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  • 1.
    Abo Al Ahad, George
    et al.
    Linköpings universitet, Institutionen för ekonomisk och industriell utveckling, Nationalekonomi. Linköpings universitet, Institutionen för teknik och naturvetenskap, Fysik och elektroteknik.
    Gerzic, Denis
    Linköpings universitet, Institutionen för ekonomisk och industriell utveckling, Nationalekonomi. Linköpings universitet, Institutionen för teknik och naturvetenskap, Fysik och elektroteknik.
    A Study on the Low Volatility Anomaly in the Swedish Stock Exchange Market: Modern Portfolio Theory2017Independent thesis Basic level (degree of Bachelor), 10 poäng / 15 hpOppgave
    Abstract [en]

    This study investigates, with a critical approach, if portfolios consisting of high beta stocks yields more than portfolios consisting of low beta stocks in the Swedish stock exchange market. The chosen period is 1999-2016, covering both the DotCom Bubble and the financial crisis of 2008. We also investigate if the Capital Asset Pricing Model is valid by doing a test similar to Fama and Macbeth’s of 1973.

    Based on earlier studies in the field and our own study we come to the conclusion that high beta stocks does not outperform low beta stocks in the Swedish stock market 1999-2016. We believe that this relationship arises from inefficiencies in the market and irrational investing. By doing this study we observe that, the use of beta as the only risk factor for explaining expected returns on stocks or portfolios is not correct.

  • 2.
    Abo Al Ahad, George
    et al.
    Linköpings universitet, Institutionen för ekonomisk och industriell utveckling, Produktionsekonomi.
    Salami, Abbas
    Linköpings universitet, Institutionen för ekonomisk och industriell utveckling, Produktionsekonomi.
    Machine Learning for Market Prediction: Soft Margin Classifiers for Predicting the Sign of Return on Financial Assets2018Independent thesis Advanced level (degree of Master (Two Years)), 20 poäng / 30 hpOppgave
    Abstract [en]

    Forecasting procedures have found applications in a wide variety of areas within finance and have further shown to be one of the most challenging areas of finance. Having an immense variety of economic data, stakeholders aim to understand the current and future state of the market. Since it is hard for a human to make sense out of large amounts of data, different modeling techniques have been applied to extract useful information from financial databases, where machine learning techniques are among the most recent modeling techniques. Binary classifiers such as Support Vector Machines (SVMs) have to some extent been used for this purpose where extensions of the algorithm have been developed with increased prediction performance as the main goal. The objective of this study has been to develop a process for improving the performance when predicting the sign of return of financial time series with soft margin classifiers.

    An analysis regarding the algorithms is presented in this study followed by a description of the methodology that has been utilized. The developed process containing some of the presented soft margin classifiers, and other aspects of kernel methods such as Multiple Kernel Learning have shown pleasant results over the long term, in which the capability of capturing different market conditions have been shown to improve with the incorporation of different models and kernels, instead of only a single one. However, the results are mostly congruent with earlier studies in this field. Furthermore, two research questions have been answered where the complexity regarding the kernel functions that are used by the SVM have been studied and the robustness of the process as a whole. Complexity refers to achieving more complex feature maps through combining kernels by either adding, multiplying or functionally transforming them. It is not concluded that an increased complexity leads to a consistent improvement, however, the combined kernel function is superior during some of the periods of the time series used in this thesis for the individual models. The robustness has been investigated for different signal-to-noise ratio where it has been observed that windows with previously poor performance are more exposed to noise impact.

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