Own-research

Understanding how Bitcoin-related instruments can fit into traditional portfolios is increasingly relevant for investors. Some risk-averse investors do not like to hold cryptocurrencies in their portfolios strategically; however, they may be open to investing in crypto-linked assets on a tactical level. In this context, our goal is to explore how we can provide short-term Bitcoin exposure while contributing to overall portfolio balance and potential downside protection.

Financial markets exhibit persistent calendar anomalies, which often defy the efficient‐market hypothesis by generating predictable return patterns tied to institutional or cultural events. In this paper, we document a novel, globally pervasive drift in gold prices surrounding major wealth-oriented festivals across the four principal cultural and religious domains: Christianity, Islam, Hinduism, and East Asian syncretic traditions. While each community endows its principal holidays with gift‐giving rituals and conspicuous displays of wealth, the sole differentiator among regions is the precise timing of these festivities on the Gregorian calendar.

Our central thesis is that gold, owing to its dual role as a universal wealth reservoir and socio-cultural status symbol, experiences concentrated, holiday-induced buying pressure that yields persistent and economically material drift in the GLD ETF. By quantifying this effect across four distinct cultural calendars, we introduce a previously undocumented demand-side factor into commodity-pricing models.

When it comes to forecasting commodity prices, traders usually turn to weather patterns, supply-demand data, or economic indicators—but what if the sun itself could offer a clue? Our latest data analysis explores a surprising relationship: periods of high solar activity, measured by an increased number of sunspots, tend to precede lower long-term prices for agricultural staples like wheat and corn. The science behind it is simple—more sunspots often mean better growing conditions, which can boost crop yields and eventually put downward pressure on prices. It’s not a quick trade idea; the effects unfold over one to three years, as natural cycles gradually outweigh short-term noise from market speculation or temporary supply shocks. Unconventional? Yes. But in a market where every edge matters, even the sun might have something to say.

Corporate conferences have long been recognized as pivotal events in financial markets, serving as catalysts that signal upcoming innovations and strategic shifts. Scheduled corporate events induce market reactions that can be systematically analyzed to reveal predictable return patterns. In this work, we focus on examining the return drift exhibited by technology stocks in the days surrounding their respective conferences, employing simple quantitative methods with daily price data.

The hypothesized return drift is premised on the notion that investor sentiment and market dynamics are significantly altered by the information disseminated at these conferences. Investors, reacting to both anticipatory signals and post-announcement adjustments, tend to drive prices in a measurable manner in the windows immediately preceding, during, and after the events. By systematically analyzing stocks of companies such as Apple, Google, and Microsoft, this study aims to validate the existence of these drift patterns and shed light on the underlying mechanisms, thereby enhancing mutual understanding of event-driven asset pricing dynamics.

When using machine learning to forecast global equity returns, it’s tempting to focus on the raw prediction—whether some stock market is expected to go up or down. But our research shows that the real value lies elsewhere. What matters most isn’t the level or direction of the machine learning model’s forecast but how much it differs from a simple, price-based benchmark—such as a naive moving average signal. When that gap is wide, it often reveals hidden mispricings. In other words, it’s not about whether the ML model predicts positive or negative returns but whether its view disagrees sharply with what a basic trend-following model would suggest. Those moments of disagreement offer the most compelling opportunities for tactical country allocation.

Valuation models for equity indexes are essential tools for investors seeking to assess long-term market conditions. Traditional models like the CAPE ratio, introduced by Robert J. Shiller, or the Buffett Indicator often rely on macroeconomic variables such as corporate earnings or GDP. While informative, these models can be complex and dependent on data that may be revised or vary across regions. In this article, we introduce a simpler alternative: a valuation ratio based solely on the inflation-adjusted total return of the index, offering a streamlined and transparent approach to index valuation. Finally, our goal would be to answer the question from the title – Are the indexes fairly priced at the moment?