Inflation significantly impacts the prices of gold and treasury bonds through various mechanisms. Gold is often viewed as a hedge against inflation, while treasury bonds exhibit a more complex relationship influenced by interest rates and investor behavior. This relationship between inflation, gold, and treasuries is well understood, but the real question is whether we can systematically capitalize on it. In this article, we explore how inflation data can be used to build trading strategies—and as our findings suggest, the answer is a definite yes.

For decades, investors and analysts have relied on traditional industry classifications like GICS, NAICS, or SIC to group companies into sectors and peer groups. However, these rigid categorizations often fail to capture the evolving nature of businesses, especially in an era of technological convergence and rapid industry shifts. Machine learning (ML) offers a more dynamic and data-driven alternative by analyzing company visuals—such as logos, product images, and branding elements—to identify similarities that go beyond predefined classifications. A recent study applies this approach to construct new industry groupings and tests them in industry momentum and reversal. The results show that ML-generated groups lead to superior performance, once again highlighting the potential of image-based classification in financial analysis.

Can we truly rely on the opening price in OHLC data for backtesting? While the overnight drift effect is well-documented in equities, we investigated its presence in gold using the GLD ETF and then extended our analysis to the GDX – Gold Miners ETF, where we observed an unusually strong overnight return exceeding 30% annualized. However, when we tested execution at 9:31 AM using 1-minute data, the anomaly diminished significantly, suggesting that the extreme return was partially a data artifact. This finding highlights the risks of blindly trusting OHLC open prices and underscores the need for higher-frequency data to validate execution assumptions.

Building upon the established research on market seasonality and the potential for front-running to boost associated profits, this article investigates the application of seasonal strategies within the context of crisis hedge portfolios. Unlike traditional asset allocation strategies that may falter during market stress, crisis hedge portfolios are designed to provide downside protection. We examine whether incorporating seasonal timing into these portfolios can enhance their performance and return-to-risk ratios, potentially offering superior risk-adjusted returns compared to static or non-seasonal approaches.

The growing urgency of climate change has increased scrutiny of companies’ ESG (Environmental, Social, and Governance) practices. Investors are now more inclined to support firms that demonstrate strong ESG commitments, often willing to pay a green premium for sustainable investments. However, is the spread in performance between the ‘Sin’ and ‘Saint’ stocks driven by the ESG factor or some other omitted variable? The recent study by Zhan Shi and Shaojun Zhang suggests that the hidden force that may be in play is the price of the oil.

Can we simplify the complexities of the stock market and distill them into a simple set of quantifiable metrics? A lot of academic papers suggest this, and they offer formulas that should make the life of a stock picker easier. Some of the most compelling methodologies within this realm are the F-Score, Magic Formula, Acquirer’s Multiple, and the Conservative Formula. These quantitative strategies are designed to identify undervalued stocks with robust fundamentals and potential for high returns. But do they really work out-of-sample? A new paper by Marcel Schwartz and Matthias X. Hanauer tries to answer this interesting question…