---
title: Quant Letter No. 137: October 2026, Week 5
url: https://www.ml-quant.com/issues/2026-10-30/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-10-30
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
issue_date: 2026-10-30
---

# Quant Letter: October 2026, Week 5: Weekly quantitative finance newsletter

*This week centers on AI agent fragility in financial markets, real-time benchmark design preventing memorization, and cryptographic signals predicting equities. AI Equity Research Without Future Data reveals forecast accuracy well below overall performance; Fragility Of Synchronized Agentic Wealth Management quantifies market destabilization from concentrated AI decisions; and Weekend Crypto Volatility Forecasts Markets demonstrates novel cross-asset predictability.*

## Top picks

### 1. [AI Equity Research Without Future Data](https://arxiv.org/abs/2610.38342) · arXiv

### 2. [AI Monoculture Paradox in Markets](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7664018) · SSRN

### 3. [150 Years of US Inflation Expectations](https://econpapers.repec.org/RePEc:zbw:qucehw:344109) · RePEc

### 4. [US Monetary Policy Spillovers in Commercial Real Estate](https://econpapers.repec.org/RePEc:dnb:dnbwpp:872) · RePEc

### 5. [Fragility Of Synchronized Agentic Wealth Management](https://arxiv.org/abs/2610.37705) · arXiv

## What's rising

### Alternative data: 9 papers this week, 3.8× the usual 2.4

### Climate and ESG: 60 papers this week, 1.4× the usual 41.8

### Graph neural networks: 9 papers this week, 2.2× the usual 4.1

## GitHub radar

### Quant repos rising

#### [goldmansachs/gs-quant](https://github.com/goldmansachs/gs-quant): Python toolkit for quantitative finance, derivatives and risk management. (5 quants, 13.8k stars, +504 this week)

#### [Jakeschincariol/day-trader-skill](https://github.com/Jakeschincariol/day-trader-skill): Eleven Claude skills for day trading with backtesting and scanning. (4 quants, 3,464 stars, +2,568 this week, new repo)

#### [QuantMLResearch/quant-handbook](https://github.com/QuantMLResearch/quant-handbook): Quantitative research handbook. (4 quants, 162 stars, +163 this week, new repo)

#### [simonro/Edge-GEX-Lab](https://github.com/simonro/Edge-GEX-Lab): Local live map of option gamma exposure for indices and stocks. (3 quants, 51 stars, +50 this week, new repo)

#### [Jakeschincariol/founder-skill](https://github.com/Jakeschincariol/founder-skill): Eleven Claude skills testing business viability before launch. (2 quants, 2,217 stars, +653 this week, new repo)

#### [nautechsystems/nautilus\_trader](https://github.com/nautechsystems/nautilus_trader): Production-grade Rust trading engine for algorithmic and crypto trading. (2 quants, 31.1k stars, +410 this week)

#### [gloom-sh/gloomberb](https://github.com/gloom-sh/gloomberb): Finance terminal for market data, portfolio tracking and stocks. (2 quants, 2,577 stars, +65 this week)

#### [Hitheshkaranth/OpenTerminalUI](https://github.com/Hitheshkaranth/OpenTerminalUI): Trading terminal UI for market data, charting and backtesting. (2 quants, 376 stars, +19 this week)

### What quants are playing with

#### [Zagitta/gpu-unlock](https://github.com/Zagitta/gpu-unlock): Unlocks higher tensor throughput on GeForce GPUs for AI workloads. (11 quants, 13.4k stars, +12.6k this week, new repo)

#### [togg53192-cmd/jailbreaks](https://github.com/togg53192-cmd/jailbreaks): List of jailbreaks. (10 quants, 10.1k stars, +6,563 this week)

#### [fabio-rovai/open-ontologies](https://github.com/fabio-rovai/open-ontologies): Plans, applies and rolls back changes to production ontologies with audit proofs. (8 quants, 2,213 stars, +898 this week)

#### [JetBrains/thinkrail](https://github.com/JetBrains/thinkrail): Lightweight IDE for AI coding agents with customizable skills. (8 quants, 6,387 stars, +5,359 this week)

#### [pollen-robotics/microduck](https://github.com/pollen-robotics/microduck): Tiny biped duck robot. (8 quants, 13.1k stars, +2,596 this week)

#### [elayadesign/ai-design-skills](https://github.com/elayadesign/ai-design-skills): No description available. (8 quants, 12.7k stars, +9,915 this week)

## We called it

### [Fourier–Laplace transforms in polynomial Ornstein–Uhlenbeck volatility models](https://arxiv.org/abs/2405.02170): now published in Finance and Stochastics (featured 8 May 2024, 5 days after release; 8 citations)

### [HybridRAG: Integrating Knowledge Graphs and Vector Retrieval Augmented Generation for Efficient Information Extraction](https://arxiv.org/abs/2408.04948): passed 250 citations (featured 15 Aug 2024, 6 days after release)

### [Sea Change in Software Development: Economic and Productivity Analysis of the AI-Powered Developer Lifecycle](https://arxiv.org/abs/2306.15033): passed 50 citations (featured 28 Jun 2023, 2 days after release)

## arXiv

__[AI Equity Research Without Future Data](https://arxiv.org/abs/2610.38342)__: Investor's Last Exam benchmarks AI agents on frozen financial databases, finding leading models score 68.1 overall yet only 52.5 on forecast accuracy. (2026-10-30, fanfare: 4)

![Scatter plot showing forecast errors for Tesla across three AI models relative to zero-credit limits.](https://www.ml-quant.com/issues/2026-10-30/figures/arxiv-01-340d8a75.png)

__[Fragility Of Synchronized Agentic Wealth Management](https://arxiv.org/abs/2610.37705)__: An agent-based model shows conversational AI wealth managers concentrate decisions into about two independent views, making markets 1.47 to 1.90 times more fragile than robo-advice. (2026-10-30, fanfare: 3)

![The agentic wealth-management layer](https://www.ml-quant.com/issues/2026-10-30/figures/arxiv-02-27f15c39.png)

__[Knowledge-Graph-Enhanced Financial LLMs](https://arxiv.org/abs/2610.36083)__: FinLLM-KG injects SEC filing facts into sentiment analysis at inference time, improving portfolio performance through explainable company-level context without retraining. (2026-10-28, fanfare: 3)

![Cumulative returns of sentiment-based 35% long–short portfolios under three conditions: Baseline (article text only),](https://www.ml-quant.com/issues/2026-10-30/figures/arxiv-03-9d434765.png)

__[LiveSearch: A Live Benchmark For Financial Agents](https://arxiv.org/abs/2610.37166)__: A live benchmark stores financial questions as executable code so answers update daily against real data, preventing memorization and revealing that 70% of daily queries change their correct answer. (2026-10-28, fanfare: 3)

![Sankey diagram showing how 109 questions change across evaluation dates, tracking periods, raw values, and gold answers.](https://www.ml-quant.com/issues/2026-10-30/figures/arxiv-04-aa288a79.png)

__[Weekend Crypto Volatility Forecasts Markets](https://arxiv.org/abs/2610.35702)__: Weekend cryptocurrency volatility predicts Monday variance in equities, commodities and bonds 11-22% better post-2024, following US Bitcoin ETF approval in late 2023. (2026-10-27, fanfare: 3)

![S&P 500 Monday volatility forecasts: realized versus HAR models with weekend data](https://www.ml-quant.com/issues/2026-10-30/figures/arxiv-05-e2967390.png)

__[DeFi Yield Trading Derivatives And Tail Risk](https://arxiv.org/abs/2610.34930)__: A large-scale Pendle dataset spanning 340 markets reveals stablecoin flight-to-quality dynamics, and a tail-risk loss function improves yield forecasting and enables profitable derivatives strategies. (2026-10-27, fanfare: 3)

![Stacked area chart showing Pendle yield trading market evolution from 2024-2026 with marked events.](https://www.ml-quant.com/issues/2026-10-30/figures/arxiv-06-5cfbefd4.png)

__[Implied Fee Rates in AMM Liquidity Pools](https://arxiv.org/abs/2610.36392)__: A no-arbitrage framework recovers implied fee rates for liquidity providers in Uniswap pools, revealing time-varying and maturity-dependent compensation that varies significantly with pool design. (2026-10-28, fanfare: 3)

![Implied fee-rate surface for ETH-USDC showing variation by moneyness and time to maturity](https://www.ml-quant.com/issues/2026-10-30/figures/arxiv-07-1dc7f2cb.png)

__[Language Models in Healthcare Equity Rankings](https://arxiv.org/abs/2610.38056)__: Language models improve one-year stock rankings for healthcare firms by 48%, especially when scientific texts cover trials or regulatory records, but contribute less at other horizons. (2026-10-30, fanfare: 3)

![Bar chart showing ranking IC across horizons (1m, 3m, 12m) with multiple model variants](https://www.ml-quant.com/issues/2026-10-30/figures/arxiv-08-f5260d44.png)

__[Systemic Risk Networks Among European Banks](https://arxiv.org/abs/2610.37487)__: Using recursive network analysis of equity returns, the research identifies distinct transmitting and receiving roles among European banks and constructs measures of systemic impact and vulnerability. (2026-10-28, fanfare: 3)

![Systemic risk quadrant map: banks plotted by impact score versus vulnerability score, sized by market cap.](https://www.ml-quant.com/issues/2026-10-30/figures/arxiv-09-aa310dcc.png)

__[Political Pressure on Fed Moves Interest Rate Expectations](https://arxiv.org/abs/2610.32300)__: Analysis of four decades of news finds dovish political pressure lowers near-term rate expectations by roughly one basis point per event, while hawkish pressure shows no effect. (2026-10-26, fanfare: 3)

![Three-Stage LLM Classification Pipeline](https://www.ml-quant.com/issues/2026-10-30/figures/arxiv-10-605798d6.png)

__[Multi-Agent Factor Generation with Diversity Regularization](https://arxiv.org/abs/2610.34936)__: A multi-agent framework with a log-determinant penalty on agent correlations generates diversified alpha factors and improves information coefficient by 1.1 percent. (2026-10-27, fanfare: 3)

![Multi-agent factor generation architecture with diversity penalty regularization flow diagram](https://www.ml-quant.com/issues/2026-10-30/figures/arxiv-11-1480cb2b.png)

__[Tail-Risk Scenarios via Generative Flow Networks](https://arxiv.org/abs/2610.38838)__: Generative Flow Networks systematically sample diverse and realistic financial crisis scenarios, outperforming reinforcement learning and MCMC baselines in scenario coverage and realism. (2026-10-30, fanfare: 3)

![Learned action distribution for the Close\_ndx variable at each of the 12 timesteps](https://www.ml-quant.com/issues/2026-10-30/figures/arxiv-12-26d2064f.png)

__[Non-Linear Governance Premium in FDI Flows](https://arxiv.org/abs/2610.35895)__: Gradient boosting reveals that sovereign ESG improvements confer zero capital advantage until institutional quality crosses a discrete threshold, cutting forecast errors 39% during tail events. (2026-10-28, fanfare: 3)

![SHAP feature importance plot showing governance and institutional quality drivers of FDI inflows with marginal impact values](https://www.ml-quant.com/issues/2026-10-30/figures/arxiv-13-13ab4a16.png)

__[Deep Learning for American Option Pricing](https://arxiv.org/abs/2610.36921)__: Operator splitting scheme with physics-informed neural networks prices five-dimensional American options under transaction costs and stochastic liquidity with validated boundary accuracy. (2026-10-28, fanfare: 2)

![American put option values across asset prices showing PINN and FDM agreement with transaction cost sensitivity.](https://www.ml-quant.com/issues/2026-10-30/figures/arxiv-14-6b643a1d.png)

__[Transformer Sentiment and Volatility Forecasting](https://arxiv.org/abs/2610.39636)__: Transformer-based news sentiment does not improve volatility forecasts beyond persistence and market indicators in walk-forward tests across eleven stocks and multiple horizons. (2026-10-30, fanfare: 2)

![Bar chart showing three-way distribution of sentiment labels: neutral, bearish, bullish in news corpus.](https://www.ml-quant.com/issues/2026-10-30/figures/arxiv-15-17549232.png)

__[Sparse Autoencoders For Crypto Portfolio Diversification](https://arxiv.org/abs/2610.39205)__: Sparse autoencoders extract interpretable factors from cryptocurrency returns and produce lower-volatility portfolios through better asset diversification, though without outperforming simple contrarian strategies. (2026-10-30, fanfare: 2)

![Sparse autoencoder pipeline: returns to sparse code to factor selection to portfolio evaluation](https://www.ml-quant.com/issues/2026-10-30/figures/arxiv-16-f66c636c.png)

__[Smooth Threshold GARCH For Volatility Asymmetry](https://arxiv.org/abs/2610.40047)__: A GARCH variant with an estimated threshold captures volatility responding asymmetrically to positive versus negative returns, allowing positive returns to have no volatility impact. (2026-10-30, fanfare: 2)

![NICs comparing volatility response of STGARCH versus T-GARCH, GARCH, GJR, and A-GARCH models](https://www.ml-quant.com/issues/2026-10-30/figures/arxiv-17-7ca5f85d.png)

__[Measuring Leveraged ETF Performance Over Time](https://arxiv.org/abs/2610.39097)__: The study shows that measuring leveraged ETF performance with arithmetic mean compounding can rank investments backwards and proposes using realized terminal wealth and expected log returns instead. (2026-10-30, fanfare: 2)

![ULPIX and UOPIX net returns, cost adjustments, and underlying-index benchmark](https://www.ml-quant.com/issues/2026-10-30/figures/arxiv-18-eeff3b75.png)

__[Robust Quadratic Optimization With Wasserstein Ambiguity](https://arxiv.org/abs/2610.39823)__: A tractable mixed-integer reformulation solves distributionally robust portfolio optimization over a Wasserstein ball, unifying chance-constrained and value-at-risk formulations. (2026-10-30, fanfare: 2)

![Wishart Ensemble perturbation model with {\mathsf{Q}}\_{\mathrm{nom}}={\mathsf{O}} . Out-of-sample performance and](https://www.ml-quant.com/issues/2026-10-30/figures/arxiv-19-b1b94879.png)

__[Time-Varying Quantile VAR Models And Inference](https://arxiv.org/abs/2610.36634)__: The research develops impulse response analysis for quantile VARs and finds that downside asymmetry in credit spreads and industrial production diminishes at longer forecast horizons. (2026-10-28, fanfare: 2)

![Plots of \rho\_{\tau,m}(u) for \tau=0.5,0.8](https://www.ml-quant.com/issues/2026-10-30/figures/arxiv-20-666e8991.png)

__[Market Making Under State-Dependent Hawkes Dynamics](https://arxiv.org/abs/2610.31903)__: A parametric volume model with state-dependent Hawkes order flow and quote-depth multipliers informs optimal placement policies that outperform baseline quoting in realized terms. (2026-10-26, fanfare: 2)

![Cumulative intra-day reward comparing five market-making strategies over one trading day.](https://www.ml-quant.com/issues/2026-10-30/figures/arxiv-21-36cb6c24.png)

__[Compiling Implied Volatility Surfaces With Survey Sampling](https://arxiv.org/abs/2610.33438)__: The study combines survey sampling and causal activity clocks to forecast NVIDIA implied volatility surfaces, finding that stratified allocation outperforms simple random sampling for cross-sectional accuracy. (2026-10-27, fanfare: 2)

![Granular NVDA Equity and Options Panel ( 2018\text{--}2025 ) and Full-Chain OPRA Index Harmonization](https://www.ml-quant.com/issues/2026-10-30/figures/arxiv-22-7f2bb233.png)

__[Neural Stratification Factors For Derivative Pricing](https://arxiv.org/abs/2610.32970)__: The research establishes continuity results for conditional expectations in stratified Monte Carlo, enabling neural networks to construct admissible stratification factors for path-dependent claims. (2026-10-26, fanfare: 2)

![Conditional probabilities of autocall and downside-protection events](https://www.ml-quant.com/issues/2026-10-30/figures/arxiv-23-5fa0fd30.png)

__[Mean-Variance Portfolios with RL Under Regime Switches](https://arxiv.org/abs/2610.32301)__: The study develops a model-free reinforcement learning algorithm that learns optimal no-shorting portfolio policies from trajectory data in regime-switching markets by decoupling constraints from policy exploration. (2026-10-26, fanfare: 2)

![Algorithm convergence showing portfolio weights and performance metrics across iterations.](https://www.ml-quant.com/issues/2026-10-30/figures/arxiv-24-7deebbd2.png)

__[Optimal Quote Placement via Hawkes Limit Order Books](https://arxiv.org/abs/2610.37324)__: A dynamic programming framework characterizes inventory-dependent optimal quotes for market makers facing Hawkes-driven order flow, tested on real limit-order-book data. (2026-10-28, fanfare: 2)

![Market maker quotes, inventory levels, and cumulative profit over a trading session.](https://www.ml-quant.com/issues/2026-10-30/figures/arxiv-25-0f4e696c.png)

__[Passive Market Impact Calibration with Queue Dynamics](https://arxiv.org/abs/2610.38442)__: A framework decomposing price into universal propagator and queue-sensitive corrections improves model fit and predicts passive limit-order impact better than classical propagator models. (2026-10-30, fanfare: 2)

![Queue sensitivity function showing decay with confidence bands and model fits.](https://www.ml-quant.com/issues/2026-10-30/figures/arxiv-26-a0221c55.png)

__[Semiparametric Real-Time GARCH with Nonstationarity](https://arxiv.org/abs/2610.36739)__: A two-step estimator combines kernel smoothing with GARCH to capture gradual variance changes while allowing short-run volatility to respond to current information. (2026-10-28, fanfare: 2)

![Returns and estimated long-run and short-run volatility components for natural gas futures.](https://www.ml-quant.com/issues/2026-10-30/figures/arxiv-27-8c214db1.png)

__[Multimodal Learning for Long Financial Documents](https://arxiv.org/abs/2610.37913)__: A transformer framework integrates semantically enriched table embeddings with unstructured text via cross-attention, showing structured data dominates but text adds incremental signal. (2026-10-30, fanfare: 2)

![Structured Table From Apple Inc](https://www.ml-quant.com/issues/2026-10-30/figures/arxiv-28-6b3ac6b9.png)

__[LLM-Enhanced Graphs for Financial Fraud Detection](https://arxiv.org/abs/2610.35298)__: Role-aware prompts and LLM-derived semantic enrichment of transaction graph attributes improve detection of fraud in heterogeneous card-merchant networks. (2026-10-27, fanfare: 2)

![Transaction graph showing global context (yellow dashed circle) and local path evidence (black edges) around target node.](https://www.ml-quant.com/issues/2026-10-30/figures/arxiv-29-f94acd87.png)

__[Dynamic Tail Intensities in Cross-Sectional Returns](https://arxiv.org/abs/2610.32011)__: Spline-mixture models detect persistent left and right tail dynamics in equity returns independent of volatility, improving one-percent quantile loss forecasts in most panels tested. (2026-10-26, fanfare: 2)

## SSRN

__[AI Monoculture Paradox in Markets](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7664018)__: When traders adopt more precise but common AI signals, synchronized order flow can outweigh forecast gains, reducing collective profits despite higher individual precision, a paradox that taxes can help resolve. (2026-10-28, fanfare: 4)

__[Upsilon: Joint Spot-Vol-Time Sensitivity](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7663319)__: The study defines Upsilon, an effective sensitivity for zero-days-to-expiration index options that captures the predictable, asymmetric joint dynamics of spot, implied volatility and time decay over their six-hour life. (2026-10-24, fanfare: 3)

__[Recognition Clocks and Liquidation Batches](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7665698)__: Research on decentralized lending shows that collateral revaluation rules predictably determine when and how many positions are liquidated together, with large price moves triggering batch liquidations far exceeding single-position boundaries. (2026-10-24, fanfare: 3)

__[GenAI and Earnings Price Formation](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7668118)__: The study shows that greater ChatGPT exposure in countries accelerates how quickly stock prices incorporate earnings-announcement information, with effects strongest where investors are sophisticated and data is transparent. (2026-10-30, fanfare: 3)

__[Macroeconomic Risk and Equity Premium](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7664661)__: The research identifies trend-growth risk as the largest component of the equity risk premium through persistent shocks in a structural VAR, with implications depending on whether yield changes come from monetary or fiscal sources. (2026-10-30, fanfare: 3)

__[State-Dependent Panic Risk Pricing](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7675439)__: A real-time panic index combining price stress, investor holdings and drawdown pain reveals that exposure to panic innovations carries a negative return premium, larger during high-panic periods and persistent for weeks. (2026-10-27, fanfare: 3)

__[ETFs and Crypto Volatility](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7665846)__: Analysis of eight staggered spot cryptoasset ETF launches finds that late-2025 altcoin ETFs reduced daily volatility by about eighteen percent, though crash propensity remained unchanged. (2026-10-24, fanfare: 3)

__[Treasury Settlements Drive Repo Pressure](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7675064)__: Treasury debt settlements, not reserve scarcity alone, explain recent repo-market pressure, with 100 billion in settlements raising the SOFR-IORB spread by 4.3 basis points. (2026-10-27, fanfare: 3)

__[Bank Systemic Risk Without Market Prices](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7673518)__: Stressed expected loss measured from balance-sheet data captures systemic risk for unlisted banks, covering 38% of European bank assets that market-based SRISK cannot reach. (2026-10-27, fanfare: 3)

__[CoVaR Contagion Forecast Calibration](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7665847)__: Conformal prediction corrects CoVaR understatement of crypto-treasury contagion by two- to fourfold, reducing simulated distress-day loss frequency from 30% to 7%. (2026-10-24, fanfare: 3)

__[Private ECB Meetings and Trading Profits](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7674539)__: Private bilateral meetings between ECB officials and financial institutions correlate with higher trading profits, suggesting selective communication conveys economically valuable central-bank information. (2026-10-28, fanfare: 3)

__[Analyst Calls and Forecast Accuracy](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7664081)__: Mobile-call data between brokerages and firms reveals that brokerage-initiated calls improve forecast accuracy, while firm-initiated calls reduce it, suggesting information gathering versus lobbying. (2026-10-28, fanfare: 3)

__[LLM Stock Ratings and News Topics](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7665703)__: Research using FinBERT embeddings finds that firm-specific and fundamental news topics explain LLM stock ratings far better than sentiment alone, doubling the model's explanatory power. (2026-10-24, fanfare: 3)

__[AI and Cognitive Capital in Markets](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7661969)__: A model shows that cheaper AI substitutes for trader expertise and erodes cognitive skills, yet price informativeness remains stable until unexpected AI outages expose fragility in human expertise. (2026-10-24, fanfare: 3)

__[Earnings Prediction Markets vs Analysts](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7667338)__: Polymarket earnings contracts incrementally improve forecasts by incorporating public information missed by analysts, though only in the final trading day before announcements. (2026-10-30, fanfare: 3)

__[Common Ownership Across 49 Countries](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7663379)__: Study of 49 economies finds common ownership roughly doubled or tripled since 2005, concentrated in large firms and increasingly driven by the Big Three asset managers globally. (2026-10-24, fanfare: 3)

__[Asset Performance Across Inflation Regimes](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7662619)__: Classifying 107 years of data into real-time inflation and growth states, the study finds sixty-forty portfolios earn negative real returns in both overheating and stagflation conditions. (2026-10-24, fanfare: 3)

__[Five-Minute Adverse Selection Measure](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7667218)__: Validating against event-contract settlement data, the five-minute midquote captures only 68.7% of true price impact and significantly misprices thin markets, challenging the convention. (2026-10-30, fanfare: 2)

__[Daily Mean Reversion in Global Equities](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7663278)__: Systematic trading strategies exploiting significant negative autocorrelation across six major equity indices achieve a pooled Sharpe ratio of 1.640 with regime filtering and downside-risk controls over 2002-2026. (2026-10-27, fanfare: 2)

__[Correlated Random-Level Volatility Model](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7664360)__: A six-parameter volatility model with one Gaussian factor prices European options exactly and captures implied-volatility skew better than SSVI or Heston, though short-maturity slopes remain too steep. (2026-10-28, fanfare: 2)

__[Limit Order Book Depth-Volatility Anomaly](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7675440)__: Shallow-book imbalance shocks predict higher future volatility and positive returns, while deep-book shocks predict lower future volatility and negative returns, a pattern robust across Chinese stock constituents since 2014. (2026-10-27, fanfare: 2)

__[Rebuilding Volatility Surfaces from Public Data](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7664478)__: A pipeline reconstructs equity volatility surfaces from free options data and assesses reliability; a short delta-hedged straddle harvests the variance risk premium, profitable in pre-registered tests but with realistic frictions limiting the edge. (2026-10-28, fanfare: 2)

__[Arbitrage-Free Neural Operators for Options](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7665478)__: Proposes a neural-operator architecture that certifiably outputs arbitrage-free European call-price surfaces, addressing failure of standard neural operators to enforce no-arbitrage constraints. (2026-10-27, fanfare: 2)

__[Decomposing Returns with Grouped Predictors](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7666838)__: A VAR that groups 25 predictors by persistence into three composite state variables recovers stable estimates of discount-rate and cash-flow news without specification dependence. (2026-10-30, fanfare: 2)

__[Adverse Selection in NSE Options Market Making](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7667778)__: Analysis of live market-making on Indian options shows losses to adverse selection within one second, but regime-detection signals that pull quotes in stress cut losses by forty-six percent for NIFTY in early tests. (2026-10-30, fanfare: 2)

__[Replicating Low-Volatility Indices](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7664321)__: Rebuilding low-volatility indices from public data shows that a rank-based method replicates closely but an optimization-based method fails due to index-specification ambiguity, not covariance estimation. (2026-10-28, fanfare: 2)

__[Distribution Timing in Currency Portfolios](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7660982)__: Adding skewness and kurtosis to mean-variance allocations in currency markets produces economically meaningful gains that depend critically on the investment mandate and ability to adjust gross exposure. (2026-10-24, fanfare: 2)

__[Multi-Factor Investing in Borsa Istanbul](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7662780)__: A five-characteristic long-only strategy for Turkish stocks earns 4.4% annual alpha against the cap-weighted index, but equal-weighted scaling erodes the advantage, suggesting gains come from breadth and risk management rather than characteristics. (2026-10-24, fanfare: 2)

__[Fraud Detection Under Temporal Shift](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7675297)__: A multi-view learning framework for fraud detection integrates transaction, behavioral, temporal and graph features while avoiding look-ahead bias, with value depending strongly on the dataset. (2026-10-27, fanfare: 2)

__[Official Inflation Data and Turkish Expectations](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7665101)__: Türkiye survey respondents shown the official 30.6-32.6% inflation rate lower their expectations by 3.2 percentage points, though the effect is selective by age and political affiliation. (2026-10-28, fanfare: 2)

## RePEc

__[150 Years of US Inflation Expectations](https://econpapers.repec.org/RePEc:zbw:qucehw:344109)__: A narrative intensity measure from New York Times articles since 1870 predicts inflation better than commodity-based measures and reveals that the 1930s deflation was anticipated earlier than consensus suggests. (2026-10-26, fanfare: 4)

![Three time series comparing inflation, narrative intensity, and grain futures spreads from 1870-2020.](https://www.ml-quant.com/issues/2026-10-30/figures/repec-01-80377a2d.png)

__[US Monetary Policy Spillovers in Commercial Real Estate](https://econpapers.repec.org/RePEc:dnb:dnbwpp:872)__: Research reveals that US quantitative easing raises global commercial real estate prices and credit, with spillback effects amplified through trade linkages and cross-border investor ownership. (2026-10-19, fanfare: 4)

![CRE price responses to US QE shock within and outside the US across multiple variables](https://www.ml-quant.com/issues/2026-10-30/figures/repec-02-5a113f6d.png)

__[US Monetary Policy and Global Risk Premia](https://econpapers.repec.org/RePEc:nbr:nberwo:35902)__: A general equilibrium model shows US monetary tightening erodes currency-mismatched arbitrageurs' wealth, raising global risk premia and generating spillovers to international asset prices. (2026-10-26, fanfare: 3)

![impulse responses to U.S. monetary tightening](https://www.ml-quant.com/issues/2026-10-30/figures/repec-03-563c986c.png)

__[NDF Order Flow and Exchange Rate Transmission](https://econpapers.repec.org/RePEc:bis:biswps:1383)__: Transaction-level data from centrally cleared NDFs shows offshore order flow moves both forward and spot rates, with a $1 billion long-USD order depreciating the spot by roughly 10 basis points. (2026-10-26, fanfare: 3)

![Market structure diagram showing offshore investors, NDF dealers, and arbitrageurs connected by order flow arrows.](https://www.ml-quant.com/issues/2026-10-30/figures/repec-04-33da21e4.png)

__[Global Financial Cycles and Firm Investment](https://econpapers.repec.org/RePEc:nbr:nberwo:35918)__: The study shows that rising risk premia trigger large investment contractions, especially among risky firms, through a channel linking global price of risk to financing costs via firm debt sensitivity. (2026-10-26, fanfare: 3)

![Decomposition of Emerging Market Corporate Borrowing Costs](https://www.ml-quant.com/issues/2026-10-30/figures/repec-05-36ec7348.png)

__[Stablecoins and Bank Deposits as Substitutes](https://econpapers.repec.org/RePEc:bis:biswps:1382)__: Stablecoin growth slows roughly 3.75 percentage points per standard-deviation rise in deposit rates, with effects three times larger using instrumental variables and stronger for USDC than USDT. (2026-10-26, fanfare: 3)

![OLS and IV estimates of stablecoin growth response to deposit rate increases over time.](https://www.ml-quant.com/issues/2026-10-30/figures/repec-06-29a803d6.png)

__[Loan Officer Pricing Power in Mortgages](https://econpapers.repec.org/RePEc:nbr:nberwo:35907)__: Mortgage intermediaries capture over 60% of $1.5 billion in GSE fee waivers; loan officers with histories of pricing expensive mortgages pass through less than 20% of savings. (2026-10-26, fanfare: 3)

![Pass-through rates over time showing sharp drop after policy change, with confidence intervals](https://www.ml-quant.com/issues/2026-10-30/figures/repec-07-7f810ab3.png)

__[Shareholder Diversification and Investment Resilience](https://econpapers.repec.org/RePEc:ecb:ecbwps:20263296)__: Firms with home-biased shareholders cut investment 20% more than diversified peers during the pandemic, revealing equity concentration weakens corporate resilience to shocks. (2026-10-19, fanfare: 3)

![Sectoral distribution of EA shareholders](https://www.ml-quant.com/issues/2026-10-30/figures/repec-08-82cc607e.png)

__[Monetary Policy Transmission in an AI Economy](https://econpapers.repec.org/RePEc:diw:diwwpp:dp2184)__: A model shows monetary policy amplifies more when AI automates some workers but not others, because shielded workers' higher wages generate additional demand. (2026-10-26, fanfare: 3)

![Stacked bar charts decomposing consumption response by income source for non-AI and AI economies](https://www.ml-quant.com/issues/2026-10-30/figures/repec-09-2b8e4baf.png)

__[Credit Markets with New Screening Technologies](https://econpapers.repec.org/RePEc:nbr:nberwo:35905)__: When screening errors correlate through shared AI systems, lenders specialize into distinct market segments with uniform rates or uniform quality but dispersed rates. (2026-10-27, fanfare: 3)

__[Macroprudential Policy Against Inflationary Shocks](https://econpapers.repec.org/RePEc:ptu:wpaper:w202609)__: Pre-emptive, countercyclical macroprudential tools strengthen banking resilience and dampen external shocks in a monetary union, while procyclical tightening worsens outcomes. (2026-10-26, fanfare: 3)

__[Sentiment and Sovereign Bond Markets](https://econpapers.repec.org/RePEc:ema:worpap:2026-14)__: A framework shows how domestic political polarization shifts both portfolio demand in sovereign bond markets and government incentives in crises, creating a sentiment-driven constraint on confrontational foreign policy. (2026-10-26, fanfare: 3)

__[DSGE Models in Central Banks](https://econpapers.repec.org/RePEc:bfr:banfra:1066)__: A comprehensive review traces how DSGE models became standard policy tools and assesses their value for connecting theory, data and policy experiments despite known limitations. (2026-10-26, fanfare: 3)

![Timeline showing evolution of DSGE models from macroeconomic to representative-agent to HANK toolkits](https://www.ml-quant.com/issues/2026-10-30/figures/repec-13-9f0aeab0.png)

__[Financial Inclusion Determinants](https://econpapers.repec.org/RePEc:ces:ceswps:_13039)__: Meta-analysis of 5,172 estimates finds robust evidence for technology and infrastructure as drivers of financial inclusion, while publication bias distorts reported effects across studies. (2026-10-22, fanfare: 3)

![Funnel plot showing publication bias in financial inclusion effect estimates across studies.](https://www.ml-quant.com/issues/2026-10-30/figures/repec-14-d2c2b580.png)

__[EU ETS Carbon Leakage via Trade](https://econpapers.repec.org/RePEc:jau:wpaper:2026/10)__: Causal evidence shows the EU emissions scheme drives an 8% rise in intermediate-goods imports from non-regulated countries, with 11% higher embodied carbon in manufacturing. (2026-10-21, fanfare: 3)

![Event-study plot showing 8% rise in imports from non-ETS countries after EU ETS introduction](https://www.ml-quant.com/issues/2026-10-30/figures/repec-15-ec50f9d5.png)

__[Climate Spillovers in Sovereign Ratings](https://econpapers.repec.org/RePEc:ces:ceswps:_13066)__: Global value chains transmit climate risk to sovereigns: ignoring foreign climate spillovers underestimates total risk by 30% and raises measured borrowing costs by 11 basis points. (2026-10-26, fanfare: 3)

![Temperature impacts decompose total, domestic, and foreign climate risk on sovereign ratings.](https://www.ml-quant.com/issues/2026-10-30/figures/repec-16-9c02583f.png)

__[Stress Vulnerability Indices for Banking Sectors](https://econpapers.repec.org/RePEc:ecb:ecbwps:20263295)__: Two new indicators track euro-area banking vulnerabilities using stress tests: one measures capital depletion under tail risk, the other assesses earnings capacity to absorb losses. (2026-10-19, fanfare: 2)

![Stacked area chart decomposing stress vulnerability index components over time by color.](https://www.ml-quant.com/issues/2026-10-30/figures/repec-17-7bc14756.png)

__[Insurer Demand for Corporate Bonds and Policy](https://econpapers.repec.org/RePEc:boe:boeewp:023786)__: UK monetary tightening raises domestic insurer demand for corporate bonds but reduces foreign demand, creating asymmetric effects driven by regulation and liability structures. (2026-10-26, fanfare: 2)

![UK and US insurers' gilt and corporate bond holdings comparison, 2019 Q4.](https://www.ml-quant.com/issues/2026-10-30/figures/repec-18-f8d1a10c.png)

__[Cross-Border Payment Rails Compared](https://econpapers.repec.org/RePEc:bis:biswps:1385)__: Fintech money-transfer operators have compressed cross-border payment costs relative to banks; stablecoins do not eliminate frictions across corridors and payment values. (2026-10-26, fanfare: 2)

![Median costs by fintech MTOs decline steeply with transfer amount across four tier levels.](https://www.ml-quant.com/issues/2026-10-30/figures/repec-19-4b2a0466.png)

__[Carbon Neutrality Pledges and Stock Returns](https://econpapers.repec.org/RePEc:prg:jnlwps:v:6:y:2026:id:6.005)__: Market analysis of 887 carbon neutrality announcements reveals statistically significant negative abnormal returns over 12 months, suggesting investors view pledges as signals of future cost burdens. (2026-10-27, fanfare: 2)

![Market reaction after the standard earnings call (Group 0) and earnings call connected with carbon neutraliy pledge](https://www.ml-quant.com/issues/2026-10-30/figures/repec-20-ff130e12.png)

__[Deep Learning for Central Bank Forecasting](https://econpapers.repec.org/RePEc:ptu:wpaper:w202612)__: Pretrained time-series foundation models match or outperform benchmarks on selected inflation and nowcasting targets but add limited gains for labor-market aggregates or irregular series. (2026-10-26, fanfare: 2)

__[Energy Shocks and Household Inflation Expectations](https://econpapers.repec.org/RePEc:ces:ceswps:_13047)__: Using German survey data, the paper finds that oil and gas supply shocks shift inflation expectations rightward and increase disagreement, with households overestimating inflationary effects. (2026-10-26, fanfare: 2)

![Effects of energy supply shocks in 2022](https://www.ml-quant.com/issues/2026-10-30/figures/repec-22-0ae6a4e2.png)

__[Monetary Policy Investment Responsiveness Across Firms](https://econpapers.repec.org/RePEc:fip:fednls:103898)__: The research documents that most firms respond little to interest rate changes, but some show strong sensitivity; substantial heterogeneity cannot be easily explained by standard firm traits. (2026-10-19, fanfare: 2)

__[Assessing Monetary Policy with Language Models](https://econpapers.repec.org/RePEc:fip:fedgfn:103907)__: The paper employs large language models and semi-structural models to evaluate central bank communication and its role in shaping policy expectations. (2026-10-26, fanfare: 2)

__[Sovereign Ratings and Fiscal Fundamentals](https://econpapers.repec.org/RePEc:imf:imfwpa:2026/195)__: Sovereign credit ratings overweight debt relative to primary balances, understate the role of growth, and exhibit large unexplained country-specific effects. (2026-10-23, fanfare: 2)

__[Gold Safe Haven Under Shifting Conditions](https://econpapers.repec.org/RePEc:bis:biswps:1380)__: Geopolitical risks increase gold returns more strongly during loose financial conditions via credit and liquidity channels, with the largest impact in 2024–26. (2026-10-19, fanfare: 2)

![Impulse response functions showing geopolitical risk impact on gold returns under tight versus loose credit conditions.](https://www.ml-quant.com/issues/2026-10-30/figures/repec-26-30bd89b2.png)

__[State-Dependent Pricing and Expectation De-Anchoring](https://econpapers.repec.org/RePEc:zbw:bofrdp:344113)__: The research finds that price-adjustment behavior and de-anchored household expectations interact super-additively to raise inflation costs, compounding risks when both channels activate. (2026-10-26, fanfare: 2)

![Super-additivity of state-dependent pricing and de-anchoring: four mechanisms with markup shock impulse responses](https://www.ml-quant.com/issues/2026-10-30/figures/repec-27-06b278a2.png)

__[Demand Shocks and Monetary Policy Trade-Offs](https://econpapers.repec.org/RePEc:bon:boncrc:crctr224_2025_785)__: The study shows that in New Keynesian models, more persistent demand shocks do not always boost output more; monetary policy stance determines whether permanent income or real interest rate effects dominate. (2026-10-19, fanfare: 2)

![Impact Multipliers and Persistence](https://www.ml-quant.com/issues/2026-10-30/figures/repec-28-1af20421.png)

__[AI Assistants and Investor Belief Reinforcement](https://econpapers.repec.org/RePEc:bfi:wpaper:2026-133)__: AI-generated investment replies are systematically more bullish than human replies on Reddit; instruction-tuning amplifies bullishness beyond the base model's inherent bias. (2026-10-26, fanfare: 2)

![AI-human bullish gap: instruction-tuned model versus human replies](https://www.ml-quant.com/issues/2026-10-30/figures/repec-29-6a837182.png)

__[Monetary Policy and Firm Productivity](https://econpapers.repec.org/RePEc:onb:oenbwp:284)__: Using Austrian firm data, tighter monetary policy reduces productivity-enhancing investment and firm total factor productivity, with stronger effects among highly leveraged firms. (2026-10-26, fanfare: 2)

![Causal diagram showing monetary policy stance effects on firm productivity through multiple channels.](https://www.ml-quant.com/issues/2026-10-30/figures/repec-30-4f6ebca3.png)