Browsing by Author "Lindstedt, Philip"

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    Aktierekommendationers påverkan på aktiemarknaden
    (2022-07-07) Lindstedt, Philip; Wilner, Anton; University of Gothenburg/Department of Economics; Göteborgs universitet/Institutionen för nationalekonomi med statistik
    This study examines the impact of stock recommendations on the Swedish stock market. A recommendation can be either a buy recommendation or a sell recommendation. A recommendation is established by an analyst producing a target price, which is a forecast of the future stock price. If the target price is higher than today's share price, the analyst makes a buy recommendation; if it is lower, the analyst makes a sell recommendation. This study also examines stock analysts' valuation methods and how they determine a recommendation for a stock. Behavioural economics, on how people act during investments and the efficient market hypothesis are studied. By using data from January 2017 to December 2021 of the Stockholm Stock Exchange OMXS30 index, which contains the most traded stocks, the impact of a recommendation can be tested. An event study is used, it compares what the price of the stock would have been if the event had never taken place and compares it with the actual price after the event (Brown and Warner, 1980). This is done by using Campbell, Lo and MacKinlay (1997) principles and by doing this we can reflect a specific event and then evaluate how the recommendations have affected returns. The empirical result found in the study is that there is a positive as well as a negative relationship between stock performance and the publication of a buy or sell recommendation on the Swedish stock market.
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    Predicting Global Stock Returns Using Commodities: A Gradient Boosting Decision Tree Approach
    (2025-07-07) Frisell, Jesper; Lindstedt, Philip; University of Gothenburg/Graduate School; Göteborgs universitet/Graduate School
    We examine the predictability of stock returns using commodity futures prices across 39 countries from 1999 to 2024 using the XGBoost implementation of the Gradient Boosting Decision Tree approach. There is evidence of increased integration between commodities and stock markets. Despite this, research examining the predictability of commodities on stock returns is limited, especially on a global scale. The aim is to build on previous studies and explore heterogenous effects of commodity price changes on countries. We find evidence of predictability for four individual commodities and two commodity indices after sampling twelve commodities and four indices. Copper and crude oil show the strongest predictability among individual commodities, while industrial metals and energy demonstrate the strongest predictability among commodity indices. Our results also indicate strong heterogeneous effects, with some countries exhibiting significantly greater exposure to commodity prices. In particular, the Australian stock market is more exposed to price changes in copper and industrial metals, while the Norwegian market shows large sensitivity to oil and energy. Based on our findings, a competitive long-short trading strategy is proposed.