The thesis is divided into two parts concerning different topics. The first is solving a multi-period portfolio decision problem, and the second, more theoretical, is a numerical comparison of uncertainty measures within evidence theory. Nowadays, portfolio problems are very common and present in several fields of study. The problem is inspired by a real-world infrastructure manage- ment case in the energy distribution sector. The problem consists of the optimal selection of a set of activities and their scheduling over time. In scheduling, various constraints and limits that the company has to meet must be considered, and the selection must be based on prioritizing the activities with a higher priority value. The problem is addressed by Port- folio Decision Analysis: the priority value of activities is assigned using the Multi-Attribute Value Theory method, which is then integrated with a multi-period optimization problem with activities durations and con- straints. Compared to other problems in the literature, in this case, the ac- tivities have different durations that must be taken into account for proper planning. The planning obtained is suitable for the user’s requirements both in terms of speed in providing results and in terms of simplicity and comprehensibility. In recent years, measures of uncertainty or entropy within evidence theory have again become a topic of interest in the literature. However, this has led to an increase in the already numerous measures of total uncertainty, that is, one that considers both conflict and nonspecificity measures. The research aims to find a unique measure, but none of those proposed so far can meet the required properties. The measures are often complex, and especially in the field of application, it is difficult to understand which is the best one to choose and to understand the numerical results obtained. Therefore, a numerical approach that compares a wide range of measures in pairs is proposed alongside comparisons based on mathematical proper- ties. Rank correlation, hierarchical clustering, and eigenvector centrality are used for comparison. The results obtained are discussed and com- mented on to gain a broader understanding of the behavior of the measures and the similarities and non-similarities between them.

Traditional statistical analysis starts with computing the basic statisti- cal characteristics such as the population mean E, population variance V , cova- riance and correlation. In computing these characteristics, it is usually assumed that the corresponding data values are known exactly. In real life there are many situations in which a more complete information can be achieved by describing a set of statistical units in terms of interval data. For example, daily tempera- tures registered as minimum and maximum values offer a more realistic view on the weather conditions variations with respect to the simple average values. In environmental analysis, we observe a pollution level x(t) in a lake at different mo- ments of time t, and we would like to estimate standard statistical characteristics such as mean, variance and correlation with other measurements. Another exam- ple can be given by financial series. The minimum and the maximum transaction prices recorded daily for a set of stocks represent a more relevant information for experts in order to evaluate the stocks tendency and volatility in the same day. We must therefore modify the existing statistical algorithms to process such interval data. In this work we will analyze algorithms and their modifications for computing various statistics under...

Producing a model explaining the trading volume can be attractive for companies who’s main revenue resides on it. Previous studies have shown that factors such as stock returns, volatility and uncertainty affects the trading volume. The purpose of this work is to clarify the consensus that prevails and determine the factors that impact Avanza’s customers trading volume. Factors such as daily stock returns and economic, political and financial uncertainty are analyzed through a multiple linear regression analysis with a daily time period between 2000-2019. The work is thus designed within the framework of mathematical statistics and industrial economics. To be able to draw a conclusion, further investigation is required in the form of a time series analysis in combination with a deeper understanding of the applied area and the mathematical methods that have been used. / Att ta fram en modell som förklarar handelsvolymen kan vara eftertraktat hos företag vars huvudintäkter beror av den. Tidigare forskning visar att faktorer som prisförändringar på aktiemarknaden, volatilitet och osäkerhet påverkar handelsvolymen. Syftet med arbetet är att klargöra den konsensus som råder och fastställa de faktorer som har störst påverkan gällande handelsvolymen för Avanza’s kunders. Faktorer som dagliga förändringar inom börsmarknaden och ekonomisk, politisk och finansiell osäkerhet har genom en multipel linjär regressionsanalys analyserats med en daglig tidsperiod mellan 2000-2019. Arbetet är således utformat inom ramen för matematisk statistik och industriell ekonomi. För att kunna dra en slutsats krävs vidare undersökning i form av en tidsserieanalys och en djupare förståelse av det tillämpade området och metoderna som har an- vänds.