Can we simplify the complexities of the stock market and distill them into a simple set of quantifiable metrics? A lot of academic papers suggest this, and they offer formulas that should make the life of a stock picker easier. Some of the most compelling methodologies within this realm are the F-Score, Magic Formula, Acquirer’s Multiple, and the Conservative Formula. These quantitative strategies are designed to identify undervalued stocks with robust fundamentals and potential for high returns. But do they really work out-of-sample? A new paper by Marcel Schwartz and Matthias X. Hanauer tries to answer this interesting question…

The year 2024 is nearly behind us, so it’s an excellent time for a short recapitulation. In the previous 12 months, we have been busy again (as usual) and have published over 70 short analyses of academic papers and our own research articles. The end of the year is a good opportunity to summarize 10 of them, which were the most popular (based on the Google Analytics ranking). The top 10 is diverse, as usual; once again, we hope that you may find something you have not read yet …

For those who have not yet had the chance to read it, we recommend the latest empirical study by Minghui Chen, Matthias X. Hanauer, and Tobias Kalsbach, which shows that design choices in machine learning models, such as feature selection and hyperparameter tuning, are crucial to improving portfolio performance. Non-standard errors in machine learning predictions can lead to substantial portfolio return variations, and authors are highlighting the importance of robust model evaluation techniques.

Active Share is a popular metric used to gauge how actively managed a portfolio is compared to its benchmark, but its predictive power for fund performance is questionable. Our research suggests that high Active Share often reflects exposure to systematic equity factors rather than genuine stock-picking skill. Additionally, inaccuracies in benchmark selection can distort the metric’s insights, making it unreliable as a standalone measure. A more effective approach is to conduct a factor analysis of alpha to better understand a manager’s performance and true sources of over/underperformance.

Studies using machine learning techniques for return forecasting have shown considerable promise. However, as in empirical asset pricing, researchers face numerous decisions around sampling methods and model estimation. This raises an important question: how do these methodological choices impact the performance of ML-driven trading strategies? Recent research by Vaibhav, Vedprakash, and Varun demonstrates that even small decisions can significantly affect overall performance. It appears that in machine learning, the old adage also holds true: the devil is in the details.

Short sellers often have a bad reputation, seen as market disruptors who profit from declining prices. Yet, they play a crucial role in making markets more efficient by identifying overvalued assets and correcting mispricings. A recent study uncovers another surprising aspect of their behavior: rather than just demanding liquidity, the most informed short sellers actually provide it. Using transaction-level data, the research shows that these traders supply liquidity, especially on news days and when trading on known anomalies, challenging the conventional view of short sellers as merely aggressive market participants.