Browsing NHH Brage by Author "Otneim, Håkon"

Assessing the Impact of the Iberian Exception on Day-Ahead Prices in Spain : A Difference in Difference and Quantile Regression Approach

Farkas, Julia; Wergeland, Olve Bjørke (Master thesis, 2023)

The energy crisis of 2021 and 2022 has had severe consequences for Europe. The skyhigh energy prices have reduced economic growth, created inflation, and increased GHG emissions. In an effort to tackle the record high ...

Automatic machine learning applied to time series forecasting for novice users in small to medium-sized businesses : a review of how companies accumulate and use data along with an interface for data preparation as well as easy and powerful prediction analysis capable of providing valuable insight

Gran, Anders Stykket (Master thesis, 2019)

Data analytics is gradually becoming one of the most essential tools and sources of competitive advantage for modern companies. There is a multitude of analytical services and solutions on the market, and the effect of ...

Blokkjedeteknologi og Gjensidige Forsikring ASA : en kvalitativ casestudie av blokkjedeteknologi og dens betydning for Gjensidige Forsikring ASA sin forretningsmodell

Frantzen, Andrea Pedersen; Ranberg, Erik Sebastian (Master thesis, 2018)

Blokkjedeteknologi og kryptovaluta har preget nyhetsbildet de siste årene, og slik det fremgår av media representerer teknologien et paradigmeskifte innen næringslivet. Teknologien utgjør grunnlaget for desentraliserte ...

ESG-uenighet og aksjeavkastning: En empirisk studie av sammenhengen mellom uenighet i ESC-rating og aksjeavkastning blant skandinaviske selskaper

Baastad, Hennie; Foss, Emilie Therese (Master thesis, 2022)

Bærekraftige investeringer, på engelsk omtalt som ESC (environmental, social, og governance) investing, har det siste tiåret blitt svært populært og en trend blant investorene. Mange spør seg om det i det hele tatt ...

Explaining Individual Predictions on Financially Distressed Companies Using Shapley Values

Dokset, Henrik Rodahl; Vindenes, Eirik (Master thesis, 2021)

Prediction results from complex machine learning models can be challenging to interpret. Understanding these models is essential when trusting results in decision-making. In this master thesis, we will utilize Shapley ...

Hidden semi-Markov models for rainfall-related insurance claims

Shi, Yue; Punzo, Antonio; Otneim, Håkon; Maruotti, Antonello (Discussion paper;17/23, Working paper, 2023-11-06)

We analyze the temporal structure of a novel insurance dataset about home insurance claims related to rainfall-induced damage in Norway, and employ a hidden semi-Markov model to capture the non-Gaussian nature and temporal ...

Level Up Your Sneaker Game : Applying machine learning techniques to support data-driven investment decisions in the sneaker resale market

Kenny, Sandrina; Cetin, Asli (Master thesis, 2021)

Sneaker resale has become a worldwide phenomenon. The resale market is growing, expected to potentially reach up to $30 bn by 2030. More and more people want to take part in making fortunes out of shifting high valued ...

Local Lead–Lag Relationships and Nonlinear Granger Causality: An Empirical Analysis

Otneim, Håkon; Berentsen, Geir Drage; Tjøstheim, Dag Bjarne (Peer reviewed; Journal article, 2022)

The Granger causality test is essential for detecting lead–lag relationships between time series. Traditionally, one uses a linear version of the test, essentially based on a linear time series regression, itself being ...

Machine learning in the aviation industry and the potential of using air traffic as a real-time indicator of GDP : a study of how useful machine learning is to predict Norwegian air traffic and investigating the causal relationship between air traffic and GDP

Lohne, Andrea Madeleine; Skrbo, Nejira (Master thesis, 2020)

Travel by air is an essential part of both the Norwegian society and its infrastructure, where Norway has one of the highest number of flights per capita in Europe. Nonetheless, the aviation industry is characterized by ...

Non-parametric estimation of conditional densities: A new method

Otneim, Håkon; Tjøstheim, Dag (Discussion paper;22/16, Working paper, 2016-12-07)

Let X = (X1,...,Xp) be a stochastic vector having joint density function fX(x) with partitions X1 = (X1,...,Xk) and X2 = (Xk+1,...,Xp). A new method for estimating the conditional density function of X1 given X2 is presented. ...

Nowcasting GDP Growth on a Weekly Basis : Leveraging Comprehensive News Article Information and Macroeconomic Indicators

Kvinnsland, Jon; Foss, Nicholas (Master thesis, 2023)

Timely economic indicators are crucial for effective macroeconomic decision-making. In particular, this applies during crises when economic shifts can be large and costly. In this thesis, we produce weekly Gross Domestic ...

Portfolio allocation under asymmetric dependence in asset returns using local Gaussian correlations

Sleire, Anders Daasvand; Støve, Bård; Otneim, Håkon; Berentsen, Geir Drage; Tjøstheim, Dag Bjarne; Haugen, Sverre Hauso (Peer reviewed; Journal article, 2021)

It is well known that there are asymmetric dependence structures between financial returns. This paper describes a portfolio selection method rooted in the classical mean–variance framework that incorporates such asymmetric ...

Predicting financial distress in Norway : using logistic regression and random forest models

Zhang, Guang Na; Ye, Fan (Master thesis, 2019)

Financial distress can be a highly costly and disruptive event, both on the level of the firm as well as for the society. Models to predict financial distress for this reason have been beneficial. In this thesis, we aim ...

Predicting Private Equity Fund Performance with Machine Learning

Kruglikov, Nikita; Forthun, Andreas (Master thesis, 2022)

This paper has the objective of applying machine learning models to predict the performance of private equity funds, to allow for more effective fund selection for investors in the private markets. Prior research has ...

Predikerte boligpriser ved introduksjonen av Bybanen til Åsane : hvordan vil Bybanen påvirke boligprisene langs traseen?

Døskeland, Kristoffer; Francois, Lucas (Master thesis, 2020)

Formålet med denne masterutredningen er å predikere hvordan boligprisutviklingen mellom Byparken og Åsane ville vært for perioden 2010-2019, i et tenkt scenario hvor Bybanen til Åsane hadde blitt ferdigstilt i 2010. Det ...

Shapley values in the context of GDPR Can Shapley Values be used as a means of interpreting black-box machine learning models while also complying with the General Data Protection Regulation?

Juelsen, Eirik; Thoresen, Marius Andre (Master thesis, 2021)

The General Data Protection Regulation implemented in 2018 by the European Union imposes strict requirements when handling personal data regarding European citizens. This is especially true when processing said data in ...