Machine Learning will quite certainly continue to be a hot topic in 2024, and we are committed to bringing you new developments and keeping you in the loop. Today, we will review original research from Demirbaga and Xu (2023) that highlights the critical role of machine learning model execution time (combination of time for ML training and prediction) in empirical asset pricing. The temporal efficiency of machine learning algorithms becomes more pivotal, given the necessity for swift investment decision-making based on the predictions generated from a lot of real-time data. Their study comprehensively evaluates execution time across various models and introduces two time-saving strategies: feature reduction and a reduction in time observations. Notably, XGBoost emerges as a top-performing model, combining high accuracy with relatively low execution time compared to other nonlinear models.

The primary motivation behind our study stems from an observation of the Global Tactical Asset Allocation (GTAA) strategies throughout the existing papers – the majority of them require relatively frequent rebalancing from the point of view of the ordinary investor. Portfolio rebalancing is usually done on a weekly or monthly basis, and while this period may seem overly boring and slow for the majority of traders (who like to trade on intraday or daily basis), fans of GTAA strategies are not traders; they are investors. Of course, some like to follow the ebbs and flows of the market. But a lot of investors just want to have a life. The financial market is not their hobby. However, on the other hand, they also do not want to hold just the passive buy & hold portfolio. Recognizing the demand for the semi-active strategy, we introduce our novel Pragmatic Asset Allocation.

The period of high inflation in the 1970s prompted researchers to carefully examine the relationship between inflation and stock returns and to look for ways to avoid unexpected inflation. The year 2022 brought back inflationary pressures to the U.S. economy not seen in more than 40 years, and this has spurred new efforts to answer long-standing questions about inflation and asset prices. Authors from the Board of Governors of the Federal Reserve System (2023) bring a fresh perspective on this topic, and their paper allows us to get a FED insider’s view on the ageless question of how inflation affects equity returns.

In today’s fast world, where information flows freely and transactions happen at the speed of light, the significance of cybersecurity cannot be overstated. But it’s no longer just a concern for IT professionals or tech enthusiasts. The specter of well-documented hacks and phishing incidents casts a long shadow over investors, acting as powerful illustrations of how security breaches, vulnerabilities, and cyber threats can reverberate through financial markets. In this blog post, we’ll delve into the intricate relationship between cybersecurity risk and stock performance, uncovering how these digital hazards can influence financial markets.

Social media are often the main and primary choice of information in almost every area of our lives, and they also influence the financial decisions of retail traders and investors. A lot of people give opinions anywhere on the Internet; some are respected, others are disrespected, some are more well-known, and others obscure. But the power of those people, financial influencers, as a group, is substantial as they create the market sentiment. But what’s the real value of their advice? Can we extract useful information from their opinions?

The part of the attention of our own research from the last few months was a little skewed on the side of countries’ indices and their corresponding ETFs representing them, and we finally conclude our “trilogy” of investigation on the efficiency of these markets. Firstly, we analyzed price-based valuation measures, and then, in November, we investigated the impact of military expenditures on the performance of international stock markets. We will wrap up this mini-series by analyzing a few additional alternative datasets containing variables we thought might be of interest in meaningfully describing each country’s societal standing – the climate change awareness index, the happiness score, the corruption perception index, and the income inequality score.