Open AccessUsing Machine Learning to Profit on the Risk Premium of the Nordic Electricity Futures
Author(s) -
Hélder Sebastião,
Pedro Godinho,
Sjur Westgaard
Publication year - 2020
Publication title -
scientific annals of economics and business
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.204
H-Index - 6
eISSN - 2501-3165
pISSN - 2501-1960
DOI - 10.47743/saeb-2020-0024
Subject(s) - futures contract , speculation , profitability index , profit (economics) , trading strategy , market liquidity , risk premium , electricity market , pairs trade , economics , electricity , algorithmic trading , financial economics , business , industrial organization , econometrics , microeconomics , finance , alternative trading system , engineering , electrical engineering
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.