This thesis aims to examine whether it is possible to forecast which firmswill be shorted. To do this a regression was constructed using a sample of thecompanies currently included in S&P 500. Short interest as percentage offloat was set as the dependent variable with volatility, institutionalownership, past stock returns, growth in net sales and price-to-earnings ratio(P/E) as the independent variables. Our results concluded that all variablesexcept institutional ownership were statistically significant at a 5% level withthree of these being significant even at a 1% level. Based on these results, webelieve that it to a certain degree is possible to forecast which firms will beshorted.

This study examines the impact of building use on the prevalence of illegal parking in Copenhagen. Illegal parking poses significant challenges to urban mobility, environmental sustainability, and urban aesthetics. By focusing on Copenhagen, a city renowned for its progressive urban policies and commitment to green mobility, this research investigates how different building use categories such as commercial, residential, institutional, industrial, leisure, and technical affect illegal parking incidents. Utilizing data from 2021 to 2023, the study employs spatial analysis and Ordinary Least Squares (OLS) regression to analyze the relationship between building use and illegal parking. The findings indicate that commercial buildings have the highest positive impact on illegal parking prevalence, followed by technical and institutional buildings. Conversely, residential buildings, while highly prevalent, show a less significant impact on illegal parking. The study underscores the importance of urban design and effective parking management in mitigating illegal parking. Proximity to public transportation hubs shows a complex relationship with illegal parking, highlighting the need for comprehensive strategies that address both parking demand and public transport accessibility. These findings provide valuable insights for urban planners and policymakers aiming to develop sustainable urban environments.

Using three different models, we examine the determinants of average stock returns on the Stockholm Stock Exchange during 2012-2016. By using time-series data, we find that a Fama-French three-factor model (directed at capturing size and book-to-market ratio) functions quite well in the Swedish stock market and is able to explain the variation in returns better than the traditional CAPM. Additionally, we investigated if the addition of a Price/Earning variable to the Fama-French model would increase the explanatory power of the expected returns of the different dependent variables portfolios. We conclude that the P/E ratio does not influence the expected returns in the sample we used.