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.

Professor Robert Shiller’s CAPE (cyclically adjusted price-to-earnings) ratio is well-known among the investment community. His methodology for assessing a valuation of the U.S. equity market is undoubtedly the most cited and discussed. Therefore, it’s not surprising that there exists quite a lot of papers that try to refine and expand the CAPE’s methodology. One such last attempt is the work of James White and Victor Haghani, whose research paper revolves around the use of a modified version of the Cyclically-Adjusted Price Earnings (CAPE) ratio, termed P-CAPE. Their methodology aims to improve the estimation of long-term expected real returns of the stock market by incorporating the dividend payout ratio into the traditional CAPE metric.

Equal-weighted benchmark portfolios are sometimes overshadowed by the more popular market capitalization benchmarks but are still popular and often used in practice. One of the advantages of equal-weighted portfolios is that academic research shows that in the long term, they tend to outperform their market-cap-weighted peers, mainly due to positive loadings on well-known factor premiums like size and value. So, if equal weighting outperforms market-cap weighting (in the long term), what options do we have if we want to outperform equal weighting? A recent paper by Cirulli and Walker comes to our aid with an interesting proposal …

The area we want to explore today is an interesting intersection between quantitative and more technical approaches to trading that employ intuition and experience in strictly data-driven decision-making (completely omitting any fundamental analysis!). Can just years of screen time and trading experience improve the metrics and profitability of trading systems through discretionary trading actions and decisions?

It is not easy to build a robust trend-following strategy that will withstand different difficult market conditions and bring consistent results. The author of today’s work was not frightened by this task and delivered a full framework on how to design a robust trend-following strategy step by step.