loser portfolio. Hence, we reject the hypothesis of weak market efficiency. Splitting the sample in two parts, 1963:07 to 1981:12 and 1982:01 to 2002:12 we found that the best momentum strategy was profitable during the first period and not during the second. The overall significance is thus driven by events in the earlier part of the sample and it appears that the market has become more efficient.

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Paper provided by Stockholm School of Economics in its series SSE/EFI Working Paper Series in Economics and Finance with number 536.

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Length: 13 pages
Date of creation: 25 Sep 2003
Date of revision:
Handle: RePEc:hhs:hastef:0536
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  1. Tobias J. Moskowitz & Mark Grinblatt, . "Do Industries Explain Momentum?," CRSP working papers 352, Center for Research in Security Prices, Graduate School of Business, University of Chicago.
  2. Halbert White, 2000. "A Reality Check for Data Snooping," Econometrica, Econometric Society, vol. 68(5), pages 1097-1126, September.
  3. Hansen, Peter Reinhard, 2005. "A Test for Superior Predictive Ability," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 365-380, October.
  4. K. Geert Rouwenhorst, 1998. "International Momentum Strategies," Journal of Finance, American Finance Association, vol. 53(1), pages 267-284, 02.
  5. Chan, Louis K C & Jegadeesh, Narasimhan & Lakonishok, Josef, 1996. " Momentum Strategies," Journal of Finance, American Finance Association, vol. 51(5), pages 1681-1713, December.
  6. Fama, Eugene F & French, Kenneth R, 1996. " Multifactor Explanations of Asset Pricing Anomalies," Journal of Finance, American Finance Association, vol. 51(1), pages 55-84, March.
  7. Narasimhan Jegadeesh, 2001. "Profitability of Momentum Strategies: An Evaluation of Alternative Explanations," Journal of Finance, American Finance Association, vol. 56(2), pages 699-720, 04.
  8. Conrad, Jennifer & Kaul, Gautam, 1998. "An Anatomy of Trading Strategies," Review of Financial Studies, Society for Financial Studies, vol. 11(3), pages 489-519.
  9. Jonathan Lewellen, 2002. "Momentum and Autocorrelation in Stock Returns," Review of Financial Studies, Society for Financial Studies, vol. 15(2), pages 533-564, March.
  10. Savin, N.E., 1984. "Multiple hypothesis testing," Handbook of Econometrics, in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 2, chapter 14, pages 827-879 Elsevier.
  11. Lo, Andrew W & MacKinlay, A Craig, 1990. "Data-Snooping Biases in Tests of Financial Asset Pricing Models," Review of Financial Studies, Society for Financial Studies, vol. 3(3), pages 431-67.
  12. Jegadeesh, Narasimhan & Titman, Sheridan, 1993. " Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency," Journal of Finance, American Finance Association, vol. 48(1), pages 65-91, March.
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