The Weekly Print
1. Market Regime Snapshot
VIX Term Structure
| Tenor | Level | vs. Spot |
|---|---|---|
| VIX Spot | 19.5 | β |
| VIX 3M | 20.5 | +1.0 (Contango) |
Realized vs. Implied Vol: SPY 20D RV = 9.8%, VIX = 19.5. Spread = -9.7pp (realized vol below implied β calmer than feared).
Cross-Asset Momentum (1-Month)
| Asset | 1M Return | Signal |
|---|---|---|
| SPY (Equity) | -1.5% | π Negative |
| TLT (Bonds) | +3.0% | π Positive |
| GLD (Gold) | -6.8% | π Negative |
| UUP (US Dollar) | +1.2% | π Positive |
Regime: Mixed/transitional regime
Last updated: 2026-10-25 19:49 UTC
2. Factor Performance Dashboard
Source: ETF Proxies (MTUM, VLUE, QUAL, USMV, IWM vs SPY)
Note: ETF proxy returns include market beta and are not directly comparable to factor-neutral French library returns.
| Factor | Weekly | 1M | 3M | Mean (52W wkly) | Std (52W wkly) | Z |
|---|---|---|---|---|---|---|
| Momentum | -1.27% | -8.82% | -4.07% | +0.33% | 3.07% | -0.52 |
| Value | +0.49% | -3.62% | -0.63% | +0.86% | 2.56% | -0.14 |
| Quality | -0.94% | +0.12% | +2.78% | +0.28% | 1.49% | -0.82 |
| Low Volatility | +1.67% | +4.79% | +5.88% | +0.19% | 1.23% | +1.20 |
| Size | +2.39% | -1.17% | -8.44% | +0.01% | 1.60% | +1.49 |
No factor stress signals this week (all within Β±2Ο).
Last updated: 2026-10-25 19:49 UTC
3. Macro Signal Tracker
| Indicator | Current | 1W Change | Signal |
|---|---|---|---|
| HY Spread (OAS) | 340bps | +17bps | β οΈ Widening |
| IG Spread (OAS) | 89bps | +5bps | β οΈ Widening |
| 2s10s Yield Curve | 0.31% | -0.04% | Normal |
| 3M10Y Yield Curve | 0.78% | -0.05% | Normal |
| Fed Funds Rate | 3.75% | N/A | β |
| HY β IG Spread | 251bps | β | Risk sentiment proxy |
Macro Summary: Neutral macro backdrop
Last updated: 2026-10-25 19:49 UTC
4. Quant Research Digest
Three papers I found worth reading this week:
Lineage-Aware Search Control for LLM-Based Evolutionary Factor Mining β Xintong Liu et al. arXiv
This framework introduces a search control mechanism that actively tracks the genetic lineage of symbolic alpha factors generated by LLMs acting as evolutionary operators. By forcing the algorithm to recognize semantic ancestry, it prevents the LLM from getting trapped in local optima and generating redundant math variations, solving a major hurdle in automated factor discovery pipelines.
Dynamics of the Liquidity Profile in Concentrated Liquidity Provision: An SPIDE Approach β Cheuk Yin Lee et arXiv
The authors model the aggregate liquidity profile in concentrated automated market makers (like Uniswap V3) using a stochastic partial integro-differential equation to capture provider repositioning. It offers a rigorous mathematical foundation for how liquidity clusters and diffuses around the active price, which is critical for backtesting execution algorithms in DeFi liquidity pools.
Equilibria and Learning in Competitive Market Making β Matteo Onger et al. arXiv
This paper formulates a competitive market-making game in a continuous quote space among strategic agents, addressing the non-convexities that usually break standard continuous existence proofs. It directly tackles the instability and convergence issues found when training multi-agent reinforcement learning models in order-driven limit order books.
Last updated: 2026-10-25 19:49 UTC
5. Stat of the Week
| Stat | Value | Context |
|---|---|---|
| CBOE Skew Index | 152 | Elevated tail risk (>130) |
The CBOE Skew Index hit 152 this week, and high-yield spreads widened to 340bps, yet the 20-day realized volatility on SPY is sitting at a very quiet 9.8%. This divergence is a textbook reminder that simple historical variance doesnβt capture the whole picture. Options and credit markets are heavily pricing in asymmetric downside risk, which shows exactly why we need dedicated tail-risk features in our models rather than just assuming normal distributions based on recent calm price action.
Last updated: 2026-10-25 19:49 UTC
Generated: 2026-10-25 19:49 UTC