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Using Machine Learning to Profit on the Risk Premium of the Nordic Electricity Futures

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  • Helder Sebastião
  • Pedro Godinho
  • Sjur Westgaard

Abstract

This study investigates the use of several trading strategies, based on Machine Learning methods, to profit on the risk premium of the Nordic electricity base-load week futures. The information set is only composed by financial data from January 02, 2006 to November 15, 2017. The results point out that the Support Vector Machine is the best method, but, most importantly, they highlight that all individual models are valuable, in the sense that their combination provides a robust trading procedure, generating an average profit of at least 26% per year, after considering trading costs and liquidity constraints. The results are robust to the different data partitions, and there is no evidence that the profitability of the trading strategies has decreased in recent years. We claim that this market allows for profitable speculation, namely by using combinations of non-linear signal extraction techniques. JEL Codes - G13; G14; Q40

Suggested Citation

  • Helder Sebastião & Pedro Godinho & Sjur Westgaard, 2020. "Using Machine Learning to Profit on the Risk Premium of the Nordic Electricity Futures," Scientific Annals of Economics and Business (continues Analele Stiintifice), Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, vol. 67(4), pages 1-17, December.
    • Handle: RePEc:aic:saebjn:v:67:y:2020:i:4:p:1-17:n:178
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    References listed on IDEAS

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    1. Huisman, Ronald & Kilic, Mehtap, 2012. "Electricity Futures Prices: Indirect Storability, Expectations, and Risk Premiums," Energy Economics, Elsevier, vol. 34(4), pages 892-898.
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    4. Weron, Rafał, 2014. "Electricity price forecasting: A review of the state-of-the-art with a look into the future," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1030-1081.
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    More about this item

    Keywords

    Nord Pool; electricity futures; risk premium; machine learning; trading;
    All these keywords.

    JEL classification:

    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General

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