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.

Nowadays, it is a standard that fillings such as 10-Ks and 10-Qs are analyzed with machine learning models. ML models can extract sentiment, similarity metrics and many more. However, words are not everything, and we humans also communicate in other forms. For example, we show our emotions through facial expressions, but the research on this topic in finance is scarce. Novel research by Banker et al. (2021) fills the gap and examines the CEOs facial expressions during CNBC’s video interviews about corporate earnings.

Summer Olympics are a major event that attracts attention from the moment the host country is announced. However, that’s not shocking. The Olympics require a lot of planning, infrastructure building and investments. Still, countries battle for the opportunity to host these events. Undoubtedly, hosting the Olympics is prestigious, helps tourism, and many even argue that it also helps the domestic economy despite the costs of hosting. Therefore, it is natural to expect that the Tokyo Olympics should impact the domestic stock market.

Nowadays, we see an increasing number of machine learning based strategies and other related financial analyses. But can the machines replace us? Undoubtedly, AI algorithms have greater capacities to “digest” big data, but as always in the markets, everything is not rational. Cao et al. (2021) dives deeper into this topic and examines the stock analysts. Target prices and earnings forecasts are crucial parts of the investing practice and are frequently used by traders and investors (and even ML-based strategies). The novel research examines and compares the abilities of human analysts versus the AI algorithm in forecasting the target price. As a whole, AI-based analysts, on average, outperforms human analysts, but it is not that straightforward. While AI can learn from large datasets, humans do not seem to be replaced soon. There are certain fields where human uniqueness is valuable. For example, in illiquid and smaller firms or firms with asset-light business models. Moreover, it seems that rather than competing with each other, AI and human analysts are complementary. The novel technology can be used with great success to help us in areas where we lag, and the combined knowledge and forecasts of AI and humans outperform the AI analyst in each year.

Quantitative based community is represented by the Quantconnect – Algorithmic Trading Platform, where quants can research, backtest and trade their systematic strategies. Additionally, similar to Seeking Alpha, there is a possibility to follow other quants/analysts through the open free market – Alpha Market.
To our best knowledge, the literature on community/social media alpha is scarce, and this paper aims to fill this gap. In the first part, we evaluate the benchmark strategy that consists of all strategies in the alpha market that are equally weighted. Moreover, through multidimensional scaling and clustering analysis, we examine how well can significantly lower amount of strategies track the aforementioned benchmark. This could solve the problem of costly and inconvenient following of every strategy in the market. Overall, this approach can lead to a strategy that follows the benchmark with drastically reduced costs, and these strategies can be even more profitable and less volatile.

Stay tuned for the 2nd, 3rd and 4th part of this series, where we will step on the gas and explore factor meta-strategies built on top of the QuantConnect’s Alpha Market.