Inflation is one of the key macroeconomic forces shaping financial markets, influencing asset prices across the board. In our previous analysis, we examined how gold and Treasury prices react to changes in the inflation rate, uncovering patterns that suggested inflation dynamics also impact the US dollar. In this follow-up, we shift our focus entirely to the dollar, analyzing how it responds to both accelerating and decelerating inflation. As the world’s reserve currency, the dollar’s movements have far-reaching implications, affecting global trade, monetary policy, and asset allocation. Our goal is to determine whether inflation serves as a clear driver of dollar performance and, if so, in what ways.

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Navigating the financial markets requires a keen understanding of risk sentiment, and one often-overlooked dataset that provides valuable insights is FINRA’s margin debt statistics. Reported monthly, these figures track the total debit balances in customers’ securities margin accounts—a key proxy for speculative activity in the market. Since margin accounts are heavily used for leveraged trades, shifts in margin debt levels can signal changes in overall risk appetite. Our research explores how this dataset can be leveraged as a market timing tool for US stock indexes, enhancing traditional trend-following strategies that rely solely on price action. Given the current uncertainty surrounding Trump’s presidency, margin debt data could serve as a warning system, helping investors distinguish between market corrections and deeper bear markets.

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.