Seasonality is a well-known phenomenon in the commodity markets, with certain sectors exhibiting predictable patterns of performance during specific times of the year. These patterns often attract investors who aim to capitalize on anticipated price movements, creating a self-reinforcing cycle. But what if you could stay one step ahead of the crowd? By front-running these seasonal trends—buying sectors with expected positive performance (or shorting those with negative seasonality) before their favorable months begin—you can potentially gain a significant edge over traditional seasonality-based strategies. In this blog post, we explore how to construct and backtest a systematic strategy using commodity sector ETFs to exploit this seasonal front-running effect.

Our research will explore the intriguing phenomenon of the Pre-Holiday effect in commodities, particularly crude oil and gasoline. Historical data reveals a short-term price drift prior to major U.S. holidays, suggesting a trend in these markets. We hypothesize that this anomaly may be driven by increased demand for oil and its derivatives, such as gasoline, as people prepare for travel, often by car, during the holiday season. This seasonal behavior offers unique opportunities for market participants.

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.

Choosing the right investment strategy plays a crucial in portfolio allocation decisions, particularly when considering both private and public asset classes. While the reported performance of public assets typically matches their real-world performance, the same cannot be said for private assets due to the complexities of fund selection, commitment pacing, and return on uncalled and uncommitted capital. Fortunately, there are ways to incorporate public and private asset classes into one portfolio optimally. One example is the recent paper written by Xiang Xu, which introduces the Fair Comparison (FC) framework, which provides a methodology to measure the real-world performance of private investment strategies.

Alternative investment assets (also such as rare vintage and collectible items, expensive old high-quality alcohol, discontinued fashion, etc.) are a hit among wealthy investors, even though it is not easy to obtain direct or indirect exposure to diversified art investment(s) in a traditional finance kind of way. However, alternative assets are helpful in portfolio diversification as they last (if stored properly), usually appreciate in value (but sometimes not very predictably), and have a low correlation to traditional assets like stocks, real estate, gold, or fixed-income securities. Although alternative assets are highly illiquid and sometimes very challenging to value correctly, researchers are interested in them. We will closely look at one of the research papers that investigates the role of art in the portfolio, utilizing mean-variance optimization and less-used STL decomposition.

Despite the significant challenges in accurately forecasting market trends and economic conditions over such lengthy periods, this task remains critically important for investors, portfolio managers, and policy makers. They depend on these forecasts to develop effective investment strategies and policy frameworks.