Some of the more vague terms in Technical Analysis are really hard to quantify as nearly every TA user defines and interprets them differently. We mean mainly TA patterns like supports, resistances, trend lines, double tops, double bottoms, and/or more complex patterns like head-and-shoulders. Now, what we can do with that? We tried to spend some time and fought a little with some of these TA terms, and the following article/study results from our attempts to quantify a tiny subset of the world of Technical Analysis patterns.

Cremers, Liu, B. Riley (Apr 2023) share their view on and try to answer the question: how well do factor investing funds perform? They conclude that, on average, factor-investing funds do not outperform. But using active characteristic share (ACS)—an adaption of Cremers and Petajisto’s (2009) original active share measure—, the authors demonstrate that the factor investing funds that match indexes the most have significantly better performance. An equal-weighted portfolio of factor investing funds in the lowest tercile of ACS outperforms an equal-weighted portfolio of funds in the highest tercile by 3.82% per year (t-stat = 3.89) using the CAPM and by 1.08% per year (t-stat = 2.01) using the CPZ6 model.

Beta-adjusted returns equity factors are considerably more stable, indicating that factor construction methodologies may be improved beyond dollar and size neutrality. Low-beta effect at the level of factors confirms the existence of seasonal and momentum effects in the cross-section of factor returns. Altogether, these insights deepen the understanding of factor behavior and can aid the development of more robust factor-based investment strategies.

Sector/industry picking or country picking can be a profitable trading style but is usually much more challenging than it seems at first sight. Building a good trading model requires a lot of research and dedication. Unfortunately, due to the limited numbers of industries and countries, sorting them on aggregate characteristics can wash out important cross-sectional variations in the characteristics and lead to concentrated portfolios prone to noisier realized returns.

In their fresh Dimensional Fund Advisors research piece, Dong, Huang, and Medhat (2023) touch on the question of whether investors should systematically emphasize certain industries or countries to increase expected returns. Their overhead view provides new insights and sums that investors will likely be better off pursuing premiums in the larger cross-section of individual securities and maintaining broad diversification across the smaller cross-sections of industries and countries.

Sustainable investing is a topic we cover extensively in the form of systematic ESG investing strategies and/or blogs. Enormous capital allocation decisions are based on ESG ratings given by various agencies. The problem is that there is no actual normalization and standardization, which creates wrinkles on the faces of hedge and pension fund managers when making those critical individual equity allocations, be they inclusions or exclusions.

Ehling, Paul and Sørensen, Lars Qvigstad (January 2023) new paper analyzes the portfolio choice consequences arising from the well-known divergence of ESG scores. From a risk point of view, the optimized ESG portfolios differ more across each other than they differ relative to the benchmark, suggesting that the different rating agencies’ scores result in substantially different portfolios.

And how dissimilar are ratings among the agencies? We find staggeringly comic that ratings of Warren Buffett’s (and Charlie Munger’s) Berkshire Hathaway (NYSE: BRK.B) disagree by a large margin (see red ellipses in Figure 2 below). While Sustainalytics give it an outperforming rating, FTSE and MSCI regard it as one of the top laggards.

Science has been in a “replication crisis” for more than a decade. But what does it mean to us, investors and traders? Is there any “edge” in purely academic-developed trading strategies and investment approaches after publishing, or will they perish shortly after becoming public? After some time, we will revisit our older blog on this theme and test the out-of-sample decay of trading strategies. But this time, we have hard data – our regularly updated database of replicated quant strategies.