Today we will again talk more about the portfolio management theory, and we will focus on techniques for combining quantitative strategies into one multi-strategy portfolio. So, let’s imagine we already have a set of profitable investment strategies, and we need to combine them. The goal of such “strategy allocation” usually is to achieve the best risk-adjusted return possible. There is no single correct solution to this task, but there are a few methods that we can try.

The “appropriate combination” highly depends on the type of strategies we are about to combine. Are we combining equity and bond strategies together? Are we combining equity strategies, with each one having an entirely different logic? Or do we rather need to assign weights to strategies that are similar in nature yet still different? We will focus this article on the last option – combining similar yet different strategies.

The CAPM model was a breakthrough for asset pricing, but the times where the market factor was most widely used are long gone. Nowadays, if we exaggerate a bit, we have as many factors as we want. Therefore, it might not be straightforward which factor model should be used. 

Hanauer et al. (2021) provide several insights into factor models. The authors postulate that the factor models should be examined in the international samples since this can be understood as a test for asset pricing models. The domestic Chinese A-shares stock market seems to be an excellent “playground” for the factors models, given the size of the Chinese stock market, but mainly because of its uniqueness. The paper compares the models (and factors) based on various methods (performance, data-driven asset pricing framework, test assets, turnovers and even transaction costs). Apart from valuable insights into the several less-known factors, the key takeaway message could be that the “US classic” Fama-French factor models perform poorly in China. The modified Fama-French six-factor model or q-factor is better, but overall, it seems that factor models designed for China, such as the model of Liu, Stambaugh and Yuan (2019), are the best.

In part 1 of our article, we analyzed tendencies and trends among the Top 10 quantitative strategies of 2021. Thanks to Quantpedia Pro’s screener, we published several interesting insights about them.

In part 2 of our article, we got deeper into the first five specific strategies, which are significantly outperforming the rest in 2021. 

Today, without any further thoughts, let’s proceed to the five single best performing strategies of 2021 as of August 2021.

In the first part of our article, we talked about quantitative strategies which achieved even better results in 2021 than passive US equity investors. Then, we focused more on tendencies and trends among the best quantitative investment strategies. Today we will talk more deeply about specific trading strategies and shortly describe number #10 to number #6 of the best quantitative strategies of 2021.

Can we explain stock momentum by industry, sector or factor momentum? Moreover, a similar question could be raised about the short-term reversal. The novel research by Li and Turkington (2021) uses a robust regression model to divide momentum and reversal returns into the main drivers. The individual momentum anomaly that broader market groups do not fully explain exists in the whole sample but is statistically weak. On the other hand, the reversal anomaly is highly significant. Secondly, the traditional 12-months momentum can be better explained by the factor momentum than the industry or sector momentum. Still, the industries, industry groups, sectors, and even factors have distinct drivers, and the anomalies seem different.

Momentum seems to be present everywhere and based on academic studies, it is even hard to find assets where the anomaly does not work. Among the large number of research papers related to momentum, the discovery of factor momentum is still relatively new. It is a truly important finding in the world of systematic strategies – there seems to be a return continuation among factors. The novel research of Fan et al. (2021) builds on the recent academic research and shows that, after all, the factor momentum might be different. To be more precise, the authors show that looking at the universe of 20 factor strategies, the factor momentum seems to work and can span individual equity momentum strategies (standard momentum, industry momentum and intermediate momentum). However, the factor momentum is mostly driven by only six factor strategies, and the return continuation of the remaining factors is weak. Additionally, those sixteen non-return continuation strategies cannot span the momentum effects mentioned above. Therefore, the results show that the factor momentum works on the aggregate but individually works much better. In fact, the factor momentum return of the six return continuation factor is significantly better compared to the rest or buy-and-hold portfolio. Moreover, the authors have also identified that the “best” factor momentum strategy is the Betting against beta and conclude that the reason is the unique weighting scheme utilized by the factor. The beta weighting assigns a higher weight to smaller companies, where the momentum tends to be stronger. Overall, the research paper is an important extension of the factor momentum literature.