The final thesis deals with the construction of a stock portfolio. The traditional portfolio theory models of Markowitz and Sharpe and anomalies based on fundamentals are shown and applied in Germany. In the first part, portfolio theory and fundamentals are explained. The mathematical model is demonstrated in the second part. Empirical results are shown in the last part.

We investigate the novel idea of using computer vision to predict future stock price movement, which is performed by training a convolutional neural network (CNN) to detect patterns in images of stock graphs. Subsequently, we create a portfolio strategy based on the CNN stock price predictions to see if these predictions can generate alpha for investors. We apply this method in the Swedish stock market and evaluate the performance of CNN portfolios across two different exchanges and various stock indices segmented by market capitalisation. Our findings show that trading based on CNN predictions can outperform our benchmarks and generate positive alpha. Most of our portfolios generate positive alpha before transaction costs, while one also generates positive alpha after deducting transaction costs. Further, our results demonstrate that CNN models are capable of successfully generalising their trained knowledge, being able to detect information in stock graphs it has never seen before. This suggests that CNN models are not limited to the characteristics present in their training data, indicating that models trained under one set of market conditions can also be effective in a different market scenario. Our resultsfurther strengthen the overall findings of other researchers utilising similar methods as ours.