The End-Of-Month Effect in Value–Growth and Real‑Estate–Equity Spreads

The clustering of excess returns on the final trading days of the month constitutes a robust empirical regularity with significant implications for portfolio construction. We document a month-end premium that is both statistically and economically significant, distinct from the canonical turn-of-the-month (ToM) effect. Our strategy highlights systematic style rotations—particularly shifts in value versus growth exposures, as proxied by the IVE–IVW spread—and documents parallel contemporaneous dislocations between real-estate and broad-equity benchmarks, as measured by the IYR–SPY spread.

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Can Technology Sector Leadership Be Systematically Exploited?

The U.S. equity market has periodically been dominated by a few technology-driven stocks, most recently the so-called “Magnificent Seven.” Historically, similar dominance occurred during the Nifty Fifty era in the 1960s–1970s and the dot-com boom in the 1990s. These periods of concentrated leadership often led to temporary outperformance, but systematically capturing such gains has proven challenging. Our study investigates the potential to exploit technology sector dominance using momentum-based strategies across Fama–French 12 industry portfolios, analyzing whether long-only, long-short, and rolling-basis approaches can generate persistent alpha, and assessing the limitations of simple timing methods.

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Cross-Sectional and Dollar Components of Currency Risk Premia

Currency strategies often appear simple on the surface – go long high-yielding currencies, short low-yielding ones, or take a position on the U.S. dollar. But these trades actually mix two distinct components: a Dollar component, which bets on broad movements of the U.S. dollar against all others, and a Cross-Sectional (CS) component, which exploits relative differences across countries. The question is, which of these components really drives currency risk premia? A new paper by Vahid Rostamkhani tackles this long-standing question by decomposing the predictive power of eleven macroeconomic fundamentals—such as interest rates, inflation, unemployment, and fiscal variables—into these two components across almost a century of data (1926-2023). This approach directly tests whether it is more rewarding to time the dollar itself or to focus on cross-country fundamental spreads.

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How Can We Explain the Low-Risk Anomaly?

The low-risk anomaly in financial markets has puzzled researchers and investors, challenging the traditional risk-return paradigm (higher risk->higher return). This phenomenon, where low-risk assets outperform their high-risk counterparts on a risk-adjusted basis, has been observed across various asset classes, including stocks and mutual funds. What may be the possible explanation? Pass-through mutual funds, which aim to replicate the performance of specific market indices, play a crucial role in this context by channeling investor flows and potentially influencing asset prices through demand pressure.

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Why Most Markets and Styles Have Been Lagging US Equities?

Over the past decade and a half, the US equities have set the hard-to-beat performance benchmark. Nearly all of the other countries, no matter if small or big, emerging or developed, have lagged behind. However, what are the forces behind this outperformance? Why did most of the other markets and even investing styles bow to the US large-cap growth dominance? A new paper written by David Blitz nicely analyses the rise of the behemoth.

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Can We Finally Use ChatGPT as a Quantitative Analyst?

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.

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