Using Machine Learning to Profit on the Risk Premium of the Nordic Electricity Futures
Keywords:Nord Pool, electricity futures, risk premium, machine learning, trading
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
Aoude, S., De Mello, L., and Truck, S., 2016. Electricity futures markets in Australia - An analysis of risk premiums during the delivery period. Paper presented at the Meeting Asia's Energy Challenges, 5th IAEE Asian Conference. International Association for Energy Economics. https://www.mq.edu.au/__data/assets/pdf_file/0007/583288/working_paper_16-03.pdf.
Botterud, A., Bhattacharyya, A. D., and Ilic, M. D., 2002. Futures and spot prices – An analysis of the Scandinavian electricity market. Paper presented at the 34th Annual North American Power Symposium (NAPS 2002).
Botterud, A., Kristiansen, T., and Ilic, M. D., 2010. The relationship between spot and futures prices in the Nord Pool electricity market. Energy Economics, 32(5), 967-978. http://dx.doi.org/10.1016/j.eneco.2009.11.009
Breiman, L., 2001. Random Forests. Machine Learning, 45(1), 5-32. http://dx.doi.org/10.1023/A:1010933404324
Breiman, L., Friedman, J., Stone, C. J., and Olshen, R. A., 1984. Classification and Regression Trees: CRC press.
Cartea, A., and Villaplana, P., 2008. Spot price modelling and the valuation of electricity forward contracts: The role of demand and capacity. Journal of Banking & Finance, 32(12), 2502-2519. http://dx.doi.org/10.1016/j.jbankfin.2008.04.006
Ferreira, M., and Sebastião, H., 2018. The Iberian electricity market: Analysis of the risk premium in an illiquid market. Journal of Energy Markets, 11(2), 1-22. http://dx.doi.org/10.21314/JEM.2018.176
Fleten, S. E., Hagen, L. A., Nygard, M. T., Smith-Sivertsen, R., and Sollie, J. M., 2015. The overnight risk premium in electricity forward contracts. Energy Economics, 49, 293-300. http://dx.doi.org/10.1016/j.eneco.2014.12.022
Gao, C., Bompard, E., Napoli, R., and Cheng, H., 2007. Price forecast in the competitive electricity market by Support Vector Machine. Physica A, 382(1), 98-113. http://dx.doi.org/10.1016/j.physa.2007.03.050
González, C., Mira-McWilliams, J., and Juarez, I., 2015. Important variable assessment and electricity price forecasting based on regression tree models: Classification and regression trees, bagging and random forests. IET Generation, Transmission & Distribution, 9(11), 1120-1128. http://dx.doi.org/10.1049/iet-gtd.2014.0655
Haugom, E., Hoff, G. A., Mortensen, M., Molnar, P., and Westgaard, S., 2014. The forecasting power of medium-term futures contracts. Journal of Energy Markets, 7(4), 47-69. http://dx.doi.org/10.21314/jem.2014.108
Haugom, E., Hoff, G. A., Mortensen, M., Molnar, P., and Westgaard, S., 2018. The forward premium in Nord Pool power market. Emerging Markets Finance & Trade, 54(8), 1793-1807. http://dx.doi.org/10.1080/1540496X.2018.1441021
Huisman, R., and Kilic, M., 2012. Electricity futures prices: Indirect storability, expectations, and risk premiums. Energy Economics, 34(4), 892-898. http://dx.doi.org/10.1016/j.eneco.2012.04.008
Liaw, A., and Wiener, M., 2002. Classification and regression by randomForest. R News, 2(3), 18-22.
Lubnau, T., and Todorova, N., 2015. Trading on mean-reversion in energy futures markets. Energy Economics, 51, 312-319. http://dx.doi.org/10.1016/j.eneco.2015.06.018
Lucia, J. J., and Torró, H., 2011. On the risk premium in Nordic electricity futures prices. International Review of Economics & Finance, 20(4), 750-763. http://dx.doi.org/10.1016/j.iref.2011.02.005
Ludwig, N., Feuerriegel, S., and Neumann, D., 2015. Putting big data analytics to work: Feature selection for forecasting electricity prices using the LASSO and Random Forests. Journal of Decision Systems, 24(1), 19-36. http://dx.doi.org/10.1080/12460125.2015.994290
Mei, J., He, D., Harley, R., Habetler, T., and Qu, G., 2014. A random forest method for real-time price forecasting in new york electricity market. Paper presented at the PES General Meeting Conference & Exposition, 2014 IEEE. http://dx.doi.org/10.1109/PESGM.2014.6939932
Meyer, D., Dimitriadou, E., Hornik, K., Weingessel, A., and Leisch, F., 2017. e1071: Misc Functions of the Department of Statistics, Probability Theory Group (Formerly: E1071), TU Wien. from https://CRAN.R-project.org/package=e1071
Mork, E., 2006. The dynamics of risk premiums in Nord Pool's futures market. Energy Studies Review, 14(1), 170-185. http://dx.doi.org/10.15173/esr.v14i1.485
Paraschiv, F., Fleten, S. E., and Schurle, M., 2015. A spot-forward model for electricity prices with regime shifts. Energy Economics, 47, 142-153. http://dx.doi.org/10.1016/j.eneco.2014.11.003
Politis, D. N., and Romano, J. P., 1994. The stationary bootstrap. Journal of the American Statistical Association, 89(428), 1303-1313. http://dx.doi.org/10.1080/01621459.1994.10476870
Politis, D. N., and White, H., 2004. Automatic block-length selection for the dependent bootstrap. Econometric Reviews, 23(1), 53-70. http://dx.doi.org/10.1081/etc-120028836
Politis, D. N., and White, H., 2009. Correction to "Automatic block-length selection for the dependent bootstrap. Econometric Reviews, 28(4), 372-375. http://dx.doi.org/10.1080/07474930802459016
Sadeghi-Mobarakeh, A., Kohansal, M., Papalexakis, E. E., and Mohsenian-Rad, H., 2017. Data mining based on random forest model to predict the California ISO day-ahead market prices. Paper presented at the Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), 2017 IEEE. http://dx.doi.org/10.1109/ISGT.2017.8086061
Saini, L. M., Aggarwal, S. K., and Kumar, A., 2010. parameter optimisation using genetic algorithm for support vector machine-based price-forecasting model in national electricity market. IET Generation, Transmission & Distribution, 4(1), 36-49. http://dx.doi.org/10.1049/iet-gtd.2008.0584
Shrivastava, N. A., Khosravi, A., and Panigrahi, B. K., 2015. Prediction interval estimation of electricity prices using PSO-tuned support vector machines. IEEE Transactions on Industrial Informatics, 11(2), 322-331. http://dx.doi.org/10.1109/TII.2015.2389625
Smith-Meyer, E., and Gjolberg, O., 2016. The Nordic futures market for power: Finally mature and efficient? Journal of Energy Markets, 9(4), 1-15. http://dx.doi.org/10.21314/jem.2016.151
Steinert, R., and Ziel, F., 2019. Short-to mid-term day-ahead electricity price forecasting using futures. Energy Journal, 40(1), 105-127. http://dx.doi.org/10.5547/01956574.40.1.rste
Tay, F. E., and Cao, L., 2001. Application of support vector machines in financial time series forecasting. Omega, 29(4), 309-317. http://dx.doi.org/10.1016/s0305-0483(01)00026-3
Therneau, T., Atkinson, B., and Ripley, B., 2018. rpart: Recursive Partitioning and Regression Trees. from https://CRAN.R-project.org/package=rpart
Torgo, L., 2016. Data Mining with R: Learning with Case Studies: CRC press. http://dx.doi.org/10.1201/9781315399102
Weron, R., 2014. Electricity price forecasting: A review of the state-of-the-art with a look into the future. International Journal of Forecasting, 30(4), 1030-1081. http://dx.doi.org/10.1016/j.ijforecast.2014.08.008
Weron, R., and Zator, M., 2014. Revisiting the relationship between spot and futures prices in the Nord Pool electricity market. Energy Economics, 44, 178-190. http://dx.doi.org/10.1016/j.eneco.2014.03.007
Zhao, J. H., Dong, Z. Y., Xu, Z., and Wong, K. P., 2008. A Statistical approach for interval forecasting of the electricity price. IEEE Transactions on Power Systems, 23(2), 267-276. http://dx.doi.org/10.1109/TPWRS.2008.919309
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