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.

The topic of quantum computing has been gaining popularity recently, and both the scientific community and investors seem to have high hopes for its future. It seems that this brand-new technology could revolutionize various aspects of computing as we currently know them. Great contributions could be made in the fields of medicine and healthcare, security, and computability [1], as well as in the field of finances, which interests us here at Quantpedia the most. Quantum computers are especially great in optimization tasks, so optimizing a portfolio could be one of the key contributions in our interest. [2] In this article, we would like to introduce the concept of quantum computers, their current state, their potential use in finance, and more.

The following article is a short distillation of the research paper Leveraging the Technical Competence of a Stock for the Purpose of Trading written by Rishabh Gupta. The author spent a summer internship at Quantpedia, investigating the Patent-to-Market (PTM) ratio developed by Jiaping Qiu, Kevin Tseng, and Chao Zhang. The PTM ratio uses public information about the number and dates of patents assigned to publicly listed companies, calculates an expected market value of patents, and tries to predict future stock performance.

It would be great if the investment factors and trading strategies worked all around the world without change and under all circumstances. But, unfortunately, it doesn’t work like that. Some of the strategies are market-specific, as shown in this short analysis. The Chinese market has its own specifics, mainly higher representation of retail investors and lower efficiency. And it’s not alone; countless strategies work just in cryptocurrencies, selected futures, or some other derivatives markets. So, what’s the takeaway? Simple, it’s really important to understand that each anomaly is linked to the underlying dataset and market structure, and we need to account for it in our backtesting process.

Choosing the correct portfolio-construction techniques is very important. The new paper that is written by Amar Soebhag, Bart van Vliet, and Patrick Verwijmeren explores the various ways in which different design choices in portfolio construction can, either intentionally or unintentionally, influence and distort the statistical results of a market factor’s research. Their takeaway is that seemingly small differences in design can significantly impact the resultant portfolio’s performance.