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Alternative Market Signals: Investing with the Box Manufacturing Index

December 2, 2025

Investors are increasingly exploring alternative indicators to gain an edge in financial markets. Traditional signals, such as earnings reports or macroeconomic data, often come with delays or may already be priced in. As a result, unconventional metrics have attracted attention. In this article, we examine the Producer Price Index (PPI) for the Corrugated and Solid Fiber Box Manufacturing industry, including corrugated boxes and pallets. Our motivation is to evaluate this index’s effectiveness as a predictive signal for the S&P 500 ETF, sector-specific ETFs, and individual stocks such as Amazon (AMZN), one of the largest consumers of materials tracked by this index. We present several investment strategies that incorporate this indicator and assess whether it can enhance risk-adjusted returns.

Cryptocurrency as an Investable Asset Class – 10 Lessons

October 24, 2025

Cryptocurrencies have matured from experimental curiosities into a viable investable asset class whose return-generation and risk characteristics merit treatment within empirical asset pricing. A recent paper by Nicola Borri, Yukun Liu, Aleh Tsyvinski, Xi Wu summarizes ten facts from the literature that show cryptocurrencies share important similarities with traditional markets—comparable risk-adjusted performance and a small set of cross-sectional factors—while retaining distinctive features such as frequent large jumps and price signals embedded in blockchain data. Key themes include portfolio diversification, factor structure, market microstructure, and the evolving role of regulation and derivatives in shaping market discovery and stability.

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

October 20, 2025

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.

Can Technology Sector Leadership Be Systematically Exploited?

October 16, 2025

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.

Cross-Sectional and Dollar Components of Currency Risk Premia

September 26, 2025

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.

How Can We Explain the Low-Risk Anomaly?

August 28, 2025

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.

Why Most Markets and Styles Have Been Lagging US Equities?

June 18, 2025

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.

Can We Finally Use ChatGPT as a Quantitative Analyst?

May 30, 2025

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.

Quantpedia Awards 2025 – Winners Announcement

May 27, 2025

This is the moment we all have been waiting for, and today, we would like to acknowledge the accomplishments of the researchers behind innovative studies in quantitative trading. So, what do the top five look like, and what will the authors of the papers receive?

Let’s find out …

Is Machine Learning Better in Prediction of Direction or Value?

May 21, 2025

Building machine learning models for trading is full of nuances, and one important but often overlooked question is: what exactly should we try to predict—the direction of the next market move or the actual value of the asset’s return? A recent paper by Cheng, Shang, and Zhao, titled “Direction is More Important than Speed” offers a clear and practical answer. Their research shows that focusing on direction—simply whether returns will be positive or negative—leads to better model accuracy and, more importantly, stronger real-world investment performance. This is especially true when using machine learning methods, where predicting the direction allows models to better capture downside risks and build more effective trading strategies. For anyone using ML in finance, this paper makes a strong case that predicting where the market is headed is often more valuable than predicting how far it will go.