Browsing by Author "Pettersson, Gustav"

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    Navigating in the ESG score jungle- A cross-sectional approach to determine the ESG risk factor
    (2022-06-29) Pettersson, Gustav; Öhrn, Mattias; University of Gothenburg/Graduate School; Göteborgs universitet/Graduate School
    This thesis examines the relationship between ESG scores and yearly excess return between 2010 and 2020 on the S&P 500 Index. With a solid theoretical background regarding investor preferences, we ask whether investors accept lower returns for holding greener assets. Our method is a cross-sectional approach, using pooled time-series regressions and Fama-MacBeth regressions, where we seek to determine the ESG risk score factor. We find significant evidence that ESG scores have a negative relationship with yearly excess return in all our regressions when controlling for other return predictors and the Sin Stock anomaly. This relationship holds for the overall ESG score and the separate ESG pillar scores, Environmental, Social, and Governance. Our results prove to be consistent with previous research regarding ESG-motivated investors. We found inconsistent results with previous research regarding the Governance pillar score, arguing that the Governance pillar score may not be an appropriate proxy. Our results remain consistent while conducting further robustness tests with clustering on the sector level.
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    Reliable and User Friendly Low Bandwidth Web Surfing - Performance and Reliability Improvements of a Proxy Server and Web Browser Prototype
    (2020-10-29) Pettersson, Gustav; Simone Damaschke, Loke; Ala Hadi, Tarik; Blid Sköldheden, Kristoffer; Göteborgs universitet/Institutionen för data- och informationsteknik; University of Gothenburg/Department of Computer Science and Engineering
    In remote locations, internet access can be enabled using satellite connections, such as the Iridium Satellite Network. The Iridium GO! device provides a bandwidth of 2.4 kbit/s. On such a low bandwidth it would take hours to download most modern websites. In 2018, a prototype which enables general web browsing on a low bandwidth was developed at Charmers. The prototype acts as a proof of concept for the use of a performance enhancing proxy server to extract data in order to reduce content size and achieve viable loading times. The 2019 prototype replaces components of the 2018 iteration, providing a faster and more reliable service. In particular, no excess data is being sent and the delay caused by compression and data extraction is notably shorter, resulting in a 66% reduction on average to the time between sending a request and the moment the first content arrives to the client.
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    Tidying up the factor zoo: Using machine learning to find sparse factor models that predict asset returns.
    (2020-07-01) Klingberg Malmer, Oliver; Pettersson, Gustav; University of Gothenburg/Department of Economics; Göteborgs universitet/Institutionen för nationalekonomi med statistik
    There exist over 300 firm characteristics that provide significant information about average asset return. John Cochrane refers to this as a “factor zoo” and challenges researchers to find the independent characteristics which can explain average return. That is, to find the unsubsumed and non-nested firm characteristics that are highly predictive of asset return. In this thesis we act on the posed challenge by using a data driven approach. We apply two machine learning methods to create sparse factor models composed by a small set of these characteristics. The two methods are one unsupervised learning method, the Principal Component Analysis, and one supervised learning method, the LASSO regression. The study is done using the S&P 500 index constituents and 54 firm characteristics over the time period 2009-07-01 to 2019-07-01. The performance of the factor models is in this study measured using out-of-sample measurements. Using established methods of post-LASSO regression and new developed techniques for variable selection based on PCA, we generate four new factor models. The latter mentioned variable selection method based on PCA is, to our knowledge, an original contribution of this thesis. The generated factor models are compared against the Fama French factors in the out-of-sample test and are shown to all outperform. The best performer is a LASSO generated factor model containing 6 factors. By analysing the results we find that momentum factors, such as price relative to 52-week-high-price, are highly predictive of return and are commonly selected factors, which confirms the results of previous responses to the same challenge.