If different investors share a common goal, why are there differences in strategy choices and portfolio characteristics across investor classes? Elsaify (2022) attempts to provide an answer. In his study, he documents heterogeneity in investors’ processing abilities, which is the key factor influencing investor’s strategy choice and finds that such heterogeneity stems from factor timing ability.

According to the results, hedge funds seem to have the highest attention capacity, the most precise information and excel at factor timing. On the other hand, long-term investors (insurance companies and pension funds), brokers, and short-sellers exhibit low attention capacity because of their timing inability. They spend relatively more attention on the fundamental, their portfolios have the least dispersion and variance and their impact on factor returns is limited.

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.

Hello all,

What have we accomplished in the last month?

– A new Quantpedia Prime subscription + new Quantpedia Premium features
– 11 new Quantpedia Premium strategies have been added to our database
– 11 new related research papers have been included in existing Premium strategies during the last month
– Additionally, we have produced 8 new backtests written in QuantConnect code
– And finally, 5+2 new blog posts that you may find interesting have been published on our Quantpedia blog in the previous month

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.

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.

Skewness is one of the less-known but practical measures from statistics that can be used in trading. It is defined as a measure of the asymmetry of the probability distribution of a random variable around its mean. The goal of this analysis is to explore the commodity skewness trading strategy and perform the battery of robustness tests to see how sensitivity analysis changes overall results regarding performance, volatility, and Sharpe ratios.