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.

We have a new video featuring three examples of how to built new strategies on top of ideas from Quantpedia’s database. We hope you will like it …

Stock prices react to the new information that investors continually receive from many sources. There are some major events, which are commonly connected with a new piece of information and subsequent reactions of investors. For example, quarterly earnings-announcements are the cause of the post-earnings announcement drift or PEAD. According to the PEAD, prices tend to continue to drift up (down) after positive (negative) news. But news related to quarterly announcements is not the only important information. A novel research paper written by the Hong and Yu explores implications of the month-end reporting, analyst revisions and management guidance that are coming to market usually in the first half of each month and are also connected with drifts that offer practitioners profitable opportunities.

Authors: Claire Yurong Hong and Jialin Yu

Title: Month-End Reporting, Cash-Flow News, and Asset Pricing

During past months we made a set of articles analyzing the performance of equity factors and selected systematic strategies during coronavirus crisis. These articles were short-ranged with data only from the start of the year 2020, which is enough for the purpose of the quick blog posts, but very short-sighted to see the nature of these strategies. Therefore, we expanded the time range by 20 years. For a better understanding of hedge possibilities of these strategies, we have added a comparison to essential safe-haven assets, not only to equities.

What will happen if we mix ESG scoring with price momentum? Can we improve simple ESG investing strategy?

The pure price momentum can be combined with ESG scores using a Knapsack algorithm. Knapsack algorithm is a well-known mathematical problem of optimization, and in the case of momentum and ESG, can be used to make the momentum portfolios significantly more responsible, with lower volatility and better risk-adjusted return. The second option is to make the ESG portfolio substantially more profitable by using Knapsack algorithm to construct high ESG portfolio with large momentum. The approach resulted in a strategy with high ESG score and compared to pure momentum or momentum-ESG strategy, with significantly reduced volatility. Therefore, the ESG-momentum strategy has the best risk-adjusted return, the lowest drawdown, the lowest volatility and the most consistent returns.