Continuing our research of different factor allocations and models, we will look at the evergreen momentum effect closer. Cakici, Fieberg, Metko, and Zaremba’s (January 2023) paper contributes to the never-ending debate of the chicken-or-egg problem of what comes first: Does the stock price momentum originate from the factor momentum? The study reexamined the relationship between the factor and price momentum on an extensive sample of 95 years of data from 51 countries. And what are the main takeaways? Let’s find out …

Today, we will evaluate some specifics that are akin to the now second-largest market in the world – China. The abundance of “shell companies” creates a problem when researchers try to uncover sources of alpha in the Chinese market. We present recent research by Zhiyong Li and Xiao Rao (2022) that proposes a new alternative filter, which excludes the stocks with a high estimated shell probability when constructing equity factor models.

Arbitrage is a central concept in finance. It is defined as simultaneous long and short positions in similar assets to exploit mispricing. Hedge funds experienced fast growth over the past three decades, as real-world arbitrageurs as a group. As they increasingly influence the financial market, it is important to understand the economic drivers of hedge fund returns. Therefore we would like to present a paper dealing with the development of a parsimonious factor model, based on anomalies, to explain hedge fund returns.

Factor investing and detailed allocation according to different sets of factors are lively researched topics with many unanswered and open questions. Many views are often conflicting and from both radical sides — on one, that only a few factors should be necessary to explain the cross-section of mean returns, which is attractive, especially because of its simplicity; on the other, that you can use complex (authors examine the 161 “clear predictors” and 44 “likely predictors”) combinations of factors from less known and unorthodox models, but falling into dangerous and often unexamined “factor zoo” with many undesirable, unexamined and non-controllable outcomes. A huge gap is often seen in finance between the theory of academia and practical applications (by PMs [portfolio managers]), and so is especially present in this one. Let’s take a look at what the complexity of factors does for various equities pricing models.

The problem of missing financial data is widespread yet often overlooked. An interesting insight into the structure of missing financial data provides a novel research paper by authors Bryzgalova et al. (2022). Firstly, examining the dataset of the 45 most popular characteristics in asset pricing, the authors found that missing data is frequent among almost any characteristic and affects all kinds of firms – small, large, young, mature, profitable, or in financial distress. The requirement of multiple characteristics simultaneously makes the problem even worse. Moreover, the data is not missing randomly; missing values clusters both cross-sectionally and over time. This may lead to a selection bias, making most famous ad-hoc approaches like the median invalid. Considering the abovementioned findings, the authors propose a novel imputation method based on Principal Component Analysis (PCA).

We have brought attention to the importance of evaluating factors models in different market regimes, and now, we will take a closer look at the size factor. Size [SMB (small minus big)] factor is a popular investment choice for asset investigation by many portfolio managers worldwide. The Size earned prominence in Fama and French’s three and five-factor models, and enjoy the continued discussion about its place in today’s portfolio construction. But it’s crucially important for investors seeking to capture the Size premium to realize that it is dependent on the monetary policy being pursued by the Federal Reserve, as the monetary easing seems to induce a Size premium.