The Determinants of Financial Leverage of Large Publicly Listed Companies - Evidence from Nordic Telecom Sector

This thesis examines the determinants of financial leverage ratio of large publicly listed companies within Nordic Telecom sector. The study is done as a case study and it covers 5 case companies headquartered in Nordic countries during period of 2002 - 2014 and by using restated values of quarterly observations from each case company’s interim reports. The chosen hypotheses are tested with multiple linear regressions firm by firm. The Findings of the study showed that uniqueness of Telecom sector and the region of our sample could not provide us unequivocal determinants of leverage ratio within the sector. However, e.g. Pecking order theory’s statement of Liquidity was widely confirmed by 3 out of 5 case companies which is worth to be taken into account in the big picture. The findings also showed that theories and earlier empirical evidence are confirmed by our case companies individually and non-systematically. Though Telecom sector is considered as quite unique industry and we did not discover absolute common relationships that would have held through all the Nordic case companies, we got unique and valuable evidence to conduct the research of this sector in future.

A Bayesian Approach for Forecasting Heat Load in a District Heating System

The growing population in cities increases the energy demand and affects the environment by increasing carbon emissions. Information and communications technology solutions which enable energy optimization are needed to address this growing energy demand in cities and to reduce carbon emissions. District heating systems optimize the energy production by reusing waste energy with combined heat and power plants. Forecasting the heat load demand in residential buildings assists in optimizing energy production and consumption in a district heating system. However, the presence of a large number of factors such as weather forecast, district heating operational parameters and user behavioural parameters, make heat load forecasting a challenging task. This thesis proposes a probabilistic machine learning model using a Naive Bayes classifier, to forecast the hourly heat load demand for three residential buildings in the city of Skellefteå, Sweden over a period of winter and spring seasons. The district heating data collected from the sensors equipped at the residential buildings in Skellefteå, is utilized to build the Bayesian network to forecast the heat load demand for horizons of 1, 2, 3, 6 and 24 hours. The proposed model is validated by using four cases to study the influence of various parameters on the heat load forecast by carrying out trace driven analysis in Weka and GeNIe. Results show that current heat load consumption and outdoor temperature forecast are the two parameters with most influence on the heat load forecast. The proposed model achieves average accuracies of 81.23 % and 76.74 % for a forecast horizon of 1 hour in the three buildings for winter and spring seasons respectively. The model also achieves an average accuracy of 77.97 % for three buildings across both seasons for the forecast horizon of 1 hour by utilizing only 10 % of the training data. The results indicate that even a simple model like Naive Bayes classifier can forecast the heat load demand by utilizing less training data.

The Impact of the 2007-2009 Financial Crisis on the Relationship between Central Bank Rates and the Corresponding Interbank Interest Rates in 2002-2014

The role of central banks throughout the global financial system has become even more important during and after the events of the financial crisis. In order to stabilize the market conditions and provide solid ground for future development, the central banks use discount rate as their primary monetary policy tool in many developed and emerging economies. The purpose of this thesis is to examine how the relationship between central bank rates and corresponding interbank rates has developed before, during and after the crisis period of 2007-2009 in five developed countries and five emerging market countries. The results indicate that during the before-crisis period the interest rate markets reacted diversely but the joint recovery attempts of global economies seem to have stabilized the reactions during and especially after the crisis. The crisis also seems to have highlighted the characteristics of each country’s survival strategy as the role of other policy instruments arose.

A Project Portfolio Tool for Forecasting Small Projects Profitability

The case company in this study is a large industrial engineering company whose business is largely based on delivering a wide-range of engineering projects. The aim of this study is to create and develop a fairly simple Excel-based tool for the sales department. The tool’s main function is to estimate and visualize the profitability of various small projects. The study also aims to find out other possible and more long-term solutions for tackling the problem in the future. The study is highly constructive and descriptive as it focuses on the development task and in the creation of a new operating model. The developed tool focuses on estimating the profitability of the small orders of the selected project portfolio currently on the bidding-phase (prospects) and will help the case company in the monthly reporting of sales figures. The tool will analyse the profitability of a certain project by calculating its fixed and variable costs, then further the gross margin and operating profit. The bidding phase of small project is a phase that has not been covered fully by the existing tools within the case company. The project portfolio tool can be taken into use immediately within the case company and it will provide fairly accurate estimate of the profitability figures of the recently sold small projects.