There are many ways one can lose money when investing, and exchange rates are one of the potential risk factors. Luckily, there are several ways to minimize this type of loss in your portfolio. Systematic FX hedging that uses currency factor strategies is a way of protecting an existing or anticipated position from an unwanted move in an exchange rate. It does not eliminate the risk of loss completely but helps to manage currency exposure better.

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.

Numerous new businesses are emerging related to autonomous traffic, clean energy, biotechnology, etc. Without any doubt, these new companies look promising and at least the technology behind them seems to be the future. Moreover, this novel trend is also supported by the most prominent index creators S&P and MSCI. Both providers have created numerous thematic indexes connected to these hot industries. The popularity has caused that ETFs are nowhere behind, and as a result, these thematic indexes could be easily tracked. However, popularity itself does not guarantee the best investment, and we should be interested in these indexes in greater detail. A vital insight provides the novel research paper of Blitz (2021). The findings are interesting – the thematic investors bet against quantitative investors or, more precisely, against the most common factors that are well-known from the asset pricing models.

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.

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.