Mimicking Claimed Alpha Generating Strategies

Sammanfattning: This research paper focuses on the implementation and evaluation of Minervini's momentum analysis techniques in an algorithmic approach. The study aimed to assess the limitations and challenges associated with executing Minervini's strategy in an algorithmic trading system. Several technical restrictions, practical application problems, and the exclusion of fundamental and catalyst aspects contribute to the implementation of a primitive variant of Minervini's strategy. The challenges included the subjective nature of base patterns making bases difficult to identify and limitations in risk and position sizing. However, despite the challenges, the algorithmic approach offers advantages such as the ability to analyze a large number of stocks rapidly. It is suggested to use the algorithm as a tool for stock exclusion rather than fully automating the buying and selling decisions. The research investigates the possibility of generating excess returns in Sweden, Denmark, and Finland using the implemented algorithm over different time periods from 2008 to 2023. Hundreds of stocks were divided up into 18 stock portfolios based on market capitalization size calculations for a given year. These portfolios were traded using both the momentum strategy and an index strategy. The empirical results indicate that small-cap portfolios exhibited consistent excess returns compared to mid-cap and large-cap portfolios, particularly during high volatility periods. However, the research did not account for transaction costs, which are essential to evaluate the strategy's net returns in real-world scenarios. Despite the exclusion of transaction costs in the study, the significant excess returns observed in small-cap portfolios indicate that the implemented momentum strategy performs notably better for small-cap stocks compared to mid-cap and large-cap stocks. This finding contradicts the efficient market hypothesis, assuming equal transaction costs across different market capitalizations. Further research should consider incorporating transaction costs to gain a more comprehensive understanding of the strategy's overall performance and its practical implications for various market segments. Future research should consider incorporating transaction costs and optimizing the stop-loss and profit-taking levels, and exploring a weekly-based approach instead of a daily-based approach. Additionally, volume analysis, data handling improvements, and a more detailed analysis of buy and sell decisions are recommended to optimize the algorithm's performance for future research. To summarize, while the implemented algorithm does not fully mimic Minervini's strategy, it offers valuable insights and potential value, especially in small-cap stocks. Further research and optimization are required to enhance its effectiveness and address the identified limitations.