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#Risk Management
20 OctTue
  1. arXiv q-fin31

    Optimal Loss Allocation and Reflected Free Boundaries in Systemic Risk

    This study focuses on the allocation of bank default losses without external capital injection, proving that for all non-decreasing concave terminal payoffs, the "tax the richest" truncation rule is optimal. The study constructs a continuous-time optimal solution through time discretization, characterizes the optimal flow as a novel reflected free boundary problem, and proves the positive time smoothness of the free boundary and the density of surviving banks.

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  2. arXiv q-fin67

    Conformal Kelly: Why Economic Performance Fails to Materialize When Marginal Coverage Targets Are Met

    The paper proposes using the half-width of conformal prediction intervals as a scaling measure for fractional Kelly position sizing with a leverage cap, and tests this rule under a pre-registered protocol. During the development phase, an autonomous LLM agent searched approximately 200 configurations on a 2016-2021 sample of 8 U.S. equity ETFs, with subsequent data sealed before the search, and the final configuration, 9 comparison portfolios, and conclusion mapping all registered before unsealing.

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  3. arXiv q-fin34

    Coherent Risk Research under Finite Models

    This research in quantitative financial risk management proves that when estimates remain unchanged after replicating all scenarios, binary estimators can accurately describe the same population risk and recover the population dual set from finite models. The study derives Wasserstein, Kolmogorov, and total variation norms under bounded support based on Bernoulli loss risk, characterizes uniform estimability, provides impossibility bounds for arbitrary stochastic estimators, and further derives uniform regret bounds for data-driven risk-averse optimization, with applications in stop-loss retention scenarios.

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  4. arXiv q-fin33

    On the Utility Problem in Markets with Instantaneous Price Impact

    For a discrete-time financial market model with trades having instantaneous price impact, the existence of a solution to the corresponding utility maximization problem is proven. This study removes the unnatural restrictions on market depth and resilience processes present in earlier work, with the non-standard feature of this problem being that the set of attainable portfolio values may not satisfy convexity.

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  5. arXiv q-fin53

    Exact Risk Calculation for Candidate Scoring Decision Models: Analysis of the Impact of Teacher Supervision, Fixed Representations, and Calibration

    A new arXiv paper uses Gaussian surrogate models to precisely calculate the finite-sample risk of candidate scoring decision models, analyzing the boundaries of the role of three training elements: teacher soft targets, frozen pre-trained backbones, and temperature calibration. The paper points out that after a student model inherits teacher bias, more student data cannot eliminate that bias; fixed representations only reduce risk below an explicit sample size threshold; under the probit model, exact recalibration maps to temperature scaling, and can only eliminate all excess risk when the direction of the teacher's coefficients is correct.

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  6. arXiv q-fin64

    arXiv Paper: The Control Layer Behind Rule-Following Trading Agents Across Cross-Market Quotes

    This arXiv paper tests the degree to which 4 AI trading agents adhere to their own strategies and risk rules, based on 2627 closed trades from February 12 to August 27, 2026. 18% of the trades were live orders on Hyperliquid, while the rest were simulated at real-time prices, with a uniform round-trip fee of 0.09%.

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  7. arXiv q-fin57

    Conditional Weight of Evidence for Gradient Boosting Credit Models: Estimation Methods, Additivity, and Their Relationship with Adverse Behavior Causes

    This arXiv paper proposes a conditional weight of evidence method to measure the marginal impact of a single credit feature on the log odds of default, given the rest of an applicant's credit information. The same feature value can yield different evaluations for applicants with different credit backgrounds.

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  8. arXiv q-fin38

    A Dynamic Programming Approach for Value-at-Risk Markov Decision Processes in Continuous Spaces

    This study is the first to prove the validity of dynamic programming equations for Value-at-Risk Markov Decision Processes (VaR MDPs) in continuous spaces, covering both finite-horizon and infinite-horizon scenarios. Previously, related dynamic programming equations had only been derived and verified in finite state and action spaces, leaving the continuous-space problem unresolved for a long time. This framework can be used to characterize optimal dynamic strategies for optimal execution problems with VaR objectives, solving an open problem in the field.

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  9. arXiv q-fin55

    Probability Weighting Functions for Normal Tempered-Stable Distributions: Orthogonal Channels and Information-Theoretic Indices

    This arXiv paper proposes constructing probability weighting functions from changes in return distributions rather than parameter families, with the formula w(p)=F_post(F_prior^{-1}(p)). Both the prior and posterior are normal tempered-stable distributions, and the posterior differs from the prior along three independent channels—scale, skewness, and tail thickness—under the premise of fixed variance.

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  10. arXiv q-fin55

    Amortized Neural Filtering for Calibrating Posteriors in Statistical Arbitrage

    The paper proposes a Neural Filter Estimator (NFE), trained on simulated paths of mean-reverting processes, that outputs the posterior distribution of the implied state for each spread in the cross-section in a single forward pass, incorporating estimation uncertainty into trading decisions. On simulated paths with known ground truth, the method achieves accuracy comparable to other practical estimators, with its advantage lying in correctly characterizing its own uncertainty.

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  11. arXiv q-fin45

    No-Arbitrage Implied Volatility Surface Reconstruction via Additive Martingales

    This paper proposes AddMart, a few-shot implied volatility surface reconstruction framework that embeds no-arbitrage structure into the architecture to achieve global static no-arbitrage, enabling continuous and valid surface generation from a small number of option quotes. Experiments on 10 real-world option datasets show that the framework delivers competitive reconstruction accuracy with no significant static arbitrage violations, and can be directly applied to VaR, CVaR and crash probability estimation.

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  12. arXiv q-fin37

    The value of optimal trading filters and predictive signals

    This paper addresses the stationary quadratic trading problem with transient price impact, deriving optimal trading strategies using predictive theory tools, expressed as a combination of conditional predictions and explicit causal linear filters. The paper recovers the Gârleanu-Pedersen target portfolio under the exponential elasticity assumption, derives fractional trading filters under power-law impact decay, and clarifies the mechanisms determining the net realizable value of predictive signals under different trading cost scenarios.

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  13. arXiv q-fin34

    Certifiable Neural Operators for Simultaneously Solving Infinitely Many Convex Programs: Logarithmic Rank and Linear Complexity

    This arXiv paper proposes proximal equilibrium operators (PEOs), constructed via finite-dimensional deep equilibrium layers, that can solve a family of convex optimization problems on separable Hilbert spaces. The paper proves that when input loss functions lie in a suitable infinite-dimensional set, PEOs can uniformly approximate corresponding solutions to arbitrary precision, with rank and width growing only logarithmically with the inverse of approximation error, and depth growing linearly. The related theory and trainability have been validated across three application domains: nonlinear PDEs, stochastic optimal control, and financial portfolio optimization and hedging.

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19 OctMon
  1. CFA Institute Research and Policy Center: Publications54

    A veteran former fund manager used AI to test his lifelong investment strategy

    A former equity strategy partner and independent investment firm operator used AI in retirement to backtest his long-standing personal strategy, which combined Peter Lynch's growth stock selection, Hilary Kramer's undervalued obscure stock selection, and earnings revision discipline. He corrected for survivorship bias, used point-in-time data, calculated pre-fee returns, and compared it against random stock selection from the same investment pool. The results showed that the strategy did not outperform random selection, with ordinary index funds performing better than the strategy.

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  2. arXiv q-fin47

    AI Agents in Automated Markets

    The research team built behavioral models for three types of trading agents—rational arbitrageurs, manipulators, and retail momentum followers—based on reinforcement learning, and conducted a pilot study on hourly cryptocurrency data (AAVE, ETC, SUI). The results show that the behavioral characteristics of different agents have differentiated impacts on market pressure, liquidity, volatility, and risk-adjusted returns. The research conclusions support formulating differentiated regulatory rules for different behavioral types, rather than uniformly restricting volatility or turnover rates.

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  3. arXiv q-fin47

    Study on the Impact of Shared Action Ordering on Executed Credit for LLM Borrowers in a Simulated Lending Market

    An experiment on a simulated collateralized lending market shows that shared action ordering significantly affects the scale of executed credit for LLM borrowers. The experiment set up 12 ordinary order markets and 12 balanced order markets, using an exact randomization test to obtain a two-sided p-value of 2/8100, with a credit difference of 1.12 units of initial unused reference borrowing capacity between leverage-prioritized and deleveraging-prioritized ordinary order markets. No liquidations or actual bad debts occurred in all 24 markets; under the fixed-price 30% shock benchmark, most write-offs came from collateral consumed by liquidation bonuses.

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16 OctFri
15 OctThu
  1. Hong Kong Monetary Authority: Press Releases35

    HKMA launches "ART" framework to promote responsible AI adoption in the banking sector

    The HKMA launched the "ART" framework on October 15, centered on the core principles of Accountability, Resilience, and Trust, to support the banking sector in responsibly adopting artificial intelligence. The framework includes the release of the latest regulatory guidelines on responsible AI adoption, the joint establishment of an AI training data sharing platform, and the advancement of agentic AI application testing. Concurrently, the HKMA and Cyberport co-hosted a two-day "Finance + AI Forum," marking the successful completion of the second phase of the Generative AI Sandbox technical testing.

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14 OctWed
  1. Moontower63

    Key Takeaways from the Interview with LTCM Co-Founder Victor Haghani: Position Sizing Matters More Than Instrument Selection

    Moontower host Kris Abdelmessih breaks down an interview with LTCM co-founder Victor Haghani, arguing that investment decisions consist of two parts: instrument selection and position sizing, and that LTCM's failure stemmed from flawed position sizing rather than poor instrument selection.

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  3. Google DeepMind33

    How Google DeepMind Is Developing Gemini 4 Argon Responsibly and Safely

    Google DeepMind is advancing the widespread deployment of Gemini 4 Argon while expanding the coverage of trusted cyber defenders in the Fairwind Program. The model solved all 12 scenarios in end-to-end simulated cyberattack tests, a significant improvement from 5 in the previous generation. Its refusal rate for violating requests nearly doubled compared to the previous generation, and violations accounted for only 2% of attempts in coding benchmark tests.

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13 OctTue
  1. CFA Institute Research and Policy Center: Publications49

    How Investors Can Evaluate AI Heat Risk Models Used in Company Valuations

    When evaluating AI heat risk models used in company valuations, investors should focus on whether they fully cover the entire transmission chain from heat hazards to asset exposure, operational sensitivity, financial impact, and ultimately valuation. Existing heat risk estimates from different institutions show significant discrepancies and often omit key impact dimensions such as employee productivity. Investors can reference the case of Électricité de France (EDF) and require companies to disclose quantifiable operational thresholds, financial sensitivities, and adaptation costs to determine whether heat-related losses are already reflected in company value.

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  2. Robot Wealth51

    Core Questions That Change Trading Mindset: Exploring the Advantages of Independent Traders and Starting Approaches

    Author Kris Longmore shares content from his 72-minute interview on The Algorithmic Advantage podcast, looking back on an early experience at a Sydney traders' meetup where he was unable to answer a proprietary trader's question about his own trading edge. He later joined that proprietary firm as a researcher and partner, and summarized relevant insights after leaving to trade independently.

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12 OctMon
  1. CFA Institute Research and Policy Center: Publications41

    Where Have All the Portfolio Managers' LLMs Gone?

    Most portfolio managers and analysts in the investment industry have not yet adopted large language models in their daily work, with only a few well-resourced large institutions having implemented them. The article notes that LLMs can improve investment decision hit rates and long-term productivity, and that current barriers to adoption stem primarily from non-technical factors such as organizational processes, incentive structures, and psychological safety.

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  2. AQR: Research38

    Misconceptions About Tax-Aware Long-Short Strategies: The Misleading Analogy to Direct Indexing (Part 3)

    AQR has released the third part of its research series on tax-aware long-short (TA LS) strategies, clarifying two common misconceptions. As an active investment strategy, TA LS deliberately retains tracking error to express alpha models and capture excess returns and tax deferral benefits, rather than minimizing tracking error like direct indexing. Portfolio construction for TA LS requires balancing expected returns, risk, costs, and taxes, rather than simply realizing capital losses or deferring capital gains.

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8 OctThu
  1. Alpha Architect69

    How Tail Risk Projects Survive the Waiting Period: From Two Failure Cases to an Eight-Step Due Diligence Framework

    This article treats the holding costs, behavioral pressures, and governance burdens of tail risk hedging during calm periods as design constraints rather than post-hoc inconveniences. The authors first break down three types of "bleeding" sources: direct holding costs, opportunity costs, and evaluation pressure, and provide a table of cumulative sleeve losses for different annualized carry rates over 3 to 15 years, as well as a table of portfolio-level annual drag converted by sleeve weight.

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7 OctWed
  1. Moontower58

    How to Express a Specific View Using Vertical Spreads: A Practical Review of an Options Trade Around Nike’s Earnings

    In a trading lab at the Robinhood Summit, the author used Nike’s (NKE) after-hours earnings release on October 1, 2025, as a backdrop to demonstrate how to express a specific directional view using vertical spreads. At the time, NKE traded around $36, and the $36 straddle implied an expected move of about $3 (8%). The author chose to buy the 38.5/39 call vertical for a premium of $0.12, which would pay $0.50 if the stock closed above $39 at expiration, for a risk-reward ratio of roughly 3:1.

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5 OctMon
  1. Alpha Architect59

    Buying complex retail investment products may not be a good idea: Interpretation of a paper in the Review of Finance

    Alpha Architect interprets the paper "Competition, complexity, and security design: evidence from retail investment products" published in the Review of Finance in 2026. This study takes yield-enhancement products in the Swiss market as a sample and finds that issuers deliberately increase product complexity to weaken price competition when competition intensifies.

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4 OctSun
  1. Moontower51

    Moontower Recaps Robinhood Summit Options Education: Kelly Criterion, Directional Value of Options, and Analysis of Unusual INTC Options Flow

    The author participated in an options buying education session and a roundtable for trading platform data providers at the Robinhood 2026 Summit in Houston, recapping the Kelly Criterion supporting course, the profit and loss comparison logic of buying options versus holding the underlying stock, and the analytical approach for unusual INTC options flow.

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1 OctThu
  1. Quantocracy31

    Quantocracy Latest Quantitative Research Link Roundup for September 30, 2026

    Quantocracy released its latest roundup of quantitative research links on September 30, 2026, covering areas such as quantitative strategy replication, machine learning cross-sectional factors, quantitative trading podcast analysis, and correlation and volatility research. This includes the launch of the Quantpedia Awards 2027 competition, a replication of the intraday momentum strategy proposed by Gao et al. in 2018, and a systematic summary of the 335th episode of a quantitative trading podcast.

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27 SeptSun
  1. Moontower62

    Moontower Derives the Optimal Kelly Bet Size from First Principles and Discusses AI-Generated Content and 'Thickness'

    Moontower author Kris Abdelmessih released this newsletter, first sharing Adam Mastroianni's article on 'thickness', distinguishing between deep works that require attention to extract value and content-free AI-generated 'slop', then step-by-step deriving the optimal Kelly bet size formula from first principles.

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21 SeptMon
  1. Bridgewater: Research & Insights53

    Bridgewater Research: The Impact of Carbon Pricing on Global Industries is Quietly Expanding

    Bridgewater released a research report pointing out that current carbon pricing mechanisms cover approximately 30% of global emissions, mainly dominated by the emissions trading systems of the European Union and China. The EU Carbon Border Adjustment Mechanism (CBAM) is amplifying its impact and promoting the adoption of carbon pricing worldwide.

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15 SeptTue
  1. AQR: Research42

    Will Inflation Make a Comeback? AQR Updates Inflation Sensitivity and Inflation Hedging Analysis

    AQR has updated its previous analysis of inflation sensitivity and inflation hedging, based on over 50 years of market data. While global inflation has fallen rapidly from its 2022 peak, inflation uncertainty persists in major markets, stock-bond correlations are at multi-decade highs, and traditional stock-bond portfolios are vulnerable to simultaneous losses during inflation shocks. Commodities and trend-following strategies perform strongly during inflation upcycles and can serve as inflation diversification tools, with implementation methods significantly affecting long-term returns and macro diversification outcomes.

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8 SeptTue
4 SeptFri
  1. Rob Carver：This Blog is Systematic26

    Rob Carver Completes Final Edits on Fifth Trading Book "The Art And Science of Trading"

    Rob Carver has completed the final edits on his fifth trading book, "The Art And Science of Trading," scheduled for official publication on December 1 and now available for pre-order. Aimed at the mass market, this book serves as the unleveraged counterpart to his third book, "Leveraged Trading." It covers an introduction to trading delta-one instruments such as stocks, ETFs, and cryptocurrencies, explains trading science concepts like backtesting and the Kelly Criterion, and explores the integration of art and science within a semi-automated trading framework.

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3 SeptThu
1 SeptTue
  1. AQR: Research44

    AQR Research on Investing in a Warming World: Analysis of Differences Between Mainstream Portfolio Carbon Metrics and Financed Emissions

    AQR has released the research Investing in a Warming World, noting that mainstream portfolio carbon metrics such as carbon footprint and carbon intensity are easily affected by rising stock market valuations, and may show improvement even when corporate emissions change little, potentially overestimating decarbonization progress when used as time-series measures of decarbonization. Financed emissions are more directly linked to real-world emissions outcomes. The study also explores approaches to constructing climate-oriented portfolios, noting that combining multiple metrics can provide a more comprehensive assessment of climate-related investment progress.

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