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Active investment strategies in the Spanish futures market: a solution to avoid data snooping bias

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  • A. Olasolo
  • M. A. Pérez
  • V. Ruiz

Abstract

This article provides a solution to avoid the data snooping problem in the implementation of active investment strategies in the Spanish financial futures markets. The work sets out to evaluate the results obtained with optimum active investment strategies, grounded in the use of exponential moving averages, and to determine their consistency in periods subsequent to those for which they were obtained. To this end, prices were used with data of different time frequencies, different periods for calculating moving averages and different time ranges. In global terms, the differences between the yield of these active strategies and those of a passive nature are statistically significant. The weak efficient market hypothesis is, therefore, rejected for trading in futures on the IBEX-35 stock market index.

Suggested Citation

  • A. Olasolo & M. A. Pérez & V. Ruiz, 2016. "Active investment strategies in the Spanish futures market: a solution to avoid data snooping bias," Applied Economics Letters, Taylor & Francis Journals, vol. 23(9), pages 609-613, June.
    • Handle: RePEc:taf:apeclt:v:23:y:2016:i:9:p:609-613
    DOI: 10.1080/13504851.2015.1093075
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    References listed on IDEAS

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    1. Sullivan, Ryan & Timmermann, Allan & White, Halbert, 2003. "Forecast evaluation with shared data sets," International Journal of Forecasting, Elsevier, vol. 19(2), pages 217-227.
    2. Mark J Ready, 2002. "Profits from Technical Trading Rules," Financial Management, Financial Management Association, vol. 31(3), Fall.
    3. Marshall, Ben R. & Young, Martin R. & Rose, Lawrence C., 2006. "Candlestick technical trading strategies: Can they create value for investors?," Journal of Banking & Finance, Elsevier, vol. 30(8), pages 2303-2323, August.
    4. Halbert White, 2000. "A Reality Check for Data Snooping," Econometrica, Econometric Society, vol. 68(5), pages 1097-1126, September.
    5. Brock, William & Lakonishok, Josef & LeBaron, Blake, 1992. "Simple Technical Trading Rules and the Stochastic Properties of Stock Returns," Journal of Finance, American Finance Association, vol. 47(5), pages 1731-1764, December.
    6. 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 431-467.
    7. Po-Hsuan Hsu & Chung-Ming Kuan, 2005. "Reexamining the Profitability of Technical Analysis with Data Snooping Checks," Journal of Financial Econometrics, Oxford University Press, vol. 3(4), pages 606-628.
    8. Chen, Cheng-Wei & Huang, Chin-Sheng & Lai, Hung-Wei, 2009. "The impact of data snooping on the testing of technical analysis: An empirical study of Asian stock markets," Journal of Asian Economics, Elsevier, vol. 20(5), pages 580-591, September.
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