The efficacy of ETF momentum strategies, while robust until around 2010, began to show signs of waning in subsequent years. This observation raises questions about the sustainability and adaptability of these strategies in varying market cycles. Central to this research is exploring how various factors/parameters—such as the ranking period, the selection quantity of assets, and the liquidity of ETFs—impact the performance of ETF momentum strategies. The aim is to uncover whether these strategies can deliver sustainable alpha in the complex and ever-evolving market landscape of the 2020s.

Investing in emerging markets (EM) or developed markets (DM) outside of the United States tends to follow cyclical trends. At times, it becomes popular and crowded to focus solely on U.S. stocks, while in other periods, the trend shifts to favor everything except U.S. equities. This inclination often relies on historical and past performance data, although it doesn’t guarantee identical outcomes in the future. But what drives these periods of popularity? When do U.S. markets outperform Emerging Markets or other Developed Markets? When do large-cap stocks outperform small-cap stocks, and when do growth stocks outperform value stocks? Are those ebbs and flows in the performance of major thematic investments somehow interlinked, and can we uncover some insights into why this occurs? Those are the questions we will try to answer in the following analysis.

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.

“Si vis pacem, para bellum”, is an old Roman proverb translated to English as “If you want peace, prepare for war”, and it is the main idea behind the military policy of a lot of modern national states. In the current globally interconnected world, waging a real “hot war” has very often really negative trade and business repercussions (as the Russian Federation realized in 2022). Still, even though wars among developed nations are luckily not as popular as they used to be, modern states heavily invest in their own defense. Nobody wants to be caught military unprepared in case of a local or global geopolitical crisis. A strong military should bring a safe environment to do business, and trade should flourish uninterrupted. But are all those national military expenditures financially rewarded? Do stock markets of countries with a strong military outperform their peers? That’s the question we have decided to answer in the following analysis.

Some of the more vague terms in Technical Analysis are really hard to quantify as nearly every TA user defines and interprets them differently. We mean mainly TA patterns like supports, resistances, trend lines, double tops, double bottoms, and/or more complex patterns like head-and-shoulders. Now, what we can do with that? We tried to spend some time and fought a little with some of these TA terms, and the following article/study results from our attempts to quantify a tiny subset of the world of Technical Analysis patterns.

Sector/industry picking or country picking can be a profitable trading style but is usually much more challenging than it seems at first sight. Building a good trading model requires a lot of research and dedication. Unfortunately, due to the limited numbers of industries and countries, sorting them on aggregate characteristics can wash out important cross-sectional variations in the characteristics and lead to concentrated portfolios prone to noisier realized returns.

In their fresh Dimensional Fund Advisors research piece, Dong, Huang, and Medhat (2023) touch on the question of whether investors should systematically emphasize certain industries or countries to increase expected returns. Their overhead view provides new insights and sums that investors will likely be better off pursuing premiums in the larger cross-section of individual securities and maintaining broad diversification across the smaller cross-sections of industries and countries.