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.

Countries from the majority of the developed world face one challenge: Their population is steadily aging; the average age of individuals has been rising over recent periods. The United States is not different in this sense. Whenever there’s a never-inevitable reaching of higher ages, people reconsider their choices and often cut on riskier ones. So, is there a potential link between demographic changes associated with aging and aggregate financial market outcomes?

If you’re a regular reader of our blogs (and we hope you are!), you would not miss that we like to touch macro-economic subjects. One of that never-fading topics is the role of gold as a crisis hedge. The probably most known commodity is a popular choice for a portion of the total portfolio, from small investors to central banks, for various reasons (be it diversification or hedging). So let’s not further delay it, and today we ask: Is gold really a safe haven?

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.

Cryptocurrencies are a high-risk and very speculative asset class that, from being used only by tech geeks worldwide, spread from small retail craziness of early adopters to institutional adoption and mainstream. Some claim it to be a world-changing concept with the utilization of blockchain (databases) and smart contracts that open a wide range of opportunities, from decentralizing finance to self-governing algorithms; some others point to unnecessary scams, money laundering, and bubbles. We have been covering the concepts and topics relating to crypto extensively. This article will continue our investigation of this interesting field. We would like to test how the price action looks around some of the events unique to the cryptocurrency world – namely the Bitcoin reward halvings and hard and soft forks in Bitcoin and Ethereum networks.