High-yield bond ETFs represent a unique financial vehicle: they are highly liquid instruments that hold inherently illiquid securities, creating a fertile ground for predictable market behaviors. Our latest research uncovers an intriguing anomaly within these ETFs, similar to those observed in the stock market: overnight returns are systematically higher than intraday returns. This overnight anomaly in high-yield bonds is not only prevalent but also exhibits a distinct seasonal pattern, primarily from Monday’s close to Tuesday’s open and from Tuesday’s close to Wednesday’s open. Additionally, this anomaly displays a reversal characteristic, where overnight performance is typically more robust following a negative close-to-close performance in the preceding period. These findings reveal potential opportunities for trading strategies that leverage these consistent overnight return patterns, offering new insights into high-yield bond trading dynamics.

In the complex world of financial markets, subtle patterns often reveal themselves through careful observation and analysis. Among these is the intriguing phenomenon we can call the “Lunch Effect,” a pattern observed in U.S. stock indexes where market performance tends to exhibit a distinct positive shift immediately after the lunch break, following a typically negative or flat performance earlier in the trading day right before the lunch. This lunchtime revival is not an isolated occurrence; it shares a curious connection with the “Overnight Effect,” a well-documented tendency for the U.S. stock market to experience the bulk of its appreciation during non-trading hours, with relatively little movement during the trading day itself. Together, these effects underscore the intricate dynamics of market behavior, where timing and investor psychology play crucial roles in shaping intraday and overnight market performance. Understanding these patterns can offer valuable insights into the rhythm of the markets and the underlying factors that drive short-term price movements.

One of the main reasons why the Pragmatic Asset Allocation Model was designed is to give investors a tax-efficient possibility to invest in a global equity portfolio with a lower risk than the passive buy&hold approach. Therefore, the PAA model is not the “absolute return” model but rather the tactical model that prefers to invest in the equity risk premium and move to the hedging portfolio (gold, treasuries, or cash), only for short periods and only when it’s absolutely necessary. We use price trend+momentum indicators and yield curve inversion as signals for such situations when (based on the past data) there is a higher probability of recessions and equity bear markets. What is unusual in the current situation is the length of time that the YC is inverted (19 months at the moment), which makes it the 2nd longest YC inversion in the last 100 years, and we are analyzing the implications for the PAA model.

Our new study aims to investigate the lead-lag effect between prominent, widely recognized stocks and smaller, less-known stocks with similar ticker symbols (for example, TSLA / TLSA), a phenomenon that has received limited attention in financial literature. The motivation behind this exploration stems from the hypothesis that investors, especially retail investors, may inadvertently trade on less-known stocks due to ticker symbol confusion, thereby impacting their price movements in a manner that correlates with the leading stocks. By examining this potential misidentification effect, our research seeks to shed some light on this interesting factor.

In one of our older posts titled ‘Case Study: Quantpedia’s Composite Seasonal / Calendar Strategy,’ we offer insights into seasonal trading strategies such as the Turn of the Month, FOMC Meeting Effect, and Option-Expiration Week Effect. These strategies, freely available in our database, are not only examined one by one, but are also combined and explored as a cohesive composite strategy. In partnership with Deltaray, using MesoSim — an options strategy simulator known for its unique flexibility and performance — we decided to explore and quantify how our Seasonal Strategy performs when applied to options trading. Our motivation is to investigate whether this strategy can be improved in terms of risk and return. We aim to systematically harvest the VRP (volatility risk premium) timing the entries using calendar strategy to avoid historically negative trading days.

In the fast-paced world of cryptocurrencies, understanding market sentiment can provide a crucial edge. As investors and traders seek to anticipate the volatile movements of Bitcoin, innovative approaches are continuously explored. One such method involves leveraging Google Trends data to gauge public interest and sentiment towards Bitcoin. This approach assumes that search volume on Google not only reflects current interest but can also serve as a predictive tool for future price movements. This blog post delves into the intricacies of using Google Trends as a sentiment predictor, exploring its potential to forecast Bitcoin prices and discussing the broader implications of sentiment analysis in the financial market.