Measuring and evaluating financial risk exposure for energy companies

Abstract

Over the last decades, the energy industry has been exposed to significant changes within the world marked. The intention of this thesis has therefore been to measure and evaluate financial risk exposure for energy companies. Financial risk has been the primary subject of this thesis. This subject has further been divided into one prediction and two hypotheses encompassing the theoretical framework of this thesis; recession versus growth, financial risk and investments. The prediction and hypotheses have been evaluated by applying both quantitative and qualitative analysis. This thesis studies financial risk exposure and its influence on 16 major oil and gas companies during a 20 year time period, from February 1989 to February 2009. World market changes may provoke several changes in financial risk exposure facing energy companies. These changes could provoke higher volatility, meaning significant changes in a company’s stock price and could further revise future investment strategies. These considerations form the basis for the first prediction in this thesis; “economic recession and resulting changes in market risk factors increases the stock price volatility and changes the investment behavior for energy companies.” The quantitative results points toward the highest volatility measurements in periods of recession. This corresponds to the qualitative analysis, as the overall responses from the depth interviews presumed the highest volatility measures in periods of recession. Based on the quantitative and qualitative results the prediction stating that “economic recession and resulting changes in the market risk factors increases the stock price volatility for energy companies” is accepted. The investment behavior was further analyzed by evaluating the reserves replacement rate and finding and development costs of each of the company represented. Neither the quantitative nor the qualitative analysis points towards periods of recession causing changes in the investment behavior for the companies analyzed, so the predicting stating changes in a company’s investment behavior as the results of periods of recession is rejected. Periods of recession and growth caused by world market changes could have dissimilar influence on an energy company’s stock price, depending on size and value of company. The subsequent hypothesis analyzed in this thesis therefore claimed that “the stock price is more influenced by market risk in periods of recession than in periods of growth”. The quantitative results show a higher relation between the market risk factors in periods of recession compared to periods of growth. From the results of the t-test, oil and gas frequently proves a significant correlation at given significance levels. This is similar to the qualitative analysis, as the financial personnel ranked oil and gas price to provide the highest influence on a company’s stock price when asked to compare this to other financial risk factors. Based on these results the hypothesis “the stock price is more influenced by market risk factors in periods of recession than in periods of growth” was accepted. The financial risk exposure facing the energy companies can be viewed by evaluating the stock price fluctuation, and further to apply models to calculate the historical expected stock return based on included risk factors. These models are used to price risk and are therefore applied to evaluate which of the presented models provides the most accurate measure of historical expected stock return compared to the actual historic stock return. These models represent the final hypothesis stated in this thesis, “increased number of financial risk factors included in a model for pricing risk, gives a more accurate predicted historical stock return”. The results show that Brent oil price, market cap and book-to-market ratio each have a significant impact on the historic stock price return for the energy industry. Considering the calculation of expected historical stock return based on the included systematic and unsystematic risk factors, the APT multifactor model provides the most accurate model to explain historical expected stock return. The APT model incorporated the following systematic risk factors: interest rate, market index, exchange rate, oil price and gas price. These could therefore be considered as the most important risk factors for predicting historical stock return. Based on the yearly analysis, increased number of financial risk factors does not necessarily give more accurate predicted stock return. However, if considering the monthly analysis, increased number of financial risk factors did actually give a more accurate predicted stock return.