Letdin, Seagraves, and Sirmans advanced our understanding of REIT asset pricing by developing and rigorously testing six REIT-specific return factors—size, value, momentum, earnings quality, low volatility, and short-term reversal—using decades of data.
A longstanding belief in market finance is that short-term funding markets like repo are relatively stable and transparent. But this new research turns that idea on its head.
This paper reveals a striking pattern in U.S. mortgage markets: minority borrowers are more likely to complete applications, be approved, and avoid default when they interact with minority loan officers.
Handy and Meksi provided a clear warning: relying on past performance to select CTAs is a strategy fraught with risk, largely due to behavioral biases that distort our perception of skill and persistence. For investors, the lesson is to stay humble, diversify, and focus on robust processes rather than chasing yesterday’s winners.
A longstanding belief in household finance is that wealthier people should buy less insurance because they can afford to self-insure. But this new research turns that idea on its head. This analysis shows that wealthier U.S. households actually purchase more life and property insurance - not less.
Simpler structures—like low-dimensional lotteries or intuitive cash flows—can actually encourage investors to take on more risk.
This paper reveals a striking pattern in U.S. stock markets: the prices of individual stocks often reverse direction at the very end of the trading day. Using high-frequency data, the authors find that the last few minutes—particularly the closing auction—are dominated by large institutional flows that cause temporary price pressure. This is followed by a reversal the next day.
Can machine learning models help us exploit stock market anomalies more effectively? This paper says yes—but with a few important caveats. By applying gradient boosting algorithms to a wide array of established anomalies (like value, momentum, and quality), the authors show that machine learning methods can significantly improve the performance of long-short strategies.
This paper explores how value, momentum, low-risk, and size factors explain differences in corporate bond returns across firms and over time.
Younger and less-wealthy individuals are more prone to increasing their exposure to riskier assets in low-interest environments. Investors experiencing losses are more likely to seek higher yields.
The empirical research (for example, here, here, here and here) on insider trading demonstrates that insider transactions have significant predictive power for future stock returns [...]
Over 75% of the cross-sectional variation in P/E ratios is driven by future return differences, not growth expectations. This challenges many common asset pricing models and changes how investors should think about value, growth, and long-term return forecasting.
This article examines and compares, from a digital signal processing (DSP) time domain perspective, several filters that are modeled on the assumption that the input follows a second order process, i.e., the input contains a linear trend. These filters are, by design, better able to track linear trends than some other more commonly-used filters, such as moving average, exponential smoothing, etc., which exhibit lag, or a time delay, in response to trends. Filters modeled on a second order process are commonly referred to in the technical analysis literature as “zero lag” filters.
This study investigates whether firms' divestitures of pollutive assets genuinely contribute to environmental sustainability or merely serve as greenwashing tactics.
Momentum investing remains a viable strategy. However, the way you construct and manage your momentum portfolio matters greatly.
Increased executive effort correlates with positive earnings surprises, higher cumulative abnormal returns post-earnings announcements, and narrower credit default swap spreads. Moreover, portfolios constructed based on changes in executive effort demonstrate significant risk-adjusted returns, underscoring the tangible value of diligent leadership.
The structure of investor syndicates—hierarchical or flat—significantly impacts the flow of information and investment decisions. In hierarchical structures, differentiated incentives can lead to persuasive cascades, while flat structures promote truthful information sharing.
Christian Goulding and Campbell Harvey, authors of the study "Investment Base Pairs," proposed a groundbreaking framework for portfolio construction that challenges traditional approaches in modern finance. Their research focused on leveraging cross-asset information to optimize investment strategies and improve returns across diverse asset classes. Here's an overview of their investigation, key findings, and takeaways for investors and advisors.
The study examines how households adjust their labor supply in response to changes in mortgage payments due to fluctuating interest rates.
In the evolving landscape of financial technology, innovative methods are emerging to assess creditworthiness. One such approach involves analyzing borrowers' facial expressions during loan applications to predict delinquency risk. This study explores this novel intersection of psychology, machine learning, and finance.
© Copyright 2025 alpha architect | All Rights Reserved | Home | Terms of Use | Privacy Policy | Disclosures | Subscribe | Contact Us