1. Market Regime Snapshot

VIX Term Structure

Tenor Level vs. Spot
VIX Spot 16.0
VIX 3M 20.5 +4.6 (Contango)

Realized vs. Implied Vol: SPY 20D RV = 12.7%, VIX = 16.0. Spread = -3.3pp (realized vol below implied — calmer than feared).

Cross-Asset Momentum (1-Month)

Asset 1M Return Signal
SPY (Equity) +0.3% 📈 Positive
TLT (Bonds) -3.8% 📉 Negative
GLD (Gold) -1.7% 📉 Negative
UUP (US Dollar) -0.6% 📉 Negative

Regime: Mixed/transitional regime

Last updated: 2026-08-02 17:13 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 -2.22% -8.69% +5.61% +0.46% 2.74% -0.98
Value -1.80% -2.22% +14.81% +1.04% 2.56% -1.11
Quality +1.16% +0.21% +6.01% +0.35% 1.53% +0.53
Low Volatility +1.01% +1.07% +3.80% +0.13% 1.13% +0.78
Size -1.09% -2.90% +0.78% +0.23% 1.56% -0.84

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

Last updated: 2026-08-02 17:13 UTC


3. Macro Signal Tracker

Indicator Current 1W Change Signal
HY Spread (OAS) 284bps +7bps ⚠️ Widening
IG Spread (OAS) 80bps +1bps ⚠️ Widening
2s10s Yield Curve 0.47% +0.11% Normal
3M10Y Yield Curve 0.92% +0.19% Normal
Fed Funds Rate 3.63% N/A
HY − IG Spread 204bps Risk sentiment proxy

Macro Summary: Neutral macro backdrop

Last updated: 2026-08-02 17:13 UTC


4. Quant Research Digest

Three papers I found worth reading this week:

Train Often, Deploy Selectively: Forward-Gated Model Replacement in Crypto Markets — Aditya Dutta arXiv

This paper outlines a deployment policy where a challenger forecasting model is evaluated off the serving path against delayed labels before replacing an incumbent. It highlights a critical MLOps reality: continuous retraining does not automatically yield out-of-sample improvements over a stable baseline.

Optimal Execution with Passive Market Impact — Alexander Barzykin et al. arXiv

The authors derive an execution model using limit orders based on empirical fill probabilities and the linear response of price changes to order flow imbalance. It is a highly grounded approach to microstructure modeling that relies on observable limit order book mechanics rather than purely theoretical assumptions.

FinSMART: Financial Sentiment Analysis for Algorithmic Trading through Market-Aligned Reinforcement Learning — Giorgos Iacovides et al. arXiv

This research moves beyond static, supervised training for LLMs in sentiment analysis by using reinforcement learning to align models directly with dynamic market conditions. It marks an important structural shift from static dictionary-based sentiment scoring toward adaptive models that evolve with new financial data.

Last updated: 2026-08-02 17:13 UTC


5. Stat of the Week

Stat Value Context
CBOE Skew Index 141 Elevated tail risk (>130)

The Skew Index remains elevated at 141, while 20-day realized volatility sits low at 12.7% and the VIX is at 16.0. This continued divergence mathematically indicates that while daily index movements are subdued, the options market is persistently pricing in left-tail risk. It is a clear observation that standard mean-variance metrics, which rely heavily on average volatility, are currently obscuring the true tail behavior of the distribution.

Last updated: 2026-08-02 17:13 UTC


Generated: 2026-08-02 17:13 UTC