Most platforms now intermediate—pooling loans into short-dated portfolios and, increasingly, offering bank-like products that absorb liquidity risk. Why did credit marketplaces evolve away from pure peer-to-peer? This paper quantifies the welfare value of those design choices.
Equity duration has increased dramatically. As firms reinvest more and delay payouts to the future, asset prices become more sensitive to changes in expected returns rather than fundamentals.
This paper rethinks how financial regulators should design stress tests. Rather than treating stress testing as a pass/fail assessment, the authors show it should be viewed as an exercise in information gathering.
Rather than streamlining oversight, overlapping mandates between regulatory agencies create confusion, redundancy, and sometimes outright inconsistency.
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.
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.
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 study investigates whether firms' divestitures of pollutive assets genuinely contribute to environmental sustainability or merely serve as greenwashing tactics.
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.
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.
This article explores how researchers forecast market returns by aggregating expected returns from individual stocks.
This article explores how researchers forecast market returns by aggregating expected returns from individual stocks.
© Copyright 2025 alpha architect | All Rights Reserved | Home | Terms of Use | Privacy Policy | Disclosures | Subscribe | Contact Us