1. Market Regime Snapshot

VIX Term Structure

Tenor Level vs. Spot
VIX Spot 15.8 —
VIX 3M 20.5 +4.7 (Contango)

Realized vs. Implied Vol: SPY 20D RV = 8.7%, VIX = 15.8. Spread = -7.1pp (realized vol below implied — calmer than feared).

Cross-Asset Momentum (1-Month)

Asset 1M Return Signal
SPY (Equity) -1.7% 📉 Negative
TLT (Bonds) -1.7% 📉 Negative
GLD (Gold) -0.0% 📉 Negative
UUP (US Dollar) -0.4% 📉 Negative

Regime: Mixed/transitional regime

Last updated: 2026-09-13 21:55 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 +2.54% -2.44% -3.65% +0.41% 2.80% +0.76
Value -0.33% +1.46% +3.20% +1.06% 2.58% -0.54
Quality -1.46% -2.33% +2.17% +0.31% 1.55% -1.14
Low Volatility -2.16% -1.15% +4.48% +0.15% 1.18% -1.95
Size -1.00% -3.53% -4.06% +0.10% 1.51% -0.72

No factor stress signals this week (all within ±2σ).

Last updated: 2026-09-13 21:55 UTC

3. Macro Signal Tracker

Indicator Current 1W Change Signal
HY Spread (OAS) 270bps +5bps ⚠️ Widening
IG Spread (OAS) 80bps -1bps ✅ Tightening
2s10s Yield Curve 0.33% -0.10% Normal
3M10Y Yield Curve 0.89% +0.01% Normal
Fed Funds Rate 3.63% N/A →
HY − IG Spread 190bps — Risk sentiment proxy

Macro Summary: Neutral macro backdrop

Last updated: 2026-09-13 21:55 UTC

4. Quant Research Digest

Three papers I found worth reading this week:

Exact calibration of structural models via time-change — Argimiro Arratia et al. arXiv

This paper presents a method to exactly calibrate structural credit models to market survival curves by applying a deterministic time-change clock to a latent first-passage-time process. It demonstrates that models like AT1P can be mathematically formulated as a time-changed drifted Brownian motion, turning a complex calibration problem into a simple, fast function inversion.

Seasonal Trading in Commodity Futures: Evidence from Regression and Singular Spectrum Signals — Ralph Kosch and arXiv

This study evaluates calendar regressions against Singular Spectrum Analysis (SSA) to see if commodity futures seasonality actually survives transaction costs and out-of-sample rolling. It acts as a great reality check for backtesting, demonstrating that while flexible models can detect complex seasonal patterns, translating them into statistically robust outperformance over a simple long benchmark is incredibly difficult.

Optimized day trading via reinforcement learning and technical analysis using attention-LSTM, CNN, and explainable state modeling — Muktinath Vishwakarma et al Paper

The authors build an intraday Q-learning agent that fuses Attention-LSTMs and CNNs to capture temporal and spatial dependencies, and then uses K-means clustering to discretize those high-dimensional features into manageable states. It bridges the gap between deep representation learning and traditional reinforcement learning, offering a highly practical architecture for building adaptive trading pipelines without suffering from the curse of dimensionality.

Last updated: 2026-09-13 21:55 UTC

5. Stat of the Week

Auto-computed candidates — pick one and add your commentary below.

Stat Value Context
CBOE Skew Index 154 Elevated tail risk (>130)
Momentum / Low Vol 20D correlation -0.25 No crowding signal (< 0.6)

The Skew Index climbed to 154 this week, while the 20-day realized volatility for SPY stayed low at 8.7%. It is an interesting divergence where daily index returns are relatively flat, but out-of-the-money option pricing remains elevated. This is a good data point validating why we need to track tail-risk metrics separately from standard historical variance when evaluating market environments.

Last updated: 2026-09-13 21:55 UTC

Generated: 2026-09-13 21:55 UTC