Integrating Fundamental Metrics Into Pairs Trading
The paper proposes a novel pairs‑trading framework that blends fundamental metrics—such as ROE, sales growth, leverage, geographic proximity, and industry alignment—with traditional statistical measures. Each factor receives a regression‑derived weight, forming a composite score for pair selection. Back‑testing shows the multi‑factor model delivers higher risk‑adjusted returns than the classic Gatev et al. (2006) approach, especially during volatile periods, though it still lags passive index performance and faces out‑of‑sample challenges. The study highlights the potential of fundamental integration in statistical arbitrage.
Do Options Exhibit Momentum?
Recent academic papers reveal that options exhibit robust momentum effects across both monthly and intraday horizons. A 2022 study of delta‑neutral straddles finds that options with strong 6‑36‑month past returns generate superior subsequent returns, with lower risk than traditional short...
Extreme VIX: Regime Shifts and Return Predictability
The episode examines research on extreme VIX spikes (VIX > 45) and their predictive power for equity returns. Using U.S. data from 2008‑2025, the authors find that such spikes generate significant positive returns over a three‑month horizon, offering a contrarian signal, while...
Pairs Trading Using the Hurst Exponent of Product
The episode explores a novel pairs‑trading technique that uses the Hurst exponent of the product (HP) to gauge the co‑movement of two asset price series. It explains how HP differentiates between low, moderate, and high correlation—values near 0.5 indicate weak...
Volume Effects in Pairs Trading Performance
The episode examines a recent study that integrates trading volume into pairs‑trading strategies for S&P 500 stocks, using cointegration over data from 2005‑2024. The key finding is that high‑volume pairs consistently outperform low‑volume pairs across return, risk, and convergence metrics, and...
State-Dependent Correlation Between the S&P 500 and the VIX
The episode examines how the correlation between the S&P 500 and the VIX is not static but varies across four distinct market regimes defined by levels of volatility (VOL) and volatility‑of‑volatility (VOV) risk. The authors propose a regime‑switching model that shows...
Multifractality and Its Underlying Drivers in Cryptocurrency Markets
The episode delves into a recent study examining multifractality in major cryptocurrency markets, revealing that the complex scaling behavior of assets like Bitcoin, Ethereum, DEX tokens, and NFTs is driven chiefly by long-range temporal correlations rather than merely heavy‑tailed return...
Herding in Commodities and Cryptocurrencies
This episode explores herding behavior beyond equities, focusing on cryptocurrency markets during geopolitical shocks and commodity ETFs across different asset classes and time scales. Recent research shows strong, asymmetric herding in crypto—especially in bearish periods and when perceived geopolitical risk...
Volatility Feedback Loop in the VIX Index and Its Derivatives
The episode delves into the dynamics between the VIX spot index, VIX futures, and the implied volatility of VIX options, highlighting a unidirectional causality chain where spot VIX leads futures, which in turn lead option volatility. High‑frequency analysis shows that...