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…

Equal-weighted benchmark portfolios are sometimes overshadowed by the more popular market capitalization benchmarks but are still popular and often used in practice. One of the advantages of equal-weighted portfolios is that academic research shows that in the long term, they tend to outperform their market-cap-weighted peers, mainly due to positive loadings on well-known factor premiums like size and value. So, if equal weighting outperforms market-cap weighting (in the long term), what options do we have if we want to outperform equal weighting? A recent paper by Cirulli and Walker comes to our aid with an interesting proposal …

In a game of poker, it is usually said that when you do not know who the patsy is, you’re the patsy. The world of finance is not different. It is good to know who your counterparties are and which investors/traders drive the return of anomalies you focus on. We discussed that a few months ago in a short blog article called “Which Investors Drive Factor Returns?“. Different sets of investors and their approaches drive different anomalies, and we have one more paper that helps uncover the motivation of investors and traders for trading and their impact on anomaly returns.

Bearish trends or deep corrections in international equity markets starting in 2022 and rising interest rates worldwide brought investors’ attention back to not only once-proclaimed dead factor investing. From long-run and short run, during different market cycles, different factors behave differently. What’s fortunate is that it is pretty predictable to some extent. Andrew Ang, Head of Factor Investing Strategies at BlackRock, in his Trends and Cycles of Style Factors in the 20th and 21st Centuries (2022), used Hodrick-Prescott (HP) filter and spectral analysis to investigate different models to draw some general conclusions on most-widely used factors. We will take a look at a few of quite the most interesting ones of them.

Continuing our research of different factor allocations and models, we will look at the evergreen momentum effect closer. Cakici, Fieberg, Metko, and Zaremba’s (January 2023) paper contributes to the never-ending debate of the chicken-or-egg problem of what comes first: Does the stock price momentum originate from the factor momentum? The study reexamined the relationship between the factor and price momentum on an extensive sample of 95 years of data from 51 countries. And what are the main takeaways? Let’s find out …

It would be great if the investment factors and trading strategies worked all around the world without change and under all circumstances. But, unfortunately, it doesn’t work like that. Some of the strategies are market-specific, as shown in this short analysis. The Chinese market has its own specifics, mainly higher representation of retail investors and lower efficiency. And it’s not alone; countless strategies work just in cryptocurrencies, selected futures, or some other derivatives markets. So, what’s the takeaway? Simple, it’s really important to understand that each anomaly is linked to the underlying dataset and market structure, and we need to account for it in our backtesting process.