The US dollar is the world’s primary reserve currency, is the most widely traded currency in the world (making up over 85% of all foreign exchange transactions), and is used as the benchmark currency for pricing many commodities such as oil and gold. We can say that the US dollar is the blood of the current financial system. A few months ago, we shared how to build a really long-term (nearly 100 years long) history of the USD exchange rate. Therefore, as we already have the data, we can now perform the cross-asset analysis to study the impact of the US Dollar’s strength or weakness on the performance of other asset classes, notably US equities, US treasury bonds, and commodities.

Exchange-traded funds (ETFs) are incredibly versatile investment vehicles. They have become more popular in recent years as investors have grown more comfortable with passive investing strategies. But ETFs can be very useful also in active trading strategies, as they can be used to gain exposure to specific markets, sectors, or themes. But when you invest in ETFs or trade them regularly; it’s really good to look under the hood and learn some tricks where to obtain a new source of alpha. And one such possible source or information advantage may be the possibility of analyzing the ETF flows data …

The theme of rebalancing in longer-term investing is neglected but important as it influences the overall portfolio’s performance and risk. Unfortunately, many investors are inconsistent in choosing dates for their rebalances of portfolios, resulting in hardly predictable results (whether positively or negatively affecting it), and not contributing to handling risk management properly. The following article presents our analysis of the impact of rebalancing on portfolio returns. It also serves as an introduction to the methodology for an upcoming Quantpedia Pro report that our users would be able to use to quickly assess the impact of the rebalancing period on any selected combination of trading strategies, custom equity curves, and ETFs.

Life is not always rosy; many tragedies and unexpected events hurt individuals and society. While some are hardly avoidable, such as natural disasters, some others as wars, are generally only functions of hate and greed. In the case of predictable events, risk measures can be employed, but unexpected outbreaks of aggression can hardly be hedged across the spectrum of different financial assets. We had previously touched on a similar topic and looked at some historical geopolitical shocks and price reactions around that time. Now, we would like to do a short review of an interesting 140-page paper by Dat Mai and Kuntara Pukthuanthong (2022), which, while not providing actionable strategy, provides insightful retrospection and takes war topic modeling to the higher level, covering developing narratives and influence factors extensively.

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.

Cryptocurrencies are a very controversial asset class. Some people may hate it, others may glorify it, and a significant part may ignore it. But what’s the opportunity cost of complete ignorance? Are we able to numerically calculate it? That’s a hard question that Duchin, Solomon, Tu, and Wang tried to answer in their recent paper, and we will take a look at some of their findings and discuss it.