Asset class picking

Gold has been in the headlines lately as it climbs to new highs, prompting many investors to look for ways to benefit from the rally. However, many institutional investors – such as mutual funds and pension funds – face restrictions on buying physical gold or gold-backed ETFs. Instead, they often turn to gold mining stocks to gain indirect exposure to gold’s price. That approach seems logical on the surface: mining stocks typically offer leveraged exposure to gold’s movements. But as highlighted by Dirk G. Baur, Allan Trench, and Lichoo Tay in their recent study “Gold Shares Underperform Gold Bullion”, this strategy can be misleading. The authors demonstrate that, over the long run, gold mining shares structurally underperform physical gold itself.

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.

In the realm of quantitative finance, understanding the dynamics of real estate returns over extended periods is often overlooked, which is not good, as real estate constitutes a significant portion of investors’ portfolios. The article titled Global Housing Returns, Discount Rates, and the Emergence of the Safe Asset, 1465-2024 fills the gap and provides a comprehensive historical overview of real estate yields, offering a chronological overview of real estate returns not just over a few decades but over several centuries.

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.

In two of our previous articles, we explored the idea of using artificial intelligence to backtest trading strategies. Since then, AI has continued to develop, with tools like ChatGPT evolving from simple Q&A assistants into more complex tools that may aid in developing and testing investment strategies—at least, according to some of the more optimistic voices in the field. Over a year has passed since our first experiments, and with all the current hype around the usefulness of large language models (LLMs), we believe it’s the right time to critically revisit this topic. Therefore, our goal is to evaluate how well today’s AI models can perform as quasi-junior quantitative analysts—highlighting not only the promising use cases but also the limitations that still remain.