Why has been alternative data recently so much popular? The answer most of the time hovers around the notion of “seeking the new alpha sources”. First, the hunt for alpha is huge due to the low yield world and is getting only bigger. Secondly, some of the more popular strategies can become crowded, leading to diminishing alpha or the risk of a sudden reversal in performance (all of us remember this year’s growth vs. value switch).

We at Quantpedia don’t create nor manage any alternative data sets. But we are aware of this trend, and we strive hard to find new alpha opportunities which may lie in these new data sources. From the database of almost 700 quantitative investment strategies Quantpedia has gathered, almost 100 strategies are based on alternative datasets. Today, we picked just 6 of them to give you a little taste of how these alternative strategies may look like, what kind of datasets they utilize and how they perform.

Corporate insiders often have insight into a company’s private information, which might help them predict how the shares’ price will move in the coming days. However, laws and regulations are designed to keep them from trading based on this knowledge, as it would be unfair and hurt the company’s other shareholders. This includes the prohibition of insider trading or designing a 10b5-1 plan, which we will discuss in this article. Anyways, knowing about incoming losses or the will to create profits might lead these insiders to different practices that could be questioned. Let’s look at some of the newest research concerning these issues.

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.

As of the first half of August, the year 2021 seems to be a phenomenal year for equities. World equities have earned more than +16%, and US equities, even more, topping +20% gains. Is there even any better strategy this year than just holding US equities? Well, yes, there are actually several of them. Are they all tied to US equities? Many of them are, but many of them are not. Some of them are not even tied to equities at all.

Note: This blog is Part 1 of a series. Part 2 is available here, and Part 3 is available here.