Own-research

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?

We’ve previously examined how central bank policy decisions—particularly those by the Federal Reserve and the European Central Bank (ECB)—impact stock market behavior. The price drift in U.S. equities around the Federal Open Market Committee (FOMC) meetings is a well-documented phenomenon. Likewise, our research study of the ECB revealed a pre-announcement drift, underscoring the anticipatory nature of equity markets ahead of key policy events and the potential opportunities for trading strategies. But are such price drifts unique to the Fed and ECB? In this article, we broaden the scope to investigate whether similar market behavior occurs around monetary policy announcements by other major central banks: mainly the Swiss National Bank (SNB), the Bank of England (BoE), and the Bank of Japan (BoJ).

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.