be stored, hydropower producers rely more on the use of power derivatives in securing future prices. In essence, a power derivative is a contract between a buyer and seller to buy electricity in the future at a given price. The settlement of this contract either involves delivery of physical power, or an exchange of future cash flows. The Nordic power market is one of the most liquid power derivatives markets in the world (Nasdaq, Inc., nd). Most of the financial power trading in the Nordic countries takes place at Nasdaq Commodities, through the Nasdaq Oslo ASA Exchange and Nasdaq Clearing AB. Nasdaq Commodities differs from other exchanges as financial actors without exposure to the underlying asset constitute a minority (Saakvitne and Bjønnes, 2015). Moreover, the trading of power derivatives mainly takes place on the exchange, in contrast to international commodity markets, where trading usually happens outside of the exchange.
On the contrary, there has been a downwards trend in recent years towards less derivatives trading at the exchange, resulting in declining liquidity at Nasdaq Commodities (Finanstilsynet, 2019). A potential future risk is thus an illiquid market where hydropower producers struggle to sell their contracts. The declining liquidity, combined with the expected increase in price volatility and uncertainty of the future market, renders future earnings of hydropower producers with growing uncertainty. There is evidence of widespread risk management practices among Norwegian electricity companies (Sanda et al., 2013). Therefore, with the expected developments in the market, risk management can prove to be increasingly important.
1.2 Purpose and research question
The focus of this thesis is on large-scale hydropower producers in Norway. With uncertain factors as those presented in the previous section, price risk management strategies are important for the producers to secure future earnings. The largest hydropower producers in the market manage market risk by securing prices through financial contracts. In this thesis, the financial contracts in focus will be yearly, quarterly and monthly futures contracts at Nasdaq Commodities, as these are some of the most liquid contracts used to secure future prices. We define the contracts in question as medium-term, as opposed to short-term contracts such as daily and weekly futures, and long-term contracts that span over several years. The contracts use the Nordic system price as reference, thus this price
4 1.2 Purpose and research question
will be a recursive factor in this thesis.
Much of the general understanding of market practices for hydropower producers has been introduced through interviews with hydropower producer BKK and the industry leader for Deloitte Norway’s activities in the power market, on October 17th and November 25th respectively. Through the interviews, common hedging strategies for hydropower producers, involving general considerations and specific financial instruments, as well as general practices for price prediction have been elucidated. Much is considered sensitive information in the power market. The main focus in the interviews has therefore been on a general understanding of the most common market practice, as much market information is neither published, nor intuitive. Thus, the interviews have been an important source of understanding crucial factors. The second interview, on November 25th, also had a particular focus on transmission costs and taxes. In the sections where information from the two interviews is directly used, they will be referred to explicitly. Otherwise, the interviews in general, function as a background for our understanding of general market practice.
When trading futures contracts, traders use available information to determine whether to purchase or sell contracts. The most vital source of information is predictions of the future system price. Many of the algorithms used for predicting system prices are considered trade secrets and sources of competitive advantage (Krishna et al., 2017). Thus, the exact algorithms used for predicting prices cannot be stated, and usually a consolidated evaluation of many predictions and judgements are used. In our thesis, we consider how standard machine learning methods can be applied to predict future system prices. The accuracy of these machine learning models will be evaluated, and their performance will be tested to see whether the predictions can be used in the context of risk management for purchases of futures contracts. Thus, we provide a simple approach to the power price forecasting and investigate whether this approach can generate value that makes it applicable in practice.
This thesis contributes to price risk management indirectly. Our analyses and recommendations are intended for those in charge of trading futures contracts. The aim is to help them make better decisions, i.e. purchase the correct contracts in a profit maximizing view. More specifically, we aim to increase their decision support for trading
1.2 Purpose and research question 5
futures contracts. Thus, the decisions made by the traders are intended to maximize profit. However, when applied by the business as a whole, the purpose of the trading is to manage risk. Hence, risk management serves as a backdrop for our research question, even though the research question in itself is set to maximize profit. The objective is to introduce the use of artificial intelligence in predicting the Nordic system price and investigate how these predictions can be used in a risk management perspective. The research question that will be discussed is:
How can predictions of the Nordic system price using machine learning methods enhance decision support for hydropower producers when trading medium-term power derivatives?
The purpose of this thesis is to evaluate whether applying machine learning can increase decision support for hydropower producers when purchasing financial power contracts for price risk management purposes. To be applied in the risk management function, both the expected earnings and risks of using machine learning are discussed. More specifically, the performance of standard machine learning algorithms in predicting the system price are tested and evaluated. The performance is measured both in terms of prediction accuracy and in their guidance for developing hedging strategies. For testing the performance of the methods, 2018 is used as the year of reference. 2018 was a year with extraordinary power prices and developments, and thus might not be the best year of reference. The reason why the models are still tested on this year is because the data set begins in 2013.
Several machine learning methods require large amounts of data to give precise predictions.
Hence, 2018 is used as it is the last year in the data set with complete data.
The results of this thesis are mainly meant to be used by hydropower producers in their price risk management strategies. Overall, standard machine learning methods are applied using the programming language and system R (R Core Team, 2013). The specific packages used in R are included in Appendix A5. Data is retrieved through open sources and Datastream. In our approach we attempt to replicate how the decision-making processes of traders will be in real life. When applied to a real-life setting however, hydropower producers can use their own data set in the model estimation. With a few modifications, our results can also be used by and benefit other power producers, power suppliers and large-scale end users.