Momentum

The goal of our article is to examine the long-term relationship between small value and large growth stocks using more than 100 years of data and test whether the spread between small value and large growth portfolios shows trends that could help investors switch between the two styles. Using the Fama and French 2×3 and 5×5 size and book-to-market portfolios, we construct the small value minus large growth (SV–LG) spread and apply simple trend-following signals based on moving averages and momentum with horizons ranging from 3 to 12 months. Our results show that trend-following strategies are able to capture part of the value outperformance on the long side. Timing periods when growth stocks dominate is much more difficult.

This study examines the effectiveness of several anomaly-based trading strategies applied to the real estate sector represented by the RlEst index from the Fama–French 48 industry portfolios. Using monthly data from July 1, 1926, to December 1, 2025, we analyze whether selected strategies are capable of generating superior risk-adjusted returns compared to both the standalone RlEst index and the broader market represented by the Fama–French 12-industry portfolios. The tested approaches include trend-following strategies based on moving averages, momentum strategies based on the rate of change of the index, and seasonality-based strategies utilizing different look-back periods.

For many investors, short-term interest rates are often treated as something the market “discovers.” In reality, the Federal Reserve has enormous control over how the front end of the yield curve evolves. While textbooks often portray the Fed’s policy rate as a flexible tool that reacts quickly to economic data, the actual behavior of the Federal Open Market Committee (FOMC) looks very different. In practice, monetary policy tends to move in long, persistent cycles. The Fed spends years hiking rates, or years cutting them, and only rarely reverses direction quickly. For anyone trading rates, bonds, or rate-sensitive assets, this persistence matters. It means that the path of short-term interest rates over the next one to two years is often largely shaped by the Fed’s policy trajectory rather than by constantly shifting market expectations.

This observation has an important implication: the short end of the Treasury curve often behaves less like a forecasting market and more like a gradual reflection of the Fed’s policy cycle. When the Fed enters a tightening or easing phase, that trend tends to propagate through Treasury yields from one month out to roughly two years. In this article, we show that these policy-driven trends can be measured and used. By identifying whether the Fed is in a tightening, easing, or neutral phase, investors can improve their expectations about the near-term evolution of the yield curve. For fixed-income portfolio managers and macro traders, recognizing these policy regimes can help sharpen rate forecasts, improve duration positioning, and better manage risks tied to interest-rate movements.

This research examines the critical quantitative investment problem of systematic tactical allocation to international equity mandates—specifically Emerging Markets (EM) and Europe, Australasia, and the Far East (EAFE)—amidst conjectured macroeconomic regime transitions. The investigation is precipitated by observable deteriorations in USD hegemony, elevated geopolitical risk premiums, and protracted macroeconomic uncertainty. These factors collectively challenge the post-Global Financial Crisis paradigm of consistent US equity outperformance, suggesting a potential inflection point in relative returns and currency-adjusted Sharpe ratios.

The central research question is whether a statistically robust, signals-based framework can be engineered to systematically time exposure to EAFE equities, thereby capitalizing on these postulated regime shifts. We move beyond traditional, static mean-variance optimization by developing a dynamic model that integrates momentum variables to generate actionable, out-of-sample allocation signals.

Pragmatic Asset Allocation is a systematic, multi-asset investment strategy designed to adapt dynamically to evolving market conditions. Rather than maintaining a static equity exposure, the model actively allocates capital across a diversified set of asset classes—including equities, bonds, commodities, gold, and cash-like instruments—using momentum-based signals and disciplined periodic rebalancing. The strategy’s primary objective is to deliver attractive long-term returns while materially reducing drawdowns during adverse market environments.

It has now been two highly volatile years since we first published our paper on PAA, making this an opportune moment to review the strategy’s performance over the past year.

This article develops a price-based macro–financial model of gold that formally links its medium-horizon return dynamics to cross-asset risk-premium configurations. Although gold has traditionally been conceptualized as a non-yielding inflation hedge or safe-haven asset, contemporary empirical evidence reveals a substantially more intricate structure: gold’s forward returns are systematically conditioned by the joint momentum of (i) gold itself and (ii) long-duration U.S. Treasury total-return indices. The alignment of these two signals appears to encode macroeconomic information—specifically the direction of real interest rates, the stance and expected trajectory of Federal Reserve policy, and the prevailing global risk-appetite regime.