---
title: Quant Letter No. 139: November 2026, Week 2
url: https://www.ml-quant.com/issues/2026-11-13/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-11-14
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
issue_date: 2026-11-13
---

# Quant Letter: November 2026, Week 2: Weekly quantitative finance newsletter

*This week: generalization and robustness in execution and hedging; and systematic evidence failures in published quant research. Standouts: Microstructure Invariance For Execution Policies shows trained policies transfer across assets and venues. RL Hedging for Options cuts tail losses versus delta. Evidence Audit of 91 LLM Trading Papers catalogs severe methodological flaws—51% ignore costs, 98% have look-ahead bias.*

## Top picks

### 1. [Microstructure Invariance For Execution Policies](https://arxiv.org/abs/2611.08701) · arXiv

### 2. [Evidence Audit of 91 LLM Trading Papers](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7687481) · SSRN

### 3. [Asymmetric Idiosyncratic Return Reversal](https://econpapers.repec.org/RePEc:nbr:nberwo:35840) · RePEc

### 4. [Code-Paper Discrepancies in Finance Research](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7681378) · SSRN

### 5. [Debt Maturity Term Premium Inflation](https://econpapers.repec.org/RePEc:nbb:reswpp:202610-498) · RePEc

## What's rising

### Earnings and analysts: 44 papers this week, 1.5× the usual 29.7

### Foundation models: 13 papers this week, 2.1× the usual 6.3

### Insurance: 31 papers this week, 1.5× the usual 20.5

## GitHub radar

### Quant repos rising

#### [we-promise/sure](https://github.com/we-promise/sure): Personal finance app for managing money and budgets. (10 quants, 14.5k stars, +3,657 this week)

#### [open-net-worth-ai/open-net-worth](https://github.com/open-net-worth-ai/open-net-worth): Self-hosted AI agent managing budget, investments, and trades. (3 quants, 437 stars, +418 this week, new repo)

#### [zadescoxp/calibrain-harness](https://github.com/zadescoxp/calibrain-harness): Forward-testing harness for trading crypto perpetuals on Bybit. (2 quants, 145 stars, +145 this week, new repo)

#### [zadescoxp/order-flow-screener](https://github.com/zadescoxp/order-flow-screener): Free open source order flow screener for spot and perpetual trading. (2 quants, 184 stars, +151 this week, new repo)

#### [AI4Finance-Foundation/FinRL-Trading](https://github.com/AI4Finance-Foundation/FinRL-Trading): FinRL-X: AI-native modular infrastructure for quantitative trading. (2 quants, 3,938 stars, +27 this week)

#### [QuantJourneyOrg/quantjourney-bt](https://github.com/QuantJourneyOrg/quantjourney-bt): Backtesting engine for quantitative trading and portfolio analysis. (2 quants, 648 stars, +41 this week)

#### [marketcalls/openalgo-charts](https://github.com/marketcalls/openalgo-charts): Production-grade charting library for brokers and trading platforms. (2 quants, 197 stars, +8 this week)

#### [shiyu-coder/Kronos](https://github.com/shiyu-coder/Kronos): Foundation model for the language of financial markets. (2 quants, 47.7k stars, +655 this week)

### What quants are playing with

#### [janestreet/agent\_skills](https://github.com/janestreet/agent_skills): Agent skills framework for building AI agent capabilities. (29 quants, 8,098 stars, +8,081 this week, new repo)

#### [aws/aws-emulator](https://github.com/aws/aws-emulator): Local emulator of AWS APIs for development and testing. (13 quants, 6,507 stars, +6,510 this week, new repo)

#### [NVIDIA/auto-ontology](https://github.com/NVIDIA/auto-ontology): Python framework for semantic graph-based reasoning over data. (12 quants, 12.5k stars, +3,088 this week)

#### [GoogleChrome/modern-web-guidance](https://github.com/GoogleChrome/modern-web-guidance): Web best practices guidance for coding agents. (11 quants, 9,906 stars, +7,280 this week)

#### [storytold/craft-anything](https://github.com/storytold/craft-anything): Understand how software works and build your own. (10 quants, 31.1k stars, +12.3k this week, new repo)

#### [google/elephant-goldfish-model](https://github.com/google/elephant-goldfish-model): Open-source plugin for designing and reviewing software with agents. (9 quants, 18.4k stars, +5,239 this week, new repo)

## We called it

### [AlphaSAGE: Structure-Aware Alpha Mining via GFlowNets for Robust Exploration](http://arxiv.org/abs/2509.25055v2): now published in International Conference on Learning Representations (featured 3 Oct 2025, 4 days after release; 30 citations)

### [EDINET-Bench: Evaluating LLMs on Complex Financial Tasks using Japanese Financial Statements](http://arxiv.org/abs/2506.08762v1): now published in International Conference on Learning Representations (featured 11 Jun 2025, 1 day after release; 13 citations)

### [Passive market impact: a point process approach](http://arxiv.org/abs/2412.07461v1): now published in Finance and Stochastics (featured 12 Dec 2024, 2 days after release; 6 citations)

## arXiv

__[Microstructure Invariance For Execution Policies](https://arxiv.org/abs/2611.08701)__: An execution policy trained once on 954 stocks in normalized units generalizes to unseen assets, exchanges, and crypto with minimal performance loss. (2026-11-09, fanfare: 4)

![Zero-shot policy transfer performance across crypto exchange and stock exchange pairs with confidence intervals.](https://www.ml-quant.com/issues/2026-11-13/figures/arxiv-01-a5745d65.png)

__[RL Hedging for Options Market Makers](https://arxiv.org/abs/2611.10956)__: A reinforcement learning agent trained on historical S&P 500 options data reduces tail losses by 3.1% versus delta hedging, reaching 8.9% in volatile downturns. (2026-11-10, fanfare: 3)

![Stress-period paths for empirical TD3 (CVaR 95 ): COVID-19 crash (Q2, top) and 2022 selloff (Q3, bottom)](https://www.ml-quant.com/issues/2026-11-13/figures/arxiv-02-ece4c5fd.png)

__[Time-Series Foundation Models for Futures Returns](https://arxiv.org/abs/2611.12141)__: A pre-registered evaluation finds zero evidence that five frozen time-series foundation models beat a no-change forecast on index futures returns across 160 tests. (2026-11-10, fanfare: 3)

![What the study could detect](https://www.ml-quant.com/issues/2026-11-13/figures/arxiv-03-20e44169.png)

__[Market-Conditioned JEPA For Asset Prediction](https://arxiv.org/abs/2611.13572)__: The study conditions self-supervised learning on market factors to improve prediction of returns, exposure, risk, and liquidity across minute-level stock data. (2026-11-11, fanfare: 3)

![Cumulative excess return over the CSI300 index of the backtest in Table 2 , net of transaction costs](https://www.ml-quant.com/issues/2026-11-13/figures/arxiv-04-678e09b0.png)

__[Heterogeneous Ensemble Learning For Cross-Sectional Prediction](https://arxiv.org/abs/2611.12389)__: Combining linear and nonlinear predictors as specialists improves rank IC by over 13% on daily Chinese stock returns versus neural networks alone. (2026-11-10, fanfare: 3)

![Pipeline of the Diversified Expert Ensemble](https://www.ml-quant.com/issues/2026-11-13/figures/arxiv-05-41c5db08.png)

__[Fine-Tuned Order-Flow Generative Model](https://arxiv.org/abs/2611.14117)__: A generative model pretrained on crypto order-flow data and fine-tuned for microprice forecasting outperforms gradient boosting and transfers zero-shot to unseen assets. (2026-11-11, fanfare: 3)

![Directional skill vs lead time: corr (\mu\_{h}, return over (t{+}\text{gap},t{+}\text{gap}{+}h\]) against lead gap (as a](https://www.ml-quant.com/issues/2026-11-13/figures/arxiv-06-3023ff5e.png)

__[Tabular Foundation Models For Earnings Forecasting](https://arxiv.org/abs/2611.13550)__: A tabular foundation model pretrained on synthetic data outperforms tree-based and language models at predicting next-year earnings changes. (2026-11-11, fanfare: 3)

![ROC curves comparing TabFM, Llama, and tree-based models for earnings forecasting performance.](https://www.ml-quant.com/issues/2026-11-13/figures/arxiv-07-dcfdccfb.png)

__[In-Loop Deflation In Strategy Discovery](https://arxiv.org/abs/2611.14539)__: Moving backtest corrections inside autonomous searches exposes a variance-dilution effect; gating with safeguards eliminates spurious discovery across 53 audited runs. (2026-11-13, fanfare: 3)

![One representative real trial stream under the scripted near-mean flood (shaded), with alpha-wealth stopping](https://www.ml-quant.com/issues/2026-11-13/figures/arxiv-08-4df7d501.png)

__[Seasonal Patterns in Global Futures Markets](https://arxiv.org/abs/2611.15657)__: The research identifies five seasonality anomalies across bond, commodity and equity futures; slower seasonal patterns produce positive net returns after costs while fast patterns do not. (2026-11-13, fanfare: 3)

![Gross out-of-sample total return and Sharpe ratio for models fit using the day-of-month seasonality factor](https://www.ml-quant.com/issues/2026-11-13/figures/arxiv-09-a627ac21.png)

__[Boolean Rules and Factor Portfolio Turnover](https://arxiv.org/abs/2611.09834)__: Walsh decomposition reveals how interactions among signals drive turnover and persistence; most single-signal portfolios beat quoted spreads but few multi-signal combinations do. (2026-11-09, fanfare: 3)

![Figure OA.3: GRS p -values of FF6 (1995–2024) and q^{5} (1995–2019) for test assets built from the same twelve signals](https://www.ml-quant.com/issues/2026-11-13/figures/arxiv-10-fa909fbf.png)

__[Polynomial-Preserving Stochastic Volatility Models](https://arxiv.org/abs/2611.10928)__: Discrete-time volatility models preserving polynomial moments reduce implied volatility root mean squared errors by 8–41% versus benchmarks across options spanning 1996–2023. (2026-11-10, fanfare: 3)

![Smoothed implied volatility RMSE over time comparing four models under Gaussian and NIG innovations.](https://www.ml-quant.com/issues/2026-11-13/figures/arxiv-11-8ad9c552.png)

__[Bayesian Precision Models for Macro Panels](https://arxiv.org/abs/2611.12368)__: VAPOR jointly estimates vector autoregressive dynamics and factor-structured precision to reveal direct dependencies in high-dimensional macroeconomic time series. (2026-11-10, fanfare: 3)

![Heatmaps comparing marginal correlation versus conditional dependence among Treasury rates.](https://www.ml-quant.com/issues/2026-11-13/figures/arxiv-12-2a0a1995.png)

__[Radial Weighting for Mutual Fund Selection](https://arxiv.org/abs/2611.11762)__: Develops a robust procedure using radially weighted alpha estimation to identify skilled mutual funds, controlling false discovery rates even under heavy-tailed returns and cross-fund dependence. (2026-11-10, fanfare: 3)

![Portfolio wealth trajectories over time comparing three fund selection methods across holding periods.](https://www.ml-quant.com/issues/2026-11-13/figures/arxiv-13-d81354b5.png)

__[Neurosymbolic RL for Portfolio Mandates](https://arxiv.org/abs/2611.17329)__: Presents a continuous-action reinforcement learning method that learns to internalize investment policy constraints without post-hoc correction, reducing mandate violations from 96% to 4.8%. (2026-11-13, fanfare: 3)

![Passive violation rate drops steeply with safety weight lambda, then plateaus near 5%.](https://www.ml-quant.com/issues/2026-11-13/figures/arxiv-14-6bf1f1cb.png)

__[Pre-Consensus Trade Approvals for DEXs](https://arxiv.org/abs/2611.10254)__: Introduces FirstSwap, a decentralized exchange giving traders early price guarantees before consensus via a scaled admissibility protocol, improving welfare and reducing arbitrage losses. (2026-11-10, fanfare: 3)

__[Learning from Earnings Surprise Magnitude](https://arxiv.org/abs/2611.15747)__: The study shows that investors learning about both news and valuation multipliers from earnings surprises respond less on average but more sharply near zero when multiplier uncertainty is high. (2026-11-13, fanfare: 3)

![Average and local sensitivity decrease with multiplier uncertainty, sharper response near zero.](https://www.ml-quant.com/issues/2026-11-13/figures/arxiv-16-b04b9b97.png)

__[Optimal Trading Speed On Blockchain](https://arxiv.org/abs/2611.13616)__: Faster blocks lower priority-fee competition and shift rents to arbitrageurs; Base Flashblocks data confirms liquidity peaks at interior speeds. (2026-11-11, fanfare: 3)

![Transaction execution on a blockchain](https://www.ml-quant.com/issues/2026-11-13/figures/arxiv-17-73ac2609.png)

__[Point-in-Time Encoders For Earnings Calls](https://arxiv.org/abs/2611.14325)__: Encoders trained on earnings calls with yearly cutoffs raise rank correlation of return forecasts by about half and improve earnings-surprise prediction. (2026-11-11, fanfare: 2)

![Recall@1 on held-out calls by test quarter, for the vintage available at the time, so vintage Y is scored on the second](https://www.ml-quant.com/issues/2026-11-13/figures/arxiv-18-96fcbc71.png)

__[Backtesting High-Dimensional Expected Shortfall](https://arxiv.org/abs/2611.13882)__: Orthogonal methods remove bias in downside-risk backtests when estimating high-dimensional Value-at-Risk and Expected Shortfall forecasts. (2026-11-11, fanfare: 2)

![Power against omitted predictors](https://www.ml-quant.com/issues/2026-11-13/figures/arxiv-19-6b9ecdd9.png)

__[Price-Impact Decay Is Not Identifiable](https://arxiv.org/abs/2611.10574)__: A no-go theorem proves that propagator estimation cannot identify asymptotic impact decay because diffusive prices force fine-tuned decay artificially. (2026-11-10, fanfare: 2)

![Linear propagator estimation applied to the SFK model with M=1 , \alpha\in\{1.25,1.5,1.75\} , and \delta=1/2 . (a) The](https://www.ml-quant.com/issues/2026-11-13/figures/arxiv-20-e32f15b9.png)

__[Compact Foundation Model for Financial Forecasting](https://arxiv.org/abs/2611.09176)__: A 2.68 million-parameter model with grouped-query attention reduces inference latency and matches larger baselines on financial time-series forecasting across multiple assets. (2026-11-09, fanfare: 2)

![Scaling behavior of dense and MoE TailDART. Lower validation NLL is better in all panels](https://www.ml-quant.com/issues/2026-11-13/figures/arxiv-21-f9a33d53.png)

__[Certifying Tail Risk Under Selective Labels](https://arxiv.org/abs/2611.12713)__: The research derives bounds on conditional value-at-risk when outcome data is hidden, enabling statistical certificates for tail-risk claims across predictors. (2026-11-10, fanfare: 2)

![CVaR bounds showing how selective labeling creates ambiguity in tail risk assessment.](https://www.ml-quant.com/issues/2026-11-13/figures/arxiv-22-f009efcf.png)

__[Point-in-Time Benchmark for LLM Agents](https://arxiv.org/abs/2611.12966)__: Evaluates LLM agents on 408 investment research tasks, finding that half of workflows passing result checks contain temporal violations, revealing a 50.8% gap between output agreement and temporal validity. (2026-11-11, fanfare: 3)

![Joint distribution of result outcomes and temporal violations, showing 50% pass with PIT violations.](https://www.ml-quant.com/issues/2026-11-13/figures/arxiv-23-e4e048d3.png)

__[Knowledge Cutoff Benchmark for Financial LLMs](https://arxiv.org/abs/2611.11588)__: Introduces FinCutBench, a monthly updated benchmark measuring parametric knowledge boundaries in financial LLMs, tracking 9,000 questions from 2023 to 2026 to detect look-ahead bias. (2026-11-10, fanfare: 3)

![Monthly accuracy trends with knowledge cutoff boundaries for six financial LLMs, shaded confidence intervals.](https://www.ml-quant.com/issues/2026-11-13/figures/arxiv-24-9abbe2a0.png)

__[Market-Specific Momentum Models Beat Global Factors](https://arxiv.org/abs/2611.15646)__: Fitting nonlinear time series momentum models to individual markets rather than globally produces modestly better out-of-sample returns despite smaller sample sizes. (2026-11-13, fanfare: 2)

![Average out-of-sample correlation to the Global Linear model, evaluated across 1 , 6 and 12 -month time series momentum](https://www.ml-quant.com/issues/2026-11-13/figures/arxiv-25-ddf096eb.png)

__[Risk Management in RL Market Making](https://arxiv.org/abs/2611.09012)__: Adding inventory and cash management penalties to reinforcement learning market makers improves resilience and adaptability compared to pure profit-maximizing approaches. (2026-11-09, fanfare: 2)

![Reward per Episode](https://www.ml-quant.com/issues/2026-11-13/figures/arxiv-26-21dac562.png)

__[Multi-Agent Framework for Crypto Futures Trading](https://arxiv.org/abs/2611.10586)__: A micro-macro-micro framework combining numerical states with linguistic market interpretations and cross-asset communication improves coordinated trading decisions in perpetual futures. (2026-11-10, fanfare: 2)

![Four crypto agents' price movements and shared portfolio dynamics over time with exposure metrics.](https://www.ml-quant.com/issues/2026-11-13/figures/arxiv-27-8994ef9d.png)

__[Bayesian RL for Mean-Variance Portfolio Optimization](https://arxiv.org/abs/2611.10759)__: A continuous-time reinforcement learning framework using Thompson Sampling balances wealth and information gain, with exploration endogenously tied to posterior uncertainty. (2026-11-10, fanfare: 2)

![The learned parameters and average reward along one sample trajectory ( \mu=0.3 , \sigma=0.1 )](https://www.ml-quant.com/issues/2026-11-13/figures/arxiv-28-8a76d22d.png)

__[Neural Networks for Optimal Liquidation Maps](https://arxiv.org/abs/2611.14245)__: Neural networks approximate the solution map of optimal execution under transient price impact, reducing financial costs from kernel misspecification. (2026-11-11, fanfare: 2)

![Finite-dimensional encoding of admissible kernels, with all other inputs of the solution map held fixed and omitted for](https://www.ml-quant.com/issues/2026-11-13/figures/arxiv-29-eba22641.png)

__[THEATRON Benchmark For LLM Persona Persistence](https://arxiv.org/abs/2611.11487)__: A financial-replay benchmark reveals that persona conditioning shifts LLM behavior but does not reliably produce stable, coherent persona-specific policies. (2026-11-10, fanfare: 1)

![Overview of the Theatron benchmark pipeline](https://www.ml-quant.com/issues/2026-11-13/figures/arxiv-30-dae2fac1.png)

## SSRN

__[Evidence Audit of 91 LLM Trading Papers](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7687481)__: Pre-registers and audits 91 LLM trading papers, finding 51% ignore trading costs, median test window is 12 months, and 98% fail to control for look-ahead bias in model training. (2026-11-13, fanfare: 4)

__[Code-Paper Discrepancies in Finance Research](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7681378)__: A systematic audit of 1,820 economics and finance papers finds 10% of claims show discrepancies between code and description, with 49% containing potentially serious errors affecting central results. (2026-11-09, fanfare: 4)

__[Pre-Registered Tests on QQQ Option Chains](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7687260)__: Tests 15 pre-registered option strategies and published signals on Nasdaq-100 options with real execution costs, finding none produce tradable edges after correcting for multiple comparisons. (2026-11-12, fanfare: 4)

__[Frozen Protocol for Trading Strategy Validation](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7687160)__: Describes a pre-registered protocol using sequentially adjusted significance levels to judge trading strategies, applied to two Euro Stoxx 50 futures strategies that both fail strict statistical tests. (2026-11-12, fanfare: 4)

__[Agentic Search for Stochastic Discount Factors](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7687261)__: Automates asset pricing research using agents to propose and test stochastic discount factors, finding one with out-of-sample Sharpe ratio of 3.38 and 0.86 rank correlation across periods. (2026-11-13, fanfare: 4)

__[Pricing Ability of Nontraded Factors](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7686448)__: Robust econometric methods reduce the fraction of 102 nontraded factors classified as priced from 39% to 4%-13%, suggesting prior tests overstated their pricing ability. (2026-11-13, fanfare: 4)

__[Sustainability Information and Returns](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7683171)__: Machine learning finds no robust evidence that sustainability characteristics predict stock returns after multiple-testing corrections, contradicting prior research. (2026-11-11, fanfare: 4)

__[Complex Capital Markets Hypothesis](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7687139)__: Models a computational arms race where many weak signals drive return predictability and explain why uncertainty about signal coefficients sustains an equity premium. (2026-11-10, fanfare: 4)

__[AI Proxy Advice for Corporate Governance](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7683760)__: An AI-based proxy advisor using firm context and text embeddings beats traditional advisors by 6 to 7 percentage points, earning 10.3% annualized alpha on contested board decisions. (2026-11-12, fanfare: 4)

__[AI Compute Finance Network Risk](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7684118)__: The study maps the financial network supporting AI buildout, revealing concentrated debt exposures and network propagation risks that could cause losses reaching $16.87 trillion in a low-productivity state. (2026-11-10, fanfare: 4)

__[ChatGPT Reduces Analyst Forecast Diversity](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7681797)__: Following ChatGPT's release, analyst forecast divergence fell sharply and AI-assisted reports show stronger textual similarity, reducing information diversity in markets. (2026-11-09, fanfare: 4)

__[Quantitative Easing Effects on House and Stock Prices](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7681599)__: A 10 basis point QE-type surprise raises real house prices 3% in the US and 1.6% in the euro area, lifts technology stocks, and shifts wealth toward the middle class without raising stock indices broadly. (2026-11-09, fanfare: 4)

__[LLMs in Asset Pricing: A Literature Review](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7683739)__: Surveys applications of large language models to return prediction, risk measurement, and investor beliefs from financial text, highlighting look-ahead bias and evaluation pitfalls in existing research. (2026-11-10, fanfare: 3)

__[Agentic AI for Quant Research Deployment](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7683994)__: A practitioner's guide proposes a reference architecture for deploying agentic AI systems in quantitative research, balancing flexibility and control through two reasoning components around a deterministic execution core. (2026-11-13, fanfare: 3)

__[Commodity Bubbles and Managed Money Trends](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7681827)__: The study tracks commodity traders through explosive price episodes and finds managed money follows trends but stops buying as long-horizon trends strengthen, suggesting extrapolation does not amplify bubbles. (2026-11-09, fanfare: 3)

__[Margin Requirements and Futures Market Liquidity](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7691499)__: Using exogenous variation in maintenance margins across futures, the research finds higher margins reduce trading volume by 2.67%, cut liquidity by 7.51%, and raise volatility by 2.59%. (2026-11-11, fanfare: 3)

__[Tail Distributions Improve Portfolio Selection](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7685651)__: Incorporating upper and lower tail information from machine learning models increases Sharpe ratios for long-short equity portfolios across ten prediction models from 1992 to 2024. (2026-11-11, fanfare: 3)

![Performance and Joint Risk of Directional Tail-Based Stock Selection](https://www.ml-quant.com/issues/2026-11-13/figures/ssrn-17-ed808ccc.png)

__[Optimal Portfolio Market Making Theory](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7682027)__: The research derives a closed-form lower bound on trading costs that depends only on return and order-flow covariances, showing that Kyle's lambda achieves optimality for multi-asset liquidity provision. (2026-11-09, fanfare: 3)

__[Treasury Buybacks and Dealer Inventories](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7687299)__: Data on Treasury buybacks reveal dealers reduce inventories in targeted maturity segments, with larger declines among specialists and balance-sheet-constrained dealers facing binding leverage constraints. (2026-11-10, fanfare: 3)

__[Risk Premium Neglect in Asset Forecasts](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7686101)__: Professional forecasters systematically overestimate Treasury yields and stock returns when expecting monetary tightening, neglecting how policy changes alter the price of risk. (2026-11-12, fanfare: 3)

__[Long-Run Rates and the Carry Trade](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7684526)__: Research shows that sorting currencies on long-run interest-rate differentials yields a carry strategy with higher Sharpe ratios and significant alpha versus traditional carry. (2026-11-11, fanfare: 3)

__[Non-Standard Errors in Risk Capital](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7686542)__: The paper quantifies that reasonable modeling choices for Value-at-Risk and Expected Shortfall create capital gaps of a third between identical portfolios at different banks. (2026-11-12, fanfare: 3)

__[Model Portfolios and Fund Competition](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7681563)__: Centralized model portfolios buffer fund flows but weaken incentives for active management, reducing performance persistence and dampening rewards for outperformance. (2026-11-09, fanfare: 3)

__[Random-Walk Benchmark for Bond Returns](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7682408)__: A yield-curve slope measure based on random-walk beliefs outperforms the expectations hypothesis in forecasting Treasury returns and economic value out of sample. (2026-11-09, fanfare: 3)

__[Factor Investing in Financial Institutions](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7682039)__: Financial institutions excluded from factor studies show meaningful factor premiums; including them with adapted definitions improves risk-adjusted portfolio returns. (2026-11-09, fanfare: 3)

__[Physical Climate Risk in U.S. Equities](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7682607)__: A scenario-analysis framework reveals strong cross-state and cross-sector heterogeneity in climate risk, with portfolio-level valuations declining roughly 4.0% under current policy. (2026-11-09, fanfare: 3)

__[Fund Differentiation Narrative vs. Portfolio](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7682021)__: Narrative distinctiveness drives fees and flows while portfolio uniqueness predicts future alpha, suggesting fund prospectuses serve marketing while holdings signal performance ability. (2026-11-09, fanfare: 3)

__[Tariff Announcement Dollar Depreciation](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7684058)__: The dollar depreciated after U.S. tariff announcements not from treasury flight but from equity rotation from domestic to foreign stocks, revealed by fund flow analysis. (2026-11-13, fanfare: 3)

__[Term Premia and Bond Convexity Trading](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7687582)__: The paper derives term premia and convexity from rebalancing strategies rather than theory, showing that volatility smile materially affects convexity value and linking the result to empirical swap estimates. (2026-11-13, fanfare: 2)

__[Polymarket Calibration Across Horizons](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7683871)__: A sample of 1,500 sports and politics prediction markets shows prices are well calibrated with no favourite-longshot bias, though calibration slightly worsens seven days before events. (2026-11-13, fanfare: 2)

## RePEc

__[Asymmetric Idiosyncratic Return Reversal](https://econpapers.repec.org/RePEc:nbr:nberwo:35840)__: Positive residual stock shocks reverse slower than negative shocks, and this asymmetric reversal explains short-term predictability better than idiosyncratic volatility alone. (2026-11-09, fanfare: 4)

![Residual reversal timing and monthly aggregation weights](https://www.ml-quant.com/issues/2026-11-13/figures/repec-01-7a1e5305.png)

__[Debt Maturity Term Premium Inflation](https://econpapers.repec.org/RePEc:nbb:reswpp:202610-498)__: Lengthening government debt maturity raises term premia and affects inflation differently under monetary versus fiscal dominance, with lasting price-level consequences. (2026-11-09, fanfare: 4)

![Nine-panel grid showing impulse responses to maturity shock under monetary dominance](https://www.ml-quant.com/issues/2026-11-13/figures/repec-02-ae6c1dab.png)

__[Government Bond Risk Return Correlation](https://econpapers.repec.org/RePEc:nbr:nberwo:35973)__: Expected excess returns on government bonds correlate 67% with bond-stock betas and account for more than half of long-term yield moves since the mid-1980s. (2026-11-09, fanfare: 3)

![Scatter plot showing strong positive correlation between bond-stock betas and expected excess returns](https://www.ml-quant.com/issues/2026-11-13/figures/repec-03-eb39414b.png)

__[Post-Covid Inflation Driven by Demand](https://econpapers.repec.org/RePEc:nbr:nberwo:35980)__: Structural VARs and a DSGE model establish that post-pandemic inflation in the US and Euro Area was predominantly driven by strong demand from fiscal expansion and accommodative policy. (2026-11-09, fanfare: 3)

![Stacked area charts decomposing US GDP and CPI shocks into demand, supply, and other components.](https://www.ml-quant.com/issues/2026-11-13/figures/repec-04-c842167c.png)

__[Monetary Transmission Through Banks and Non-Banks](https://econpapers.repec.org/RePEc:boe:boeewp:023907)__: A DSGE model reveals that interest-rate policy transmission to output remains robust as non-bank finance grows, but quantitative easing effectiveness depends critically on non-bank sector composition. (2026-11-09, fanfare: 3)

![Macroeconomic responses to QE showing output, consumption, investment, inflation, and credit effects across scenarios.](https://www.ml-quant.com/issues/2026-11-13/figures/repec-05-7387e5fd.png)

__[Team Versus Solo Fund Managers](https://econpapers.repec.org/RePEc:jau:wpaper:2026/12)__: Teams outperform solo managers by 11.5 basis points during outflows through threat-rigidity cohesion, but underperform by 36 basis points during inflows due to social loafing. (2026-11-09, fanfare: 3)

![Team versus individual fund manager AUM over time, showing divergent performance trends.](https://www.ml-quant.com/issues/2026-11-13/figures/repec-06-3bf5cf93.png)

__[Passive Investing Diversification Risk](https://econpapers.repec.org/RePEc:bon:boncrc:crctr224_2025_793)__: Passive ownership raises return correlations and reduces achievable portfolio diversification by 2.1%, shifting risk from firm-specific to market-wide components. (2026-11-12, fanfare: 3)

![Decomposition of diversification benefits and risk parity across passive ownership levels, showing correlation and marginal risk effects.](https://www.ml-quant.com/issues/2026-11-13/figures/repec-07-cb16f822.png)

__[Investment Funds Geopolitical Risk](https://econpapers.repec.org/RePEc:een:camaaa:2026-88)__: Investment funds buy depressed stocks of geopolitically exposed firms seeking yield, stabilizing valuations during shocks while raising portfolio concentration. (2026-11-02, fanfare: 3)

![Distribution of t-values for randomizing the exposure variable](https://www.ml-quant.com/issues/2026-11-13/figures/repec-08-f2cf7033.png)

__[AI Innovation and Stock Returns Globally](https://econpapers.repec.org/RePEc:ces:ceswps:_13112)__: The study finds that AI patenting activity predicts higher stock market returns across 61 countries, with strongest effects in the EU, UK, and G7 economies. (2026-11-11, fanfare: 3)

__[Reddit Pathways to Stock Market Entry](https://econpapers.repec.org/RePEc:ajk:ajkdps:431)__: Tracking 6.8 million Reddit users reveals distinct entry routes to stock investing from budgeting, crypto, or gambling forums, with composition predicting market price and volume reactions. (2026-11-09, fanfare: 2)

![Entry activity by user type showing relative share over months around market entry](https://www.ml-quant.com/issues/2026-11-13/figures/repec-10-1d38495d.png)

__[Competition in Generative AI Markets](https://econpapers.repec.org/RePEc:nbr:nberwo:35953)__: Analysis of 114 billion consumer transactions finds only 2.9% of Americans pay for AI, with low switching costs and retention rates of 20-45%, suggesting strong competitive pressure ahead. (2026-11-02, fanfare: 2)

![AI subscription retention rates declining over 13 months, comparing multiple platforms and services.](https://www.ml-quant.com/issues/2026-11-13/figures/repec-11-cf895bee.png)

__[SBA Disaster Loans and Employment](https://econpapers.repec.org/RePEc:cen:wpaper:26-58)__: A regression discontinuity design shows that SBA disaster lending preserved 9.8% of jobs in affected counties, operating primarily through reduced firm exit rather than new hiring. (2026-11-12, fanfare: 2)

![County-level efects of natural disasters](https://www.ml-quant.com/issues/2026-11-13/figures/repec-12-5f071e7a.png)

__[Unconventional Monetary Policies and Inequality](https://econpapers.repec.org/RePEc:nbb:reswpp:202610-504)__: A heterogeneous-agent model shows quantitative easing and liquidity facilities outperform operation twist at the zero bound and increase wealth inequality most through portfolio shifts. (2026-11-09, fanfare: 3)

![QE effects on consumption, asset inequality, and income inequality across quarters](https://www.ml-quant.com/issues/2026-11-13/figures/repec-13-a5a2731c.png)

__[Arbitrage-Aware Volatility Surface Generation](https://econpapers.repec.org/RePEc:hal:journl:hal-05804110)__: A latent flow-matching framework generates implied volatility surfaces enforcing no-arbitrage constraints and closely reproduces empirical distributions with 90.8% surface validity. (2026-11-11, fanfare: 2)

__[Fed Policy Prediction Markets Expectations](https://econpapers.repec.org/RePEc:nbr:nberwo:35968)__: Prediction market data on macroeconomic outcomes show Fed rate hikes affect expectations through standard transmission channels, not through an information effect. (2026-11-09, fanfare: 2)

![Forecast Errors: Kalshi vs](https://www.ml-quant.com/issues/2026-11-13/figures/repec-15-2913250c.png)

__[Trading Pauses Market Quality TSE](https://econpapers.repec.org/RePEc:osk:wpaper:2603r)__: Single-stock trading pauses on the Tokyo Stock Exchange reduce subsequent volatility by roughly a quarter by allowing aggressive limit orders to offset initial imbalances. (2026-11-09, fanfare: 2)

![Order flow composition during trading pauses showing limit orders offset aggressive market orders.](https://www.ml-quant.com/issues/2026-11-13/figures/repec-16-9191b9e3.png)

__[Fixed-Floating Agency Debt Mispricing](https://econpapers.repec.org/RePEc:nbr:nberwo:35977)__: Fixed-floating interest rate parity violations in agency debt are driven by money market fund demand for floating notes rather than arbitrageur constraints. (2026-11-09, fanfare: 2)

__[Euro-Area Public Debt Decomposition](https://econpapers.repec.org/RePEc:nbb:reswpp:202610-497)__: Analyzing security-level data shows low real rates cut debt ratios substantially, while inflation surprises mattered little, and central-bank holdings initially reduced but later raised consolidated debt. (2026-11-09, fanfare: 2)

![Four panels showing macroeconomic contributions to Euro-area debt dynamics across countries over time](https://www.ml-quant.com/issues/2026-11-13/figures/repec-18-876ae25d.png)

__[Household Sector Inflation Uncertainty](https://econpapers.repec.org/RePEc:awi:wpaper:0772)__: The study links price dispersion in food and gasoline to household uncertainty and shows contractionary policy compresses this uncertainty, suggesting optimal policy should vary by sector. (2026-11-09, fanfare: 2)

__[Macroeconomic Tail Risk Forecasting](https://econpapers.repec.org/RePEc:awi:wpaper:0771)__: Incorporating professional forecasts into GARCH models improves predictions of joint tail risks for growth, inflation, and unemployment, enabling better scenario-based risk assessment. (2026-11-09, fanfare: 2)

__[Sovereign-Bank Nexus in Post-Crisis Europe](https://econpapers.repec.org/RePEc:fip:fedgif:103994)__: The link between sovereign and bank risk remains economically meaningful for less-capitalized banks with large sovereign holdings, especially during market stress and in fiscally constrained countries. (2026-11-09, fanfare: 2)

![Sovereign and bank risk remain closely linked in Europe](https://www.ml-quant.com/issues/2026-11-13/figures/repec-21-7d83b368.png)

__[Multi-Factor Equity Strategy with AI](https://econpapers.repec.org/RePEc:cfi:fseres:cf632)__: Combining a five-factor model with gradient boosting and technical indicators for hedging, the strategy outperforms index buy-and-hold on Japanese stocks while maintaining interpretability. (2026-11-09, fanfare: 2)

![Architecture diagram showing five-factor model inputs through gradient boosting to expected returns and portfolio ranking.](https://www.ml-quant.com/issues/2026-11-13/figures/repec-22-61e0f367.png)

__[ECB Sentiment on Non-Bank Finance](https://econpapers.repec.org/RePEc:ecb:ecbwps:20263304)__: A fine-tuned language model reveals the ECB holds predominantly negative views of non-banks since the financial crisis, driven by stability and regulatory concerns. (2026-11-12, fanfare: 2)

![Clusters of Positive and Negative Topics Discussed in ECB-NBFI Per- ception Index over Time](https://www.ml-quant.com/issues/2026-11-13/figures/repec-23-3379a503.png)

__[Forecasters' Narratives on Inflation and Stocks](https://econpapers.repec.org/RePEc:ces:ceswps:_13076)__: Professional forecasters hold heterogeneous narratives about inflation's impact on stocks, causing disagreement in expectations and divergent portfolio choices despite common signals. (2026-11-02, fanfare: 2)

![Distribution of the forecasters’ narratives relating inflation to DAX returns in the Decem- ber 2022 survey](https://www.ml-quant.com/issues/2026-11-13/figures/repec-24-7d079b84.png)

__[Digital Finance and Bank Performance in Africa](https://econpapers.repec.org/RePEc:agd:wpaper:26/053)__: Digital financial inclusion boosts bank profitability across Sub-Saharan Africa but shows weaker effects on stability, with impact concentrated in high-rate, low-inflation environments. (2026-11-09, fanfare: 2)

__[Exchange Rate Depreciation and Macroeconomics](https://econpapers.repec.org/RePEc:cdl:econwp:qt5wq6v1rf)__: Currency depreciations relative to the dollar stimulate borrowing from abroad and economic growth for peggers, driven by lower uncovered interest parity deviations rather than expenditure switching. (2026-11-01, fanfare: 2)

__[Dutch Housing Appraisals Track Prices](https://econpapers.repec.org/RePEc:tin:wpaper:20260086)__: Analysis of Dutch residential appraisals shows they copy realized transaction prices rather than provide independent valuations, raising collateral risk for borrower and lender. (2026-11-09, fanfare: 2)

![Copula dependence structures](https://www.ml-quant.com/issues/2026-11-13/figures/repec-27-44d1e958.png)

__[Behavioral Finance at Forty](https://econpapers.repec.org/RePEc:nbr:nberwo:35959)__: A retrospective reviews behavioral finance's progress over four decades, covering earnings beliefs, return beliefs, prospect theory, and emerging directions in cognitive foundations and AI. (2026-11-09, fanfare: 2)

__[High-Dimensional Multivariate Stochastic Volatility Estimation](https://econpapers.repec.org/RePEc:cir:cirwor:2026s-20)__: Proposes computationally efficient estimators for multivariate stochastic volatility models using moment equations and shrinkage methods, avoiding numerical optimization while maintaining reliability. (2026-11-02, fanfare: 1)

![Rejection-frequency curves for the common C-space test under monotone deviations indexed by κ](https://www.ml-quant.com/issues/2026-11-13/figures/repec-29-e71e9da8.png)

__[Bank Runs Drive Gold Price Volatility](https://econpapers.repec.org/RePEc:pre:wpaper:202637)__: Accounting for bank runs in GARCH models improves in-sample and out-of-sample performance for modeling gold price returns and volatility using annual data since 1694. (2026-11-09, fanfare: 2)

![Histogram of real gold returns](https://www.ml-quant.com/issues/2026-11-13/figures/repec-30-d70223d5.png)