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Synthetic History for Exchange Traded Funds

  • Aistis Raudys
  • Lukas Sirvydis
  • Karol Lisovskij
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 117)

Abstract

To make money in trading one ought to forecast the future price, but to do so accurately one must verify the predictions using the past data. A short trading history can present a problem. We showed both theoretically and experimentally that the history of some financial assets can be reconstructed quite accurately. We forecasted the past price movements of exchange traded funds (ETFs). The problem in practice is very acute as there are a number of very liquid ETFs that can be traded with minimum slippage but their available history is too short. In such situations systematic traders cannot test their trading models as the history length is insufficient. To forecast historical ETF prices we used stocks with a longer history available. In some cases we created multiple model instances with a variable number of stocks. As soon as the stock history became unavailable we selected a different model. We compared this and eight other methods using a set of US ETFs ranging from S&P 500 to uranium. The experimental study showed the expectation maximisation with covariance matrix normalization to be the best method for this task.

Keywords

synthetic history artificial history time series regression expectation maximisation imputation missing data 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Aistis Raudys
    • 1
  • Lukas Sirvydis
    • 1
  • Karol Lisovskij
    • 1
  1. 1.Faculty of Mathematics and Informatics, Department of InformaticsVilnius UniversityVilniusLithuania

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