We started to systematically search for systematic cryptocurrency trading strategies in academic research approximately two years ago. This article is a short analysis of the performance of systematic crypto strategies in 2021. We conclude that non-price predictors offered advantages over price-based only trading strategies in the previous year-to-date and 12-months periods.

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.

Undoubtedly, in the recent past, the value is under scrutiny. Many researchers have aimed to answer questions like is the Value factor dead? The recent underperformance of the academic value factor (HML) can be tricky to understand, especially when most well-known and influential investors are labelled as “value” investors. A novel research paper by Choi et al. (2021) adds to the literature with its valuable insights. The main topic of the paper is the thorough examination of the B/M ratio in value style investing. Despite the well-known fact of the economy shift towards intangible assets, value investing still seems to be anchored to the B/M ratio that underestimates the true value. For example, Fama and French’s well-known HML value factor is based on B/M, value indexes are based on B/M (such as Russell value indexes) and subsequently, ETFs and benchmarks too.

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.

Nowadays, factor strategies are widely spread and used by practitioners, but this factor boom has given rise to some concerns. A key question is whether these strategies stay profitable once published and if they are not arbitraged away. Some strand of the literature suggests that there is a performance decay. A different view on performance decay is presented in the novel research of Kang et al. (2021), which indicates that the performance might be time-varying. Using the commodity market and premier anomalies such as momentum, basis, and value, the authors suggest a crowding in the factor strategies that predicts future performance. Crowded factors tend to underperform in future, and there is a significantly negative impact on the expected return. Moreover, the most substantial returns are connected with the least crowding activity. Therefore, the results are especially important for active factor traders.

Volatility clustering is a well-known effect in equity markets. In simple meaning, volatility clustering refers to a tendency of large changes in asset prices to follow large changes and small changes in asset prices to follow small changes. This interesting effect can be sometimes uncovered as one of the reasons for the functionality of some selected trading strategies. For example, low-volatility months in stock indexes (like the S&P 500 Index) are usually also months with higher performance. As volatility tends to cluster, a low volatility month in the present can signal a low volatility month with a better performance also in the future.

Based on this, we will be testing two hypotheses: (1) firstly, if there is a volatility clustering anomaly present in equity factor strategies; (2) secondly, if there is any performance pattern related to volatility.