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Stock PickingQuantitative investment methods have gained foothold in the financial world in the last ten years. This paper shows how Bayesian Networks can be used to create a computerized stock-picking model. By using historical data for 14 different economic relevant variables the model is designed to give trading recommendations (buy or sell) for the different companies included in a given dataset. The model has a hitrate of 60% and it generates an average return of 15.1% in each of the five investment periods tested. The model is found to give a significant higher return than the mean value of randomly generated portfolios and can therefore be said to posses skills when it comes to stock picking.
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