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M. 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Börner","Ingo Hoffmann","John H. Stiebel"],"topics":["volatility","market-simulation"],"asset_classes":["equities"],"methods":["econophysics-complexity","simulation"],"paper_type":"theoretical","primary_category":"q-fin.ST","doi":null,"journal_ref":null,"quadrant":"Lab Rats","math_complexity":7.0,"empirical_rigor":2.0,"hub_score":4.0,"code_url":null,"paper_url":"https://arxiv.org/pdf/2303.15164.pdf","page_url":"https://thequant.space/flowcharts/on-the-connection-between-temperature-and-volatility-in-ideal-agent-systems/","tags":["spin systems","agent-based modeling","econophysics","volatility dynamics","Equities"]},{"paper_id":"2303.15216","title":"Robust Risk-Aware Option Hedging","paper_date":"2023-03-27","authors":["David Wu","Sebastian Jaimungal"],"topics":["reinforcement-learning","options-derivatives"],"asset_classes":["derivatives"],"methods":["reinforcement-learning","optimization","deep-learning"],"paper_type":"empirical","primary_category":"q-fin.CP","doi":null,"journal_ref":null,"quadrant":"Holy Grail","math_complexity":7.0,"empirical_rigor":6.5,"hub_score":6.7,"code_url":null,"paper_url":"https://arxiv.org/pdf/2303.15216.pdf","page_url":"https://thequant.space/flowcharts/robust-risk-aware-option-hedging/","tags":["reinforcement learning","policy gradient","robust optimization","barrier options","derivative hedging","Options"]},{"paper_id":"2303.15830","title":"Mean-variance hybrid portfolio optimization with quantile-based risk measure","paper_date":"2023-03-28","authors":["Weiping Wu","Yu Lin","Jianjun Gao","Ke Zhou"],"topics":["risk-management","portfolio-optimization"],"asset_classes":["macro-multi-asset"],"methods":["optimization","stochastic-calculus","econometrics-time-series"],"paper_type":"theoretical","primary_category":"q-fin.PM","doi":null,"journal_ref":null,"quadrant":"Holy Grail","math_complexity":8.5,"empirical_rigor":5.5,"hub_score":6.7,"code_url":null,"paper_url":"https://arxiv.org/pdf/2303.15830.pdf","page_url":"https://thequant.space/flowcharts/mean-variance-hybrid-portfolio-optimization-with-quantile-based-risk-measure/","tags":["spectral risk measure","Value-at-Risk","quantile optimization","martingale representation","portfolio optimization","Multi-Asset"]},{"paper_id":"2303.16012","title":"On the number of terms in the COS method for European option pricing","paper_date":"2023-03-28","authors":["Gero Junike"],"topics":["options-derivatives"],"asset_classes":["derivatives"],"methods":[],"paper_type":"empirical","primary_category":"q-fin.CP","doi":null,"journal_ref":null,"quadrant":"Holy Grail","math_complexity":7.5,"empirical_rigor":5.0,"hub_score":6.0,"code_url":null,"paper_url":"https://arxiv.org/pdf/2303.16012.pdf","page_url":"https://thequant.space/flowcharts/on-the-number-of-terms-in-the-cos-method-for-european-option-pricing/","tags":["Fourier-cosine expansion","COS method","Greeks","option pricing","heavy tails","Options"]},{"paper_id":"2303.16117","title":"Feature Engineering Methods on Multivariate Time-Series Data for Financial Data Science Competitions","paper_date":"2023-03-26","authors":["Thomas Wong","Mauricio Barahona"],"topics":["machine-learning"],"asset_classes":["equities"],"methods":["machine-learning","econometrics-time-series"],"paper_type":"dataset-benchmark","primary_category":"q-fin.ST","doi":null,"journal_ref":null,"quadrant":"Philosophers","math_complexity":3.0,"empirical_rigor":3.5,"hub_score":3.3,"code_url":null,"paper_url":"https://arxiv.org/pdf/2303.16117.pdf","page_url":"https://thequant.space/flowcharts/feature-engineering-methods-on-multivariate-time-series-data-for-financial-data/","tags":["feature engineering","time-series forecasting","predictive modeling","benchmarking","Equities"]},{"paper_id":"2303.16148","title":"Causal Modelling of Cryptocurrency Price Movements Using Discretisation-Aware Bayesian Networks","paper_date":"2023-03-26","authors":["Rasoul Amirzadeh","Asef Nazari","Dhananjay Thiruvady","Mong Shan Ee"],"topics":["crypto-defi","nlp-llm"],"asset_classes":["crypto","macro-multi-asset"],"methods":["bayesian","network-graph","causal-inference","nlp-llm"],"paper_type":"empirical","primary_category":"q-fin.ST","doi":null,"journal_ref":null,"quadrant":"Street Traders","math_complexity":4.5,"empirical_rigor":6.5,"hub_score":5.7,"code_url":null,"paper_url":"https://arxiv.org/pdf/2303.16148.pdf","page_url":"https://thequant.space/flowcharts/causal-modelling-of-cryptocurrency-price-movements-using-discretisation-aware/","tags":["Bayesian networks","discretization","causal modeling","macro-financial indicators","social media signals","Cryptocurrency"]},{"paper_id":"2303.16149","title":"Explaining Exchange Rate Forecasts with Macroeconomic Fundamentals Using Interpretive Machine Learning","paper_date":"2023-03-23","authors":["Davood Pirayesh Neghab","Mucahit Cevik","M. 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Ribeiro"],"topics":["machine-learning","factor-investing"],"asset_classes":["equities"],"methods":["econometrics-time-series","machine-learning","optimization"],"paper_type":"empirical","primary_category":"q-fin.ST","doi":null,"journal_ref":null,"quadrant":"Holy Grail","math_complexity":6.5,"empirical_rigor":8.0,"hub_score":7.4,"code_url":null,"paper_url":"https://arxiv.org/pdf/2303.16151.pdf","page_url":"https://thequant.space/flowcharts/forecasting-large-realized-covariance-matrices-the-benefits-of-factor-models/","tags":["realized covariance matrices","Vector Heterogeneous Autoregressive (VHAR)","LASSO regularization","factor decomposition","minimum variance portfolio","Equities"]},{"paper_id":"2303.16153","title":"Optimal Cross-Correlation Estimates from Asynchronous Tick-by-Tick Trading Data","paper_date":"2023-03-18","authors":["William H. 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distance","covariance matrices","correlation","Multi-Asset"]},{"paper_id":"2503.16470","title":"Ornstein-Uhlenbeck Process for Horse Race Betting: A Micro-Macro Analysis of Herding and Informed Bettors","paper_date":"2025-03-01","authors":[],"topics":["market-simulation","market-microstructure"],"asset_classes":["macro-multi-asset"],"methods":["stochastic-calculus","simulation","econophysics-complexity"],"paper_type":"empirical","primary_category":"physics.soc-ph","doi":null,"journal_ref":null,"quadrant":"Holy Grail","math_complexity":8.5,"empirical_rigor":6.5,"hub_score":7.3,"code_url":null,"paper_url":"https://arxiv.org/pdf/2503.16470v2","page_url":"https://thequant.space/flowcharts/ornstein-uhlenbeck-process-for-horse-race-betting-a-micro-m/","tags":["Betting Markets","Ornstein-Uhlenbeck Process","Agent-Based Modeling","Herding Behavior","Market Microstructure"]},{"paper_id":"2503.16696","title":"Universal approximation property of neural stochastic differential equations","paper_date":"2025-03-20","authors":["Anna P. Kwossek","David J. Prömel","Josef Teichmann"],"topics":["machine-learning"],"asset_classes":[],"methods":["stochastic-calculus","deep-learning","machine-learning"],"paper_type":"theoretical","primary_category":"math.PR","doi":null,"journal_ref":null,"quadrant":"Lab Rats","math_complexity":8.5,"empirical_rigor":1.5,"hub_score":4.3,"code_url":null,"paper_url":"https://arxiv.org/pdf/2503.16696.pdf","page_url":"https://thequant.space/flowcharts/universal-approximation-property-of-neural-stochastic-differential-equations/","tags":["neural stochastic differential equations","universal approximation","Itô diffusion","linear growth constraints","Derivatives / General Quantitative Finance"]},{"paper_id":"2503.16890","title":"Equilibrium with non-convex preferences: some insights","paper_date":"2025-03-21","authors":[],"topics":[],"asset_classes":["macro-multi-asset"],"methods":["optimization","game-theory"],"paper_type":"theoretical","primary_category":"q-fin.CP","doi":null,"journal_ref":null,"quadrant":"Lab Rats","math_complexity":9.0,"empirical_rigor":1.0,"hub_score":4.2,"code_url":null,"paper_url":"https://arxiv.org/pdf/2503.16890v1","page_url":"https://thequant.space/flowcharts/equilibrium-with-non-convex-preferences-some-insights/","tags":["General Equilibrium","Non-Convex Preferences","Demand Correspondence","Market Equilibrium","Utility Functions","General Equilibrium Theory"]},{"paper_id":"2503.16974","title":"Assessing Consistency and Reproducibility in the Outputs of Large Language Models: Evidence Across Diverse Finance and Accounting Tasks","paper_date":"2025-03-21","authors":["Julian Junyan Wang","Victor Xiaoqi Wang"],"topics":["nlp-llm"],"asset_classes":["macro-multi-asset"],"methods":["nlp-llm"],"paper_type":"empirical","primary_category":"q-fin.GN","doi":null,"journal_ref":null,"quadrant":"Street Traders","math_complexity":2.5,"empirical_rigor":8.8,"hub_score":6.28,"code_url":null,"paper_url":"https://arxiv.org/pdf/2503.16974.pdf","page_url":"https://thequant.space/flowcharts/assessing-consistency-and-reproducibility-in-the-outputs-of-large-language/","tags":["Large Language Models","reproducibility","sentiment analysis","text generation","natural language processing","Cross-Asset / General Financial Text"]},{"paper_id":"2503.17103","title":"Martingale property and moment explosions in signature volatility models","paper_date":"2025-03-21","authors":["Eduardo Abi Jaber","Paul Gassiat","Dimitri Sotnikov"],"topics":["volatility"],"asset_classes":["equities","derivatives"],"methods":["stochastic-calculus"],"paper_type":"theoretical","primary_category":"q-fin.MF","doi":null,"journal_ref":null,"quadrant":"Lab Rats","math_complexity":9.0,"empirical_rigor":2.0,"hub_score":4.8,"code_url":null,"paper_url":"https://arxiv.org/pdf/2503.17103.pdf","page_url":"https://thequant.space/flowcharts/martingale-property-and-moment-explosions-in-signature-volatility-models/","tags":["signature volatility model","martingale property","moment explosions","rough volatility","stochastic differential equations","Equities / Options & Derivatives"]},{"paper_id":"2503.17225","title":"China and G7 in the Current Context of the World Trading","paper_date":"2025-03-21","authors":[],"topics":["commodities-energy"],"asset_classes":["macro-multi-asset","fx","commodities-energy"],"methods":["game-theory"],"paper_type":"methodological","primary_category":"q-fin.TR","doi":null,"journal_ref":null,"quadrant":"Lab Rats","math_complexity":8.0,"empirical_rigor":3.0,"hub_score":5.0,"code_url":null,"paper_url":"https://arxiv.org/pdf/2503.17225v1","page_url":"https://thequant.space/flowcharts/china-and-g7-in-the-current-context-of-the-world-trading/","tags":["General Economic Equilibrium","International Trade Model","Trade Balance","Supply-Demand Structure","Equilibrium Price Vector","Commodities/International Trade"]},{"paper_id":"2503.17737","title":"Bayesian Optimization for CVaR-based portfolio optimization","paper_date":"2025-03-22","authors":[],"topics":["risk-management","portfolio-optimization"],"asset_classes":["macro-multi-asset","equities"],"methods":["bayesian","optimization"],"paper_type":"methodological","primary_category":"q-fin.PM","doi":null,"journal_ref":null,"quadrant":"Holy Grail","math_complexity":8.5,"empirical_rigor":6.5,"hub_score":7.3,"code_url":null,"paper_url":"https://arxiv.org/pdf/2503.17737v1","page_url":"https://thequant.space/flowcharts/bayesian-optimization-for-cvar-based-portfolio-optimization/","tags":["Bayesian Optimization","Conditional Value-at-Risk (CVaR)","Portfolio allocation","Constrained optimization","Risk management","Multi-asset"]},{"paper_id":"2503.17778","title":"Heterogeneity of household stock portfolios in a national market","paper_date":"2025-03-22","authors":["Matteo Milazzo","Federico Musciotto","Jyrki Piilo","Rosario N. Mantegna"],"topics":[],"asset_classes":["equities"],"methods":[],"paper_type":"empirical","primary_category":"q-fin.GN","doi":null,"journal_ref":null,"quadrant":"Street Traders","math_complexity":2.5,"empirical_rigor":8.0,"hub_score":5.8,"code_url":null,"paper_url":"https://arxiv.org/pdf/2503.17778.pdf","page_url":"https://thequant.space/flowcharts/heterogeneity-of-household-stock-portfolios-in-a-national-market/","tags":["Herfindahl-Hirschman index","portfolio concentration","retail investors","time evolution","investor demographics","Equities"]},{"paper_id":"2503.17836","title":"Clearing Sections of Lattice Liability Networks","paper_date":"2025-03-22","authors":["Robert Ghrist","Julian Gould","Miguel Lopez","Hans Riess"],"topics":["risk-management","crypto-defi"],"asset_classes":["crypto","macro-multi-asset"],"methods":["network-graph"],"paper_type":"theoretical","primary_category":"q-fin.MF","doi":null,"journal_ref":null,"quadrant":"Lab 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L. Lin","Jerry Yao-Chieh Hu","Paul W. Chiou","Peter Lin"],"topics":["portfolio-optimization"],"asset_classes":["equities","macro-multi-asset"],"methods":["bayesian","optimization","network-graph","machine-learning"],"paper_type":"empirical","primary_category":"q-fin.PM","doi":null,"journal_ref":null,"quadrant":"Holy Grail","math_complexity":8.5,"empirical_rigor":7.0,"hub_score":7.6,"code_url":null,"paper_url":"https://arxiv.org/pdf/2505.02185v1","page_url":"https://thequant.space/flowcharts/latent-variable-estimation-in-bayesian-black-litterman-model/","tags":["Bayesian Black-Litterman","latent variable estimation","portfolio optimization","Bayesian network","closed-form inference","Equity"]},{"paper_id":"2505.02635","title":"Systemic Risk in the European Insurance Sector","paper_date":"2025-05-05","authors":["Giovanni Bonaccolto","Nicola Borri","Andrea Consiglio","Giorgio Di Giorgio"],"topics":["risk-management","insurance-actuarial","fixed-income"],"asset_classes":["insurance","macro-multi-asset"],"methods":["network-graph"],"paper_type":"empirical","primary_category":"q-fin.CP","doi":null,"journal_ref":null,"quadrant":"Holy Grail","math_complexity":7.0,"empirical_rigor":8.0,"hub_score":7.6,"code_url":null,"paper_url":"https://arxiv.org/pdf/2505.02635v1","page_url":"https://thequant.space/flowcharts/systemic-risk-in-the-european-insurance-sector/","tags":["variance decomposition","systemic risk","financial contagion","interconnectedness","stress testing","Insurance"]},{"paper_id":"2505.02678","title":"Why is the volatility of single stocks so much rougher than that of the S&P500?","paper_date":"2025-05-05","authors":["Othmane Zarhali","Cecilia Aubrun","Emmanuel Bacry","Jean-Philippe Bouchaud","Jean-François 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Ma"],"topics":["machine-learning","options-derivatives"],"asset_classes":["derivatives"],"methods":["deep-learning","stochastic-calculus","network-graph"],"paper_type":"theoretical","primary_category":"cs.LG","doi":null,"journal_ref":null,"quadrant":"Lab Rats","math_complexity":8.5,"empirical_rigor":4.0,"hub_score":5.8,"code_url":null,"paper_url":"https://arxiv.org/pdf/2505.03789v2","page_url":"https://thequant.space/flowcharts/a-new-architecture-of-high-order-deep-neural-networks-that-l/","tags":["Deep Learning","Stochastic Differential Equations","High-Order Weak Approximation","Runge-Kutta","Option Pricing","Equities"]},{"paper_id":"2505.03843","title":"Economic Security of Multiple Shared Security Protocols","paper_date":"2025-05-05","authors":["Abhimanyu Nag","Dhruv Bodani","Abhishek Kumar"],"topics":["crypto-defi"],"asset_classes":["crypto"],"methods":["optimization","game-theory"],"paper_type":"theoretical","primary_category":"cs.CR","doi":null,"journal_ref":null,"quadrant":"Lab 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S. Gonchar","O. P. Dovzhyk","A. S. Zhokhin","W. H. Kozyrsky","A. P. 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Tian"],"topics":["risk-management","volatility","machine-learning"],"asset_classes":["macro-multi-asset"],"methods":["simulation","econometrics-time-series"],"paper_type":"empirical","primary_category":"q-fin.RM","doi":null,"journal_ref":null,"quadrant":"Holy Grail","math_complexity":7.0,"empirical_rigor":6.5,"hub_score":6.7,"code_url":null,"paper_url":"https://arxiv.org/pdf/2505.05646v1","page_url":"https://thequant.space/flowcharts/comparative-evaluation-of-var-models-historical-simulation/","tags":["Value-at-Risk","Filtered Historical Simulation","GARCH","risk modeling","tail risk estimation","Market Risk"]},{"paper_id":"2505.05784","title":"FlowHFT: Imitation Learning via Flow Matching Policy for Optimal High-Frequency Trading under Diverse Market Conditions","paper_date":"2025-05-09","authors":["Yang Li","Zhi Chen","Steve 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Marconi"],"topics":["volatility","machine-learning","fixed-income"],"asset_classes":["fx","fixed-income","equities"],"methods":["deep-learning","econometrics-time-series"],"paper_type":"empirical","primary_category":"q-fin.GN","doi":null,"journal_ref":null,"quadrant":"Street Traders","math_complexity":3.5,"empirical_rigor":7.5,"hub_score":5.9,"code_url":null,"paper_url":"https://arxiv.org/pdf/2507.07296.pdf","page_url":"https://thequant.space/flowcharts/time-series-foundation-models-for-multivariate-financial-time-series-forecasting/","tags":["Time Series Foundation Models","Transfer Learning","Zero-Shot Forecasting","Volatility Prediction","Cross-Asset (Fixed Income","Foreign Exchange","Equities)"]},{"paper_id":"2507.07358","title":"Variable annuities: A closer look at ratchet guarantees, hybrid contract designs, and taxation","paper_date":"2025-07-10","authors":["Jennifer Alonso-Garcia","Len Patrick Dominic M. Garces","Jonathan Ziveyi"],"topics":["insurance-actuarial","stochastic-control"],"asset_classes":["insurance"],"methods":["optimization","stochastic-calculus"],"paper_type":"theoretical","primary_category":"q-fin.PM","doi":null,"journal_ref":null,"quadrant":"Lab Rats","math_complexity":7.5,"empirical_rigor":3.0,"hub_score":4.8,"code_url":null,"paper_url":"https://arxiv.org/pdf/2507.07358v1","page_url":"https://thequant.space/flowcharts/variable-annuities-a-closer-look-at-ratchet-guarantees-hyb/","tags":["Variable Annuities","GMWB","Dynamic Programming","Taxation","Cash Fund","Insurance/Annuities"]},{"paper_id":"2507.08065","title":"Multi-Scale Network Dynamics and Systemic Risk: A Model Context Protocol Approach to Financial Markets","paper_date":"2025-07-10","authors":["Avishek Bhandari"],"topics":["risk-management","market-simulation"],"asset_classes":["macro-multi-asset"],"methods":["network-graph","simulation","econophysics-complexity","econometrics-time-series"],"paper_type":"software-tool","primary_category":"q-fin.RM","doi":null,"journal_ref":null,"quadrant":"Holy Grail","math_complexity":6.5,"empirical_rigor":7.0,"hub_score":6.8,"code_url":"https://github.com/avishekb9/MCPFM","paper_url":"https://arxiv.org/pdf/2507.08065.pdf","page_url":"https://thequant.space/flowcharts/multi-scale-network-dynamics-and-systemic-risk-a-model-context-protocol/","tags":["Systemic Risk","Transfer Entropy","Agent-Based Modeling","Wavelet Decomposition","Model Context Protocol","Cross-Asset / Systemic"]},{"paper_id":"2507.08101","title":"Three-level qualitative classification of financial risks under varying conditions through first passage times","paper_date":"2025-07-10","authors":["Carlos Bouthelier-Madre","Carlos Escudero"],"topics":["risk-management","fixed-income"],"asset_classes":["credit"],"methods":["stochastic-calculus","econometrics-time-series"],"paper_type":"theoretical","primary_category":"q-fin.MF","doi":null,"journal_ref":null,"quadrant":"Lab Rats","math_complexity":8.0,"empirical_rigor":1.5,"hub_score":4.1,"code_url":null,"paper_url":"https://arxiv.org/pdf/2507.08101.pdf","page_url":"https://thequant.space/flowcharts/three-level-qualitative-classification-of-financial-risks-under-varying/","tags":["Geometric Brownian Motion","First Passage Time","Credit Risk","Black-Cox Model","Credit / Corporate Debt"]},{"paper_id":"2507.08193","title":"Entity-Specific Cyber Risk Assessment using InsurTech Empowered Risk Factors","paper_date":"2025-07-10","authors":["Jiayi Guo","Zhiyu Quan","Linfeng Zhang"],"topics":["insurance-actuarial","machine-learning"],"asset_classes":["insurance"],"methods":["machine-learning"],"paper_type":"empirical","primary_category":"q-fin.RM","doi":null,"journal_ref":null,"quadrant":"Street Traders","math_complexity":3.5,"empirical_rigor":7.5,"hub_score":5.9,"code_url":null,"paper_url":"https://arxiv.org/pdf/2507.08193.pdf","page_url":"https://thequant.space/flowcharts/entity-specific-cyber-risk-assessment-using-insurtech-empowered-risk-factors/","tags":["Interpretable machine learning","Multilabel classification","Multioutput regression","Actuarial modeling","Risk assessment","Insurance / Actuarial"]},{"paper_id":"2507.08302","title":"Arbitrage on Decentralized Exchanges","paper_date":"2025-07-11","authors":["Xue Dong He","Chen Yang","Yutian Zhou"],"topics":["crypto-defi"],"asset_classes":["crypto"],"methods":["game-theory"],"paper_type":"empirical","primary_category":"q-fin.MF","doi":null,"journal_ref":null,"quadrant":"Holy Grail","math_complexity":7.5,"empirical_rigor":8.0,"hub_score":7.8,"code_url":null,"paper_url":"https://arxiv.org/pdf/2507.08302v1","page_url":"https://thequant.space/flowcharts/arbitrage-on-decentralized-exchanges/","tags":["Decentralized Exchange","Arbitrage","Gas Fee Competition","Nash Equilibrium","Game Theory","Cryptocurrency"]},{"paper_id":"2507.08394","title":"Temperature Measurement in Agent Systems","paper_date":"2025-07-11","authors":["Christoph J. Börner","Ingo Hoffmann"],"topics":["volatility","market-simulation"],"asset_classes":["macro-multi-asset"],"methods":["econophysics-complexity","stochastic-calculus","simulation"],"paper_type":"empirical","primary_category":"q-fin.ST","doi":null,"journal_ref":null,"quadrant":"Lab Rats","math_complexity":9.0,"empirical_rigor":2.0,"hub_score":4.8,"code_url":null,"paper_url":"https://arxiv.org/pdf/2507.08394v1","page_url":"https://thequant.space/flowcharts/temperature-measurement-in-agent-systems/","tags":["Agent-Based Models","Econophysics","Spin Systems","Temperature Measurement","Market Volatility","Multi-Asset/Agent Systems"]},{"paper_id":"2507.08482","title":"Tensor train representations of Greeks for Fourier-based pricing of multi-asset options","paper_date":"2025-07-11","authors":["Rihito Sakurai","Koichi Miyamoto","Tsuyoshi Okubo"],"topics":["options-derivatives"],"asset_classes":["derivatives","macro-multi-asset"],"methods":["simulation","quantum","network-graph"],"paper_type":"empirical","primary_category":"q-fin.CP","doi":null,"journal_ref":null,"quadrant":"Holy Grail","math_complexity":8.5,"empirical_rigor":7.0,"hub_score":7.6,"code_url":null,"paper_url":"https://arxiv.org/pdf/2507.08482v1","page_url":"https://thequant.space/flowcharts/tensor-train-representations-of-greeks-for-fourier-based-pri/","tags":["Greeks","Tensor Train","Monte Carlo","Multi-Asset Options","Fourier Transform","Derivatives"]},{"paper_id":"2507.08584","title":"To Trade or Not to Trade: An Agentic Approach to Estimating Market Risk Improves Trading Decisions","paper_date":"2025-07-11","authors":["Dimitrios Emmanoulopoulos","Ollie Olby","Justin Lyon","Namid R. Stillman"],"topics":["nlp-llm"],"asset_classes":["equities"],"methods":["nlp-llm","simulation","stochastic-calculus"],"paper_type":"empirical","primary_category":"q-fin.ST","doi":null,"journal_ref":null,"quadrant":"Holy Grail","math_complexity":7.5,"empirical_rigor":6.0,"hub_score":6.6,"code_url":null,"paper_url":"https://arxiv.org/pdf/2507.08584v1","page_url":"https://thequant.space/flowcharts/to-trade-or-not-to-trade-an-agentic-approach-to-estimating/","tags":["Large Language Models","Stochastic Differential Equations","Agentic Frameworks","Trading Strategy","Risk Metrics","Equities"]},{"paper_id":"2507.08835","title":"Representation learning with a transformer by contrastive learning for money laundering detection","paper_date":"2025-07-07","authors":["Harold Guéneau","Alain Celisse","Pascal Delange"],"topics":["machine-learning"],"asset_classes":[],"methods":["deep-learning","econometrics-time-series","machine-learning"],"paper_type":"empirical","primary_category":"cs.LG","doi":null,"journal_ref":null,"quadrant":"Lab Rats","math_complexity":6.5,"empirical_rigor":4.0,"hub_score":5.0,"code_url":null,"paper_url":"https://arxiv.org/pdf/2507.08835v1","page_url":"https://thequant.space/flowcharts/representation-learning-with-a-transformer-by-contrastive-le/","tags":["Money laundering detection","Transformer neural network","Contrastive learning","Benjamini-Hochberg procedure","Time series scoring","Cash/Transactions"]},{"paper_id":"2507.08915","title":"Quantifying Crypto Portfolio Risk: A Simulation-Based Framework Integrating Volatility, Hedging, Contagion, and Monte Carlo Modeling","paper_date":"2025-07-11","authors":["Kiarash Firouzi"],"topics":["crypto-defi","risk-management","volatility"],"asset_classes":["crypto"],"methods":["simulation","network-graph","optimization"],"paper_type":"empirical","primary_category":"q-fin.RM","doi":null,"journal_ref":null,"quadrant":"Holy Grail","math_complexity":5.5,"empirical_rigor":5.0,"hub_score":5.2,"code_url":null,"paper_url":"https://arxiv.org/pdf/2507.08915.pdf","page_url":"https://thequant.space/flowcharts/quantifying-crypto-portfolio-risk-a-simulation-based-framework-integrating/","tags":["Contagion modeling","Volatility stress testing","Monte Carlo simulation","Stablecoin hedging","Mean-variance optimization","Cryptocurrencies"]},{"paper_id":"2507.09004","title":"Function approximations for counterparty credit exposure calculations","paper_date":"2025-07-11","authors":["Domagoj Demeterfi","Kathrin Glau","Linus Wunderlich"],"topics":["risk-management"],"asset_classes":["credit","derivatives"],"methods":["simulation","stochastic-calculus"],"paper_type":"theoretical","primary_category":"q-fin.CP","doi":null,"journal_ref":null,"quadrant":"Holy Grail","math_complexity":8.5,"empirical_rigor":7.0,"hub_score":7.6,"code_url":null,"paper_url":"https://arxiv.org/pdf/2507.09004v1","page_url":"https://thequant.space/flowcharts/function-approximations-for-counterparty-credit-exposure-cal/","tags":["Exposure Measurement","Chebyshev Interpolation","XVA","Monte Carlo","Error Bounds","Derivatives/Counterparty Risk"]},{"paper_id":"2507.09181","title":"Generalized Orlicz premia","paper_date":"2025-07-12","authors":["Mücahit Aygün","Fabio Bellini","Roger J. A. Laeven"],"topics":["insurance-actuarial"],"asset_classes":["insurance"],"methods":[],"paper_type":"theoretical","primary_category":"q-fin.RM","doi":null,"journal_ref":null,"quadrant":"Lab Rats","math_complexity":8.5,"empirical_rigor":1.5,"hub_score":4.3,"code_url":null,"paper_url":"https://arxiv.org/pdf/2507.09181.pdf","page_url":"https://thequant.space/flowcharts/generalized-orlicz-premia/","tags":["Orlicz premia","Loss functions","Dual representation","Elicitability","Lp-quantiles","Insurance / Actuarial"]},{"paper_id":"2507.09196","title":"Functionally Generated Portfolios Under Stochastic Transaction Costs: Theory and Empirical Evidence","paper_date":"2025-07-12","authors":["Nader Karimi","Erfan Salavati"],"topics":["market-microstructure"],"asset_classes":["equities"],"methods":["stochastic-calculus"],"paper_type":"empirical","primary_category":"q-fin.PM","doi":null,"journal_ref":null,"quadrant":"Holy Grail","math_complexity":9.2,"empirical_rigor":6.8,"hub_score":7.76,"code_url":null,"paper_url":"https://arxiv.org/pdf/2507.09196v1","page_url":"https://thequant.space/flowcharts/functionally-generated-portfolios-under-stochastic-transacti/","tags":["Stochastic portfolio theory","Transaction costs","Relative arbitrage","Limit order book","Liquidity shocks"]},{"paper_id":"2507.09347","title":"A Framework for Predictive Directional Trading Based on Volatility and Causal Inference","paper_date":"2025-07-12","authors":["Ivan Letteri"],"topics":["volatility","machine-learning","high-frequency-trading"],"asset_classes":["equities"],"methods":["econometrics-time-series","causal-inference","machine-learning","econophysics-complexity"],"paper_type":"empirical","primary_category":"q-fin.ST","doi":null,"journal_ref":null,"quadrant":"Holy Grail","math_complexity":8.5,"empirical_rigor":7.0,"hub_score":7.6,"code_url":null,"paper_url":"https://arxiv.org/pdf/2507.09347v1","page_url":"https://thequant.space/flowcharts/a-framework-for-predictive-directional-trading-based-on-vola/","tags":["Volatility clustering","Granger causality","Dynamic Time Warping","Causal inference","Algorithmic trading"]},{"paper_id":"2507.09412","title":"Joint deep calibration of the 4-factor PDV model","paper_date":"2025-07-12","authors":["Fabio Baschetti","Giacomo Bormetti","Pietro Rossi"],"topics":["volatility","options-derivatives","machine-learning"],"asset_classes":["derivatives","equities"],"methods":["simulation","deep-learning","network-graph"],"paper_type":"empirical","primary_category":"q-fin.CP","doi":null,"journal_ref":null,"quadrant":"Holy Grail","math_complexity":8.5,"empirical_rigor":7.5,"hub_score":7.9,"code_url":null,"paper_url":"https://arxiv.org/pdf/2507.09412v1","page_url":"https://thequant.space/flowcharts/joint-deep-calibration-of-the-4-factor-pdv-model/","tags":["Joint Calibration","VIX","Neural Networks","Markov Path-Dependent Volatility","Implied Volatility","Volatility/Derivatives"]},{"paper_id":"2507.09444","title":"Norms Based on Generalized Expected-Shortfalls and Applications","paper_date":"2025-07-13","authors":["Shuyu Gong","Taizhong Hu","Zhenfeng Zou"],"topics":["risk-management","portfolio-optimization","factor-investing"],"asset_classes":["macro-multi-asset"],"methods":["optimization","machine-learning"],"paper_type":"theoretical","primary_category":"q-fin.RM","doi":null,"journal_ref":null,"quadrant":"Lab Rats","math_complexity":8.0,"empirical_rigor":3.0,"hub_score":5.0,"code_url":null,"paper_url":"https://arxiv.org/pdf/2507.09444.pdf","page_url":"https://thequant.space/flowcharts/norms-based-on-generalized-expected-shortfalls-and-applications/","tags":["Expected-Shortfall norms","distortion risk measures","duality theory","portfolio optimization","anomaly detection","Cross-Asset / Unspecified"]},{"paper_id":"2507.09554","title":"Mapping Crisis-Driven Market Dynamics: A Transfer Entropy and Kramers-Moyal Approach to Financial Networks","paper_date":"2025-07-13","authors":["Pouriya Khalilian","Amirhossein N. Golestani","Mohammad Eslamifar","Mostafa T. Firouzjaee","Javad T. Firouzjaee"],"topics":["risk-management"],"asset_classes":["macro-multi-asset","commodities-energy","equities"],"methods":["econophysics-complexity","network-graph"],"paper_type":"empirical","primary_category":"q-fin.ST","doi":null,"journal_ref":null,"quadrant":"Holy Grail","math_complexity":8.0,"empirical_rigor":7.0,"hub_score":7.4,"code_url":null,"paper_url":"https://arxiv.org/pdf/2507.09554v1","page_url":"https://thequant.space/flowcharts/mapping-crisis-driven-market-dynamics-a-transfer-entropy-an/","tags":["Transfer entropy","Kramers-Moyal expansion","Systemic risk","Non-linear dynamics","Information flow"]},{"paper_id":"2507.09601","title":"NMIXX: Domain-Adapted Neural Embeddings for Cross-Lingual eXploration of Finance","paper_date":"2025-07-13","authors":["Hanwool Lee","Sara Yu","Yewon Hwang","Jonghyun Choi","Heejae Ahn","Sungbum Jung","Youngjae Yu"],"topics":["nlp-llm"],"asset_classes":["macro-multi-asset"],"methods":["deep-learning","nlp-llm"],"paper_type":"dataset-benchmark","primary_category":"cs.CL","doi":null,"journal_ref":null,"quadrant":"Street Traders","math_complexity":2.0,"empirical_rigor":7.5,"hub_score":5.3,"code_url":null,"paper_url":"https://arxiv.org/pdf/2507.09601v2","page_url":"https://thequant.space/flowcharts/nmixx-domain-adapted-neural-embeddings-for-cross-lingual-ex/","tags":["Cross-lingual embeddings","Sentence embedding models","FinBERT","Korean financial NLP","Semantic textual similarity"]},{"paper_id":"2507.09734","title":"Boltzmann Price: Toward Understanding the Fair Price in High-Frequency Markets","paper_date":"2025-07-13","authors":["Przemysław Rola"],"topics":["market-microstructure","high-frequency-trading"],"asset_classes":["equities"],"methods":["econophysics-complexity"],"paper_type":"methodological","primary_category":"q-fin.TR","doi":null,"journal_ref":null,"quadrant":"Holy Grail","math_complexity":7.0,"empirical_rigor":6.0,"hub_score":6.4,"code_url":null,"paper_url":"https://arxiv.org/pdf/2507.09734v1","page_url":"https://thequant.space/flowcharts/boltzmann-price-toward-understanding-the-fair-price-in-high/","tags":["Maximum entropy principle","Order book imbalance","Price dynamics","Volume imbalance","Microstructure"]},{"paper_id":"2507.09739","title":"Enhancing Trading Performance Through Sentiment Analysis with Large Language Models: Evidence from the S&P 500","paper_date":"2025-07-13","authors":["Haojie Liu","Zihan Lin","Randall R. 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Vicky Zhao"],"topics":["stochastic-control"],"asset_classes":["macro-multi-asset"],"methods":[],"paper_type":"theoretical","primary_category":"q-fin.PM","doi":null,"journal_ref":null,"quadrant":"Lab Rats","math_complexity":7.5,"empirical_rigor":3.0,"hub_score":4.8,"code_url":null,"paper_url":"https://arxiv.org/pdf/2507.10052v1","page_url":"https://thequant.space/flowcharts/analyzing-the-crowding-out-effect-of-investment-herding-on-c/","tags":["Investment herding","Crowding-out effect","Optimal control theory","Consumption optimization","Household behavior"]},{"paper_id":"2507.10149","title":"A Coincidence of Wants Mechanism for Swap Trade Execution in Decentralized Exchanges","paper_date":"2025-07-14","authors":["Abhimanyu Nag","Madhur Prabhakar","Tanuj Behl"],"topics":["high-frequency-trading"],"asset_classes":["crypto","derivatives"],"methods":["network-graph"],"paper_type":"methodological","primary_category":"cs.GT","doi":null,"journal_ref":null,"quadrant":"Holy Grail","math_complexity":8.5,"empirical_rigor":7.0,"hub_score":7.6,"code_url":null,"paper_url":"https://arxiv.org/pdf/2507.10149v1","page_url":"https://thequant.space/flowcharts/a-coincidence-of-wants-mechanism-for-swap-trade-execution-in/","tags":["Decentralized exchanges (DEX)","Coincidence of Wants (CoW)","Slippage-free execution","Asset matrix formulation","Delta-neutral strategy"]},{"paper_id":"2507.10701","title":"Kernel Learning for Mean-Variance Trading Strategies","paper_date":"2025-07-14","authors":["Owen Futter","Nicola Muca Cirone","Blanka Horvath"],"topics":["portfolio-optimization"],"asset_classes":[],"methods":["stochastic-calculus","optimization","machine-learning","deep-learning"],"paper_type":"empirical","primary_category":"q-fin.TR","doi":null,"journal_ref":null,"quadrant":"Holy Grail","math_complexity":9.5,"empirical_rigor":8.0,"hub_score":8.6,"code_url":null,"paper_url":"https://arxiv.org/pdf/2507.10701v1","page_url":"https://thequant.space/flowcharts/kernel-learning-for-mean-variance-trading-strategies/","tags":["Reproducing Kernel Hilbert Space (RKHS)","Mean-variance optimization","Path-dependent strategies","Signature-based frameworks","Non-Markovian optimization"]},{"paper_id":"2507.11480","title":"Pricing energy spread options with variance gamma-driven Ornstein-Uhlenbeck dynamics","paper_date":"2025-07-15","authors":["Tim Leung","Kevin W. 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Pai"],"topics":["crypto-defi"],"asset_classes":["crypto"],"methods":[],"paper_type":"empirical","primary_category":"cs.CR","doi":null,"journal_ref":null,"quadrant":"Street Traders","math_complexity":3.5,"empirical_rigor":8.0,"hub_score":6.2,"code_url":null,"paper_url":"https://arxiv.org/pdf/2507.13023v3","page_url":"https://thequant.space/flowcharts/measuring-cex-dex-extracted-value-and-searcher-profitability/","tags":["Arbitrage","CEX-DEX","MEV (Maximal Extractable Value)","On-chain Data","Ethereum","Crypto / DeFi"]},{"paper_id":"2507.13099","title":"Governance, productivity and economic development","paper_date":"2025-07-17","authors":["Cuong Le Van","Ngoc-Sang Pham","Thi Kim Cuong Pham","Binh Tran-Nam"],"topics":["market-simulation"],"asset_classes":["macro-multi-asset"],"methods":["game-theory"],"paper_type":"perspective-policy","primary_category":"q-fin.CP","doi":null,"journal_ref":null,"quadrant":"Lab 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Papayiannis","Georgios Psarrakos"],"topics":["risk-management","insurance-actuarial"],"asset_classes":["insurance"],"methods":["econometrics-time-series"],"paper_type":"theoretical","primary_category":"q-fin.RM","doi":null,"journal_ref":null,"quadrant":"Lab Rats","math_complexity":7.0,"empirical_rigor":3.5,"hub_score":4.9,"code_url":null,"paper_url":"https://arxiv.org/pdf/2507.13562.pdf","page_url":"https://thequant.space/flowcharts/a-tail-shape-actuarial-index-based-on-equal-level-relationships-between-value/","tags":["Actuarial tail-shape index","Value at Risk","Expected Shortfall","Generalized Pareto model","Euler risk contributions"]},{"paper_id":"2507.13763","title":"Eliciting reference measures of law-invariant functionals","paper_date":"2025-07-18","authors":["Felix-Benedikt Liebrich","Ruodu Wang"],"topics":["risk-management"],"asset_classes":[],"methods":[],"paper_type":"theoretical","primary_category":"q-fin.RM","doi":null,"journal_ref":null,"quadrant":"Lab Rats","math_complexity":8.5,"empirical_rigor":1.5,"hub_score":4.3,"code_url":null,"paper_url":"https://arxiv.org/pdf/2507.13763.pdf","page_url":"https://thequant.space/flowcharts/eliciting-reference-measures-of-law-invariant-functionals/","tags":["Law-invariant functionals","Risk measures","Expected Shortfall","Value-at-Risk","Supporting sets"]},{"paper_id":"2507.14160","title":"FinSurvival: A Suite of Large Scale Survival Modeling Tasks from Finance","paper_date":"2025-07-07","authors":["Aaron Green","Zihan Nie","Hanzhen Qin","Oshani Seneviratne","Kristin P. Bennett"],"topics":["crypto-defi"],"asset_classes":["crypto"],"methods":["deep-learning"],"paper_type":"dataset-benchmark","primary_category":"q-fin.ST","doi":null,"journal_ref":null,"quadrant":"Street Traders","math_complexity":3.0,"empirical_rigor":8.0,"hub_score":6.0,"code_url":null,"paper_url":"https://arxiv.org/pdf/2507.14160v1","page_url":"https://thequant.space/flowcharts/finsurvival-a-suite-of-large-scale-survival-modeling-tasks/","tags":["Survival modeling","Decentralized Finance (DeFi)","Time-to-event prediction","Censored data","Benchmark dataset","Cryptocurrency"]},{"paper_id":"2507.14325","title":"Eigenvalue Distribution of Empirical Correlation Matrices for Multiscale Complex Systems and Application to Financial Data","paper_date":"2025-07-18","authors":["Luan M. T. de Moraes","Antônio M. S. Macêdo","Giovani L. 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Natural Language Processing (FinNLP)","Model Comparative Evaluation","ROUGE/Cosine Similarity Metrics","10-K Filings Analysis","Large Language Models (LLMs)","Equity (Technology Sector)"]},{"paper_id":"2507.23138","title":"Is Causality Necessary for Efficient Portfolios? 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differentiability","stochastic differential equations","G-expectation","Multi-Asset"]},{"paper_id":"2508.08130","title":"Optimal Dividend, Reinsurance, and Capital Injection Strategies for an Insurer with Two Collaborating Business Lines","paper_date":"2025-08-11","authors":["Tim J. Boonen","Engel John C. Dela Vega","Bin Zou"],"topics":["insurance-actuarial","stochastic-control"],"asset_classes":["insurance","equities"],"methods":["stochastic-calculus","deep-learning"],"paper_type":"theoretical","primary_category":"math.OC","doi":null,"journal_ref":null,"quadrant":"Lab Rats","math_complexity":8.5,"empirical_rigor":2.5,"hub_score":4.9,"code_url":null,"paper_url":"https://arxiv.org/pdf/2508.08130.pdf","page_url":"https://thequant.space/flowcharts/optimal-dividend-reinsurance-and-capital-injection-strategies-for-an-insurer/","tags":["diffusion risk model","optimal dividend control","proportional reinsurance","capital injection","ruin probability","Insurance / Actuarial Risk"]},{"paper_id":"2508.08148","title":"Unwitting Markowitz' Simplification of Portfolio Random Returns","paper_date":"2025-08-11","authors":["Victor Olkhov"],"topics":["portfolio-optimization","market-microstructure"],"asset_classes":["macro-multi-asset","equities"],"methods":["econometrics-time-series"],"paper_type":"methodological","primary_category":"econ.GN","doi":null,"journal_ref":null,"quadrant":"Lab Rats","math_complexity":8.0,"empirical_rigor":1.0,"hub_score":3.8,"code_url":null,"paper_url":"https://arxiv.org/pdf/2508.08148v1","page_url":"https://thequant.space/flowcharts/unwitting-markowitz-simplification-of-portfolio-random-retu/","tags":["Markowitz portfolio theory","portfolio variance","market microstructure","trade volume","arbitrage","Equities"]},{"paper_id":"2508.08152","title":"Optimal Fees for Liquidity Provision in Automated Market Makers","paper_date":"2025-08-11","authors":["Steven Campbell","Philippe Bergault","Jason Milionis","Marcel Nutz"],"topics":["crypto-defi","market-microstructure"],"asset_classes":["crypto"],"methods":[],"paper_type":"empirical","primary_category":"q-fin.TR","doi":null,"journal_ref":null,"quadrant":"Holy Grail","math_complexity":7.0,"empirical_rigor":8.0,"hub_score":7.6,"code_url":null,"paper_url":"https://arxiv.org/pdf/2508.08152v1","page_url":"https://thequant.space/flowcharts/optimal-fees-for-liquidity-provision-in-automated-market-mak/","tags":["automated market makers","adverse selection","LVR","liquidity provision","dynamic fee schedule","Crypto"]},{"paper_id":"2508.08698","title":"DiffVolume: Diffusion Models for Volume Generation in Limit Order Books","paper_date":"2025-08-12","authors":["Zhuohan Wang","Carmine Ventre"],"topics":["market-microstructure","market-simulation","machine-learning"],"asset_classes":["equities"],"methods":["deep-learning","causal-inference","simulation"],"paper_type":"empirical","primary_category":"q-fin.TR","doi":null,"journal_ref":null,"quadrant":"Holy 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Golam Rashed"],"topics":["nlp-llm","machine-learning"],"asset_classes":[],"methods":["nlp-llm","deep-learning","machine-learning"],"paper_type":"empirical","primary_category":"cs.CL","doi":null,"journal_ref":null,"quadrant":"Philosophers","math_complexity":2.0,"empirical_rigor":4.0,"hub_score":3.2,"code_url":null,"paper_url":"https://arxiv.org/pdf/2508.09935v1","page_url":"https://thequant.space/flowcharts/language-of-persuasion-and-misrepresentation-in-business-com/","tags":["Deceptive language detection","Transformer models","Computational textual analysis","Sustainable finance","Financial reporting","Equity/General Financial Reporting"]},{"paper_id":"2508.10138","title":"Uniqueness and Existence of Linear Equilibrium with a Constrained Trader","paper_date":"2025-08-13","authors":["Heeyoung Kwon","Jin Hyuk Choi"],"topics":["market-microstructure"],"asset_classes":[],"methods":["game-theory"],"paper_type":"theoretical","primary_category":"q-fin.MF","doi":null,"journal_ref":null,"quadrant":"Lab Rats","math_complexity":8.0,"empirical_rigor":1.5,"hub_score":4.1,"code_url":null,"paper_url":"https://arxiv.org/pdf/2508.10138.pdf","page_url":"https://thequant.space/flowcharts/uniqueness-and-existence-of-linear-equilibrium-with-a-constrained-trader/","tags":["Market Microstructure","Linear Equilibrium","Market Making","Constrained Trading","Discrete-Time Models"]},{"paper_id":"2508.10192","title":"Prompt-Response Semantic Divergence Metrics for Faithfulness Hallucination and Misalignment Detection in Large Language Models","paper_date":"2025-08-13","authors":["Igor Halperin"],"topics":["nlp-llm"],"asset_classes":[],"methods":["nlp-llm","machine-learning","deep-learning"],"paper_type":"empirical","primary_category":"cs.CL","doi":null,"journal_ref":null,"quadrant":"Lab Rats","math_complexity":6.5,"empirical_rigor":3.0,"hub_score":4.4,"code_url":null,"paper_url":"https://arxiv.org/pdf/2508.10192v1","page_url":"https://thequant.space/flowcharts/prompt-response-semantic-divergence-metrics-for-faithfulness/","tags":["Hallucination detection","Semantic divergence metrics","Large Language Models","Jensen-Shannon divergence","Wasserstein distance","Technology/AI"]},{"paper_id":"2508.10208","title":"CATNet: A geometric deep learning approach for CAT bond spread prediction in the primary market","paper_date":"2025-08-13","authors":["Dixon Domfeh","Saeid Safarveisi"],"topics":["insurance-actuarial","machine-learning","fixed-income"],"asset_classes":["insurance","fixed-income"],"methods":["deep-learning","network-graph","machine-learning"],"paper_type":"empirical","primary_category":"q-fin.PR","doi":null,"journal_ref":null,"quadrant":"Holy Grail","math_complexity":7.5,"empirical_rigor":6.0,"hub_score":6.6,"code_url":null,"paper_url":"https://arxiv.org/pdf/2508.10208v1","page_url":"https://thequant.space/flowcharts/catnet-a-geometric-deep-learning-approach-for-cat-bond-spre/","tags":["Relational Graph Convolutional Network","Catastrophe bonds","Network topology","Spread prediction","Geometric deep learning","Catastrophe bonds (Insurance-linked securities)"]},{"paper_id":"2508.10273","title":"A 4% withdrawal rate for American retirement spending, derived from a discrete-time model of stochastic returns on assets and their sample moments","paper_date":"2025-08-14","authors":["Drew M. Thomas"],"topics":["insurance-actuarial","fixed-income"],"asset_classes":["insurance","macro-multi-asset","equities"],"methods":[],"paper_type":"empirical","primary_category":"stat.AP","doi":null,"journal_ref":null,"quadrant":"Holy Grail","math_complexity":7.5,"empirical_rigor":6.0,"hub_score":6.6,"code_url":null,"paper_url":"https://arxiv.org/pdf/2508.10273v2","page_url":"https://thequant.space/flowcharts/a-4-withdrawal-rate-for-american-retirement-spending-deriv/","tags":["4% rule","Retirement consumption","Risk adjustment","Longevity risk","Leverage optimization","Retirement Portfolio (Equity/Bond mix)"]},{"paper_id":"2508.10300","title":"Optimal Capital Deployment Under Stochastic Deal Arrivals: A Continuous-Time ADP Approach","paper_date":"2025-08-14","authors":["Kunal Menda","Raphael S Benarrosh"],"topics":["stochastic-control","reinforcement-learning"],"asset_classes":["private-markets","equities"],"methods":["simulation","reinforcement-learning","optimization"],"paper_type":"empirical","primary_category":"q-fin.PM","doi":null,"journal_ref":null,"quadrant":"Holy Grail","math_complexity":8.5,"empirical_rigor":7.2,"hub_score":7.72,"code_url":null,"paper_url":"https://arxiv.org/pdf/2508.10300v1","page_url":"https://thequant.space/flowcharts/optimal-capital-deployment-under-stochastic-deal-arrivals-a/","tags":["Approximate dynamic programming","Continuous-time Markov decision process","Nonhomogeneous Poisson process","Capital deployment","Quasi-Monte Carlo","Private Equity/Venture Capital"]},{"paper_id":"2508.10663","title":"Higher-order Gini indices: An axiomatic approach","paper_date":"2025-08-14","authors":["Xia Han","Ruodu Wang","Qinyu Wu"],"topics":["risk-management"],"asset_classes":["macro-multi-asset"],"methods":[],"paper_type":"theoretical","primary_category":"q-fin.MF","doi":null,"journal_ref":null,"quadrant":"Holy Grail","math_complexity":8.0,"empirical_rigor":7.5,"hub_score":7.7,"code_url":null,"paper_url":"https://arxiv.org/pdf/2508.10663.pdf","page_url":"https://thequant.space/flowcharts/higher-order-gini-indices-an-axiomatic-approach/","tags":["High-Order Gini Deviation","Choquet Integral","Coherent Risk Measures","Tail Inequality","Elicitability","Cross-Asset"]},{"paper_id":"2508.10682","title":"On data-driven robust distortion risk measures for non-negative risks with partial information","paper_date":"2025-08-14","authors":["Xiangyu Han","Yijun Hu","Ran Wang","Linxiao Wei"],"topics":["risk-management"],"asset_classes":["macro-multi-asset"],"methods":["optimization"],"paper_type":"theoretical","primary_category":"q-fin.RM","doi":null,"journal_ref":null,"quadrant":"Lab Rats","math_complexity":8.5,"empirical_rigor":3.0,"hub_score":5.2,"code_url":null,"paper_url":"https://arxiv.org/pdf/2508.10682.pdf","page_url":"https://thequant.space/flowcharts/on-data-driven-robust-distortion-risk-measures-for-non-negative-risks-with/","tags":["Distortion Risk Measures","Distributional Uncertainty","Wasserstein Distance","Robust Optimization","Cross-Asset"]},{"paper_id":"2508.10776","title":"Estimating Covariance for Global Minimum Variance Portfolio: A Decision-Focused Learning Approach","paper_date":"2025-08-14","authors":["Juchan Kim","Inwoo Tae","Yongjae Lee"],"topics":["risk-management","portfolio-optimization"],"asset_classes":["equities"],"methods":["optimization","machine-learning","deep-learning"],"paper_type":"empirical","primary_category":"q-fin.PM","doi":null,"journal_ref":null,"quadrant":"Holy Grail","math_complexity":9.0,"empirical_rigor":8.0,"hub_score":8.4,"code_url":null,"paper_url":"https://arxiv.org/pdf/2508.10776v1","page_url":"https://thequant.space/flowcharts/estimating-covariance-for-global-minimum-variance-portfolio/","tags":["Decision-focused learning","Global minimum-variance portfolio","Portfolio optimization","Gradient-based optimization","Mean-squared error","Equity (Portfolio Management)"]},{"paper_id":"2508.10778","title":"Dynamic Skewness in Stochastic Volatility Models: A Penalized Prior Approach","paper_date":"2025-08-14","authors":["Bruno E. Holtz","Ricardo S. Ehlers","Adriano K. 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Taylor","Chao Wang"],"topics":["risk-management","machine-learning"],"asset_classes":["macro-multi-asset"],"methods":["econometrics-time-series"],"paper_type":"empirical","primary_category":"q-fin.RM","doi":null,"journal_ref":null,"quadrant":"Holy Grail","math_complexity":6.5,"empirical_rigor":8.5,"hub_score":7.7,"code_url":null,"paper_url":"https://arxiv.org/pdf/2508.16919.pdf","page_url":"https://thequant.space/flowcharts/combining-a-large-pool-of-forecasts-of-value-at-risk-and-expected-shortfall/","tags":["Value-at-Risk","Expected Shortfall","forecast combination","regularization","risk forecasting","Cross-Asset"]},{"paper_id":"2508.16936","title":"THEME: Enhancing Thematic Investing with Semantic Stock Representations and Temporal Dynamics","paper_date":"2025-08-23","authors":["Hoyoung Lee","Wonbin Ahn","Suhwan Park","Jaehoon Lee","Minjae Kim","Sungdong Yoo","Taeyoon Lim","Woohyung Lim","Yongjae Lee"],"topics":["portfolio-optimization","nlp-llm"],"asset_classes":["equities"],"methods":["nlp-llm","deep-learning"],"paper_type":"empirical","primary_category":"q-fin.PM","doi":null,"journal_ref":null,"quadrant":"Holy Grail","math_complexity":6.5,"empirical_rigor":8.0,"hub_score":7.4,"code_url":null,"paper_url":"https://arxiv.org/pdf/2508.16936v2","page_url":"https://thequant.space/flowcharts/theme-enhancing-thematic-investing-with-semantic-stock-repr/","tags":["Thematic investing","Hierarchical contrastive learning","LLM embedding fine-tuning","Portfolio construction","Asset retrieval","Equity Portfolio Management"]},{"paper_id":"2508.17014","title":"Risk-Neutral Pricing of Random-Expiry Options Using Trinomial Trees","paper_date":"2025-08-23","authors":["Sebastien Bossu","Michael Grabchak"],"topics":["options-derivatives"],"asset_classes":["derivatives"],"methods":["stochastic-calculus"],"paper_type":"theoretical","primary_category":"q-fin.PR","doi":null,"journal_ref":null,"quadrant":"Holy Grail","math_complexity":8.5,"empirical_rigor":7.0,"hub_score":7.6,"code_url":null,"paper_url":"https://arxiv.org/pdf/2508.17014v1","page_url":"https://thequant.space/flowcharts/risk-neutral-pricing-of-random-expiry-options-using-trinomia/","tags":["Random-expiry options","Trinomial tree","Derivative pricing","Arbitrage-free pricing","Numerical methods","Derivatives/Options"]},{"paper_id":"2508.17086","title":"Detecting Multilevel Manipulation from Limit Order Book via Cascaded Contrastive Representation Learning","paper_date":"2025-08-23","authors":["Yushi Lin","Peng Yang"],"topics":["market-microstructure","factor-investing"],"asset_classes":["equities"],"methods":["deep-learning","machine-learning"],"paper_type":"empirical","primary_category":"q-fin.CP","doi":null,"journal_ref":null,"quadrant":"Holy Grail","math_complexity":8.5,"empirical_rigor":7.0,"hub_score":7.6,"code_url":null,"paper_url":"https://arxiv.org/pdf/2508.17086v2","page_url":"https://thequant.space/flowcharts/detecting-multilevel-manipulation-from-limit-order-book-via/","tags":["Trade-based manipulation","Spoofing","Limit Order Book","Representation learning","Supervised contrastive learning","Equity Markets (Microstructure)"]},{"paper_id":"2508.17837","title":"Bimodal Dynamics of the Artificial Limit Order Book Stock Exchange with Autonomous Traders","paper_date":"2025-08-25","authors":["Matej Steinbacher","Mitja Steinbacher","Matjaz Steinbacher"],"topics":["market-simulation","market-microstructure"],"asset_classes":["equities"],"methods":["simulation","econophysics-complexity","econometrics-time-series"],"paper_type":"theoretical","primary_category":"q-fin.TR","doi":null,"journal_ref":null,"quadrant":"Lab Rats","math_complexity":7.5,"empirical_rigor":2.5,"hub_score":4.5,"code_url":null,"paper_url":"https://arxiv.org/pdf/2508.17837v1","page_url":"https://thequant.space/flowcharts/bimodal-dynamics-of-the-artificial-limit-order-book-stock-ex/","tags":["Artificial stock market","Limit Order Book","Bistability","Agent-based modeling","Path dependence","Equity Market Modeling"]},{"paper_id":"2508.17906","title":"FinReflectKG: Agentic Construction and Evaluation of Financial Knowledge Graphs","paper_date":"2025-08-25","authors":["Abhinav Arun","Fabrizio Dimino","Tejas Prakash Agarwal","Bhaskarjit Sarmah","Stefano Pasquali"],"topics":["nlp-llm"],"asset_classes":["equities"],"methods":["nlp-llm","network-graph"],"paper_type":"empirical","primary_category":"q-fin.CP","doi":null,"journal_ref":null,"quadrant":"Street Traders","math_complexity":1.5,"empirical_rigor":8.0,"hub_score":5.4,"code_url":null,"paper_url":"https://arxiv.org/pdf/2508.17906v2","page_url":"https://thequant.space/flowcharts/finreflectkg-agentic-construction-and-evaluation-of-financi/","tags":["Financial knowledge graph","SEC 10-K filings","Schema-guided extraction","Reflection-agent","LLM-as-a-Judge","General Financial Data Infrastructure"]},{"paper_id":"2508.17996","title":"Solution to the Equity Premium Puzzle with Time-Varying Variables","paper_date":"2025-08-25","authors":["Atilla Aras"],"topics":["factor-investing"],"asset_classes":["equities","macro-multi-asset"],"methods":[],"paper_type":"empirical","primary_category":"q-fin.GN","doi":null,"journal_ref":null,"quadrant":"Lab 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An empirical study of asset pricing in pretrained RNN sparse and global attention models","paper_date":"2025-08-26","authors":["Shanyan Lai"],"topics":["machine-learning","factor-investing"],"asset_classes":["equities"],"methods":["deep-learning","machine-learning","causal-inference","network-graph"],"paper_type":"empirical","primary_category":"q-fin.PR","doi":null,"journal_ref":null,"quadrant":"Holy Grail","math_complexity":7.5,"empirical_rigor":9.0,"hub_score":8.4,"code_url":null,"paper_url":"https://arxiv.org/pdf/2508.19006v1","page_url":"https://thequant.space/flowcharts/is-attention-truly-all-we-need-an-empirical-study-of-asset/","tags":["Empirical asset pricing","Attention mechanisms","RNN/Transformer models","Causal masks","Portfolio backtesting","Equity Quantitative Research"]},{"paper_id":"2508.19609","title":"FinCast: A Foundation Model for Financial Time-Series Forecasting","paper_date":"2025-08-27","authors":["Zhuohang Zhu","Haodong Chen","Qiang Qu","Vera Chung"],"topics":["machine-learning"],"asset_classes":["macro-multi-asset"],"methods":["deep-learning","econometrics-time-series"],"paper_type":"empirical","primary_category":"cs.LG","doi":null,"journal_ref":null,"quadrant":"Holy Grail","math_complexity":7.5,"empirical_rigor":8.5,"hub_score":8.1,"code_url":null,"paper_url":"https://arxiv.org/pdf/2508.19609v1","page_url":"https://thequant.space/flowcharts/fincast-a-foundation-model-for-financial-time-series-foreca/","tags":["Foundation Models","Time Series Forecasting","Zero-shot Learning","Deep Learning","Non-stationarity","Equities"]},{"paper_id":"2508.19994","title":"The Coherent Multiplex: Scalable Real-Time Wavelet Coherence Architecture","paper_date":"2025-08-27","authors":["Noah Shore"],"topics":[],"asset_classes":["macro-multi-asset"],"methods":["econometrics-time-series","network-graph"],"paper_type":"methodological","primary_category":"eess.SP","doi":null,"journal_ref":null,"quadrant":"Philosophers","math_complexity":4.5,"empirical_rigor":3.5,"hub_score":3.9,"code_url":null,"paper_url":"https://arxiv.org/pdf/2508.19994.pdf","page_url":"https://thequant.space/flowcharts/the-coherent-multiplex-scalable-real-time-wavelet-coherence-architecture/","tags":["spectral similarity","wavelet coherence","time series analysis","multilayer graph","Fourier transform","Cross-Asset / Multi-Asset"]},{"paper_id":"2508.20097","title":"Can LLMs Identify Tax Abuse?","paper_date":"2025-08-10","authors":["Andrew Blair-Stanek","Nils Holzenberger","Benjamin Van Durme"],"topics":["nlp-llm"],"asset_classes":["macro-multi-asset"],"methods":["nlp-llm","optimization"],"paper_type":"empirical","primary_category":"q-fin.CP","doi":null,"journal_ref":null,"quadrant":"Street Traders","math_complexity":2.0,"empirical_rigor":7.5,"hub_score":5.3,"code_url":null,"paper_url":"https://arxiv.org/pdf/2508.20097v1","page_url":"https://thequant.space/flowcharts/can-llms-identify-tax-abuse/","tags":["Large Language Models (LLMs)","Tax Strategy Optimization","Legal Reasoning","Information Extraction","Rule Compliance","Multi-Asset"]},{"paper_id":"2508.20100","title":"A Solow-Swan framework for economic growth with memory effect","paper_date":"2025-08-11","authors":["M. O. Aibinu","K. J. Duffy","S. Moyo"],"topics":[],"asset_classes":["macro-multi-asset"],"methods":[],"paper_type":"methodological","primary_category":"q-fin.GN","doi":null,"journal_ref":null,"quadrant":"Lab Rats","math_complexity":6.5,"empirical_rigor":2.0,"hub_score":3.8,"code_url":null,"paper_url":"https://arxiv.org/pdf/2508.20100.pdf","page_url":"https://thequant.space/flowcharts/a-solow-swan-framework-for-economic-growth-with-memory-effect/","tags":["Solow-Swan model","Caputo fractional derivative","economic growth dynamics","memory effects","capital accumulation","Macroeconomics / Cross-Asset"]},{"paper_id":"2508.20101","title":"A Heterogeneous Spatiotemporal GARCH Model: A Predictive Framework for Volatility in Financial Networks","paper_date":"2025-08-11","authors":["Atika Aouri","Philipp Otto"],"topics":["volatility"],"asset_classes":["equities"],"methods":["network-graph","econometrics-time-series"],"paper_type":"empirical","primary_category":"q-fin.ST","doi":null,"journal_ref":null,"quadrant":"Lab 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Chai"],"topics":["crypto-defi","volatility","factor-investing"],"asset_classes":["crypto"],"methods":["deep-learning","network-graph","machine-learning","econometrics-time-series"],"paper_type":"empirical","primary_category":"cs.LG","doi":null,"journal_ref":null,"quadrant":"Holy Grail","math_complexity":6.0,"empirical_rigor":8.0,"hub_score":7.2,"code_url":null,"paper_url":"https://arxiv.org/pdf/2509.03260.pdf","page_url":"https://thequant.space/flowcharts/hypv-lead-proactive-early-warning-of-cryptocurrency-anomalies-through-data/","tags":["anomaly detection","hyperbolic embedding","Peak-Valley sampling","blockchain network analysis","early-warning framework","Cryptocurrency"]},{"paper_id":"2509.03439","title":"Concentration Inequalities for Sub-Weibull Random Tensors","paper_date":"2025-09-03","authors":["Yunfan 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Zhu"],"topics":["stochastic-control","portfolio-optimization"],"asset_classes":["macro-multi-asset"],"methods":["stochastic-calculus"],"paper_type":"theoretical","primary_category":"q-fin.PM","doi":null,"journal_ref":null,"quadrant":"Lab Rats","math_complexity":9.5,"empirical_rigor":1.0,"hub_score":4.4,"code_url":null,"paper_url":"https://arxiv.org/pdf/2509.21929v2","page_url":"https://thequant.space/flowcharts/optimal-consumption-investment-with-epstein-zin-utility-unde/","tags":["Epstein-Zin recursive utility","Hamilton-Jacobi-Bellman (HJB) equation","Viscosity solution","Leverage constraints","Dynamic programming","Portfolio Management"]},{"paper_id":"2509.22088","title":"Factor-Based Conditional Diffusion Model for Portfolio Optimization","paper_date":"2025-09-26","authors":["Xuefeng Gao","Mengying He","Xuedong 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Lau"],"topics":["risk-management","crypto-defi"],"asset_classes":["crypto","macro-multi-asset"],"methods":[],"paper_type":"methodological","primary_category":"q-fin.RM","doi":null,"journal_ref":null,"quadrant":"Philosophers","math_complexity":3.5,"empirical_rigor":4.0,"hub_score":3.8,"code_url":null,"paper_url":"https://arxiv.org/pdf/2510.10469.pdf","page_url":"https://thequant.space/flowcharts/a-risk-mitigation-model-of-monetary-ecosystem-with-stablecoins/","tags":["Stablecoins","CBDC","Liquidity Risk","Systemic Risk","Monetary Architecture"]},{"paper_id":"2510.10526","title":"Integrating Large Language Models and Reinforcement Learning for Sentiment-Driven Quantitative Trading","paper_date":"2025-10-12","authors":["Wo Long","Wenxin Zeng","Xiaoyu Zhang","Ziyao Zhou"],"topics":["reinforcement-learning","nlp-llm"],"asset_classes":[],"methods":["nlp-llm","reinforcement-learning"],"paper_type":"empirical","primary_category":"q-fin.CP","doi":null,"journal_ref":null,"quadrant":"Holy 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cycles","competitive equilibrium","liquidity management","Insurance"]},{"paper_id":"2510.15879","title":"A study about who is interested in stock splitting and why: considering companies, shareholders or managers","paper_date":"2025-08-23","authors":["Jiaquan Nicholas Chen","Marcel Ausloos"],"topics":[],"asset_classes":["equities"],"methods":["causal-inference"],"paper_type":"perspective-policy","primary_category":"q-fin.GN","doi":null,"journal_ref":null,"quadrant":"Philosophers","math_complexity":2.0,"empirical_rigor":2.5,"hub_score":2.3,"code_url":null,"paper_url":"https://arxiv.org/pdf/2510.15879.pdf","page_url":"https://thequant.space/flowcharts/a-study-about-who-is-interested-in-stock-splitting-and-why-considering/","tags":["Stock Splits","Information Asymmetry","Trading Volume","Market Liquidity","Event Study","Equities"]},{"paper_id":"2510.15883","title":"FinFlowRL: An Imitation-Reinforcement Learning Framework for Adaptive Stochastic Control in Finance","paper_date":"2025-08-30","authors":["Yang Li","Zhi Chen"],"topics":["stochastic-control","reinforcement-learning"],"asset_classes":["macro-multi-asset"],"methods":["reinforcement-learning","stochastic-calculus"],"paper_type":"empirical","primary_category":"q-fin.CP","doi":null,"journal_ref":null,"quadrant":"Holy Grail","math_complexity":8.5,"empirical_rigor":7.0,"hub_score":7.6,"code_url":null,"paper_url":"https://arxiv.org/pdf/2510.15883v1","page_url":"https://thequant.space/flowcharts/finflowrl-an-imitation-reinforcement-learning-framework-for/","tags":["Stochastic Control","Reinforcement Learning","Meta-Learning","Action Chunking","Financial Markets","General Financial Markets"]},{"paper_id":"2510.15892","title":"Geometric Dynamics of Consumer Credit Cycles: A Multivector-based Linear-Attention Framework for Explanatory Economic Analysis","paper_date":"2025-09-08","authors":["Agus Sudjianto","Sandi Setiawan"],"topics":["risk-management"],"asset_classes":["macro-multi-asset"],"methods":["deep-learning","econometrics-time-series"],"paper_type":"empirical","primary_category":"q-fin.GN","doi":null,"journal_ref":null,"quadrant":"Holy Grail","math_complexity":7.5,"empirical_rigor":6.5,"hub_score":6.9,"code_url":null,"paper_url":"https://arxiv.org/pdf/2510.15892.pdf","page_url":"https://thequant.space/flowcharts/geometric-dynamics-of-consumer-credit-cycles-a-multivector-based-linear/","tags":["Geometric Algebra","Clifford Algebra","Multi-vectors","Systemic Risk Dynamics","Feedback-Spiral Decomposition","Credit"]},{"paper_id":"2510.15900","title":"Bitcoin Price Forecasting Based on Hybrid Variational Mode Decomposition and Long Short Term Memory Network","paper_date":"2025-09-11","authors":["Emmanuel Boadi"],"topics":["machine-learning","crypto-defi"],"asset_classes":["crypto"],"methods":["deep-learning","network-graph","econometrics-time-series"],"paper_type":"empirical","primary_category":"q-fin.ST","doi":null,"journal_ref":null,"quadrant":"Lab Rats","math_complexity":8.5,"empirical_rigor":6.5,"hub_score":7.3,"code_url":null,"paper_url":"https://arxiv.org/pdf/2510.15900v1","page_url":"https://thequant.space/flowcharts/bitcoin-price-forecasting-based-on-hybrid-variational-mode-d/","tags":["Bitcoin Price Forecasting","Variational Mode Decomposition (VMD)","Long Short-Term Memory (LSTM)","Cryptocurrency","Time Series Decomposition"]},{"paper_id":"2510.15903","title":"Quantum and Classical Machine Learning in Decentralized Finance: Comparative Evidence from Multi-Asset Backtesting of Automated Market Makers","paper_date":"2025-09-14","authors":["Chi-Sheng Chen","Aidan Hung-Wen Tsai"],"topics":["crypto-defi","market-microstructure","machine-learning"],"asset_classes":["crypto","macro-multi-asset"],"methods":["machine-learning","quantum","deep-learning","network-graph"],"paper_type":"empirical","primary_category":"q-fin.ST","doi":null,"journal_ref":null,"quadrant":"Holy Grail","math_complexity":8.5,"empirical_rigor":9.0,"hub_score":8.8,"code_url":null,"paper_url":"https://arxiv.org/pdf/2510.15903v1","page_url":"https://thequant.space/flowcharts/quantum-and-classical-machine-learning-in-decentralized-fina/","tags":["quantum machine learning","hybrid quantum-classical models","automated market makers","decentralized finance","cryptocurrency","Cryptocurrency"]},{"paper_id":"2510.15911","title":"The Sleeping Beauty Problem: Sleeping Kelly is a Thirder","paper_date":"2025-09-26","authors":["Ben Abramowitz"],"topics":[],"asset_classes":["macro-multi-asset"],"methods":["optimization","game-theory"],"paper_type":"methodological","primary_category":"q-fin.GN","doi":null,"journal_ref":null,"quadrant":"Philosophers","math_complexity":3.5,"empirical_rigor":2.0,"hub_score":2.6,"code_url":null,"paper_url":"https://arxiv.org/pdf/2510.15911.pdf","page_url":"https://thequant.space/flowcharts/the-sleeping-beauty-problem-sleeping-kelly-is-a-thirder/","tags":["Kelly Criterion","Sleeping Beauty Problem","Dutch Books","Growth Rate Optimization","Multi-Asset"]},{"paper_id":"2510.15915","title":"Investor Sentiment and Market Movements: A Granger Causality Perspective","paper_date":"2025-09-27","authors":["Tamoghna Mukherjee"],"topics":["nlp-llm"],"asset_classes":["equities"],"methods":["econometrics-time-series","nlp-llm","causal-inference"],"paper_type":"methodological","primary_category":"q-fin.ST","doi":null,"journal_ref":null,"quadrant":"Street 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S. Figueiredo"],"topics":["nlp-llm"],"asset_classes":["macro-multi-asset"],"methods":["nlp-llm","machine-learning"],"paper_type":"empirical","primary_category":"q-fin.ST","doi":null,"journal_ref":null,"quadrant":"Street Traders","math_complexity":3.5,"empirical_rigor":8.0,"hub_score":6.2,"code_url":null,"paper_url":"https://arxiv.org/pdf/2510.15929v1","page_url":"https://thequant.space/flowcharts/comparing-llms-for-sentiment-analysis-in-financial-market-ne/","tags":["Sentiment Analysis","Large Language Models (LLMs)","Natural Language Processing","Financial News","Market Sentiment","General Financial Markets"]},{"paper_id":"2510.15934","title":"Probability equivalent level for CoVaR and VaR in bivariate Student-\\textit{t} copulas with application to foreign exchange risk monitoring","paper_date":"2025-10-06","authors":["Daniela I. Flores-Silva","Miguel A. Sordo","Alfonso Suárez-Llorens"],"topics":["risk-management"],"asset_classes":["fx"],"methods":["econometrics-time-series","network-graph"],"paper_type":"empirical","primary_category":"q-fin.RM","doi":null,"journal_ref":null,"quadrant":"Holy Grail","math_complexity":6.5,"empirical_rigor":6.0,"hub_score":6.2,"code_url":null,"paper_url":"https://arxiv.org/pdf/2510.15934.pdf","page_url":"https://thequant.space/flowcharts/probability-equivalent-level-for-covar-and-var-in-bivariate-student-textit-t/","tags":["Value at Risk (VaR)","CoVaR","Student-t Copula","Tail Dependence","Foreign Exchange (FX)"]},{"paper_id":"2510.15937","title":"Tail-Safe Stochastic-Control SPX-VIX Hedging: A White-Box Bridge Between AI Sensitivities and Arbitrage-Free Market Dynamics","paper_date":"2025-10-09","authors":["Jian’an Zhang"],"topics":["volatility","options-derivatives"],"asset_classes":["derivatives"],"methods":["optimization","stochastic-calculus","reinforcement-learning"],"paper_type":"theoretical","primary_category":"q-fin.RM","doi":null,"journal_ref":null,"quadrant":"Holy Grail","math_complexity":8.5,"empirical_rigor":6.2,"hub_score":7.12,"code_url":null,"paper_url":"https://arxiv.org/pdf/2510.15937v1","page_url":"https://thequant.space/flowcharts/tail-safe-stochastic-control-spx-vix-hedging-a-white-box-br/","tags":["Hedging","VIX","Control Barrier Functions","Dupire Local Volatility","Quadratic Programming","Equities / Volatility"]},{"paper_id":"2510.15938","title":"Dynamic Factor Analysis of Price Movements in the Philippine Stock Exchange","paper_date":"2025-10-08","authors":["Brian Godwin Lim","Dominic Dayta","Benedict Ryan Tiu","Renzo Roel Tan","Len Patrick Dominic Garces","Kazushi Ikeda"],"topics":["factor-investing"],"asset_classes":["equities","macro-multi-asset"],"methods":["econometrics-time-series"],"paper_type":"empirical","primary_category":"q-fin.ST","doi":null,"journal_ref":null,"quadrant":"Holy Grail","math_complexity":7.5,"empirical_rigor":6.0,"hub_score":6.6,"code_url":null,"paper_url":"https://arxiv.org/pdf/2510.15938v1","page_url":"https://thequant.space/flowcharts/dynamic-factor-analysis-of-price-movements-in-the-philippine/","tags":["Dynamic Factor Model","Kalman Filter","Maximum Likelihood Estimation","Nowcasting","Systematic Risk","Equities"]},{"paper_id":"2510.15942","title":"Intrinsic Geometry of the Stock Market from Graph Ricci Flow","paper_date":"2025-10-09","authors":["Bhargavi Srinivasan"],"topics":[],"asset_classes":["equities"],"methods":["network-graph","machine-learning","econophysics-complexity"],"paper_type":"empirical","primary_category":"q-fin.ST","doi":null,"journal_ref":null,"quadrant":"Holy Grail","math_complexity":9.5,"empirical_rigor":6.0,"hub_score":7.4,"code_url":null,"paper_url":"https://arxiv.org/pdf/2510.15942v1","page_url":"https://thequant.space/flowcharts/intrinsic-geometry-of-the-stock-market-from-graph-ricci-flow/","tags":["Ollivier-Ricci curvature","Ricci flow","Graph topology","Correlation graph","Surgery singularities","Equities (NASDAQ 100)"]},{"paper_id":"2510.15949","title":"ATLAS: Adaptive Trading with LLM AgentS Through Dynamic Prompt Optimization and Multi-Agent Coordination","paper_date":"2025-10-10","authors":["Charidimos Papadakis","Angeliki Dimitriou","Giorgos Filandrianos","Maria Lymperaiou","Konstantinos Thomas","Giorgos Stamou"],"topics":["nlp-llm","reinforcement-learning"],"asset_classes":[],"methods":["nlp-llm","simulation","optimization","reinforcement-learning"],"paper_type":"empirical","primary_category":"q-fin.TR","doi":null,"journal_ref":null,"quadrant":"Street Traders","math_complexity":3.5,"empirical_rigor":7.0,"hub_score":5.6,"code_url":null,"paper_url":"https://arxiv.org/pdf/2510.15949v2","page_url":"https://thequant.space/flowcharts/atlas-adaptive-trading-with-llm-agents-through-dynamic-prom/","tags":["Large Language Models (LLM)","Multi-agent framework","Adaptive-OPRO","Order-aware action space","Reinforcement learning from human feedback","Equities"]},{"paper_id":"2510.15956","title":"ESG Signaling on Wall Street in the AI Era","paper_date":"2025-10-11","authors":["Qionghua Chu"],"topics":["factor-investing"],"asset_classes":["esg-climate","credit","private-markets"],"methods":[],"paper_type":"empirical","primary_category":"q-fin.GN","doi":null,"journal_ref":null,"quadrant":"Philosophers","math_complexity":2.5,"empirical_rigor":4.0,"hub_score":3.4,"code_url":null,"paper_url":"https://arxiv.org/pdf/2510.15956v1","page_url":"https://thequant.space/flowcharts/esg-signaling-on-wall-street-in-the-ai-era/","tags":["ESG scores","Signaling channel","Cross-sectional regression","Debt-to-total-capital ratio","Institutional investors","Equities (S&P 500)"]},{"paper_id":"2510.15984","title":"Berms without Calibration","paper_date":"2025-10-13","authors":["K. E. Feldman"],"topics":["options-derivatives","fixed-income"],"asset_classes":["fixed-income","derivatives"],"methods":[],"paper_type":"theoretical","primary_category":"q-fin.PR","doi":null,"journal_ref":null,"quadrant":"Lab Rats","math_complexity":7.5,"empirical_rigor":2.5,"hub_score":4.5,"code_url":null,"paper_url":"https://arxiv.org/pdf/2510.15984.pdf","page_url":"https://thequant.space/flowcharts/berms-without-calibration/","tags":["Bermudan Swaptions","Semi-Analytical Pricing","Swap Rate Distribution","Interest Rate Derivatives"]},{"paper_id":"2510.15988","title":"On Bellman equation in the limit order optimization problem for high-frequency trading","paper_date":"2025-10-13","authors":["M. I. Balakaeva","A. Yu. 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Ferrara"],"topics":["market-microstructure","stochastic-control","reinforcement-learning"],"asset_classes":[],"methods":["game-theory","simulation","reinforcement-learning"],"paper_type":"theoretical","primary_category":"q-fin.TR","doi":null,"journal_ref":null,"quadrant":"Lab Rats","math_complexity":7.0,"empirical_rigor":3.0,"hub_score":4.6,"code_url":null,"paper_url":"https://arxiv.org/pdf/2510.15995v1","page_url":"https://thequant.space/flowcharts/the-invisible-handshake-tacit-collusion-between-adaptive-ma/","tags":["Tacit Collusion","Market Microstructure","Stochastic Games","Reinforcement Learning","Price Formation","General Financial Markets"]},{"paper_id":"2510.16008","title":"Convolutional Attention in Betting Exchange Markets","paper_date":"2025-10-14","authors":["Rui Gonçalves","Vitor Miguel Ribeiro","Roman Chertovskih","António Pedro 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Yang"],"topics":["volatility"],"asset_classes":["equities"],"methods":[],"paper_type":"empirical","primary_category":"q-fin.ST","doi":null,"journal_ref":null,"quadrant":"Holy Grail","math_complexity":7.0,"empirical_rigor":6.0,"hub_score":6.4,"code_url":null,"paper_url":"https://arxiv.org/pdf/2510.16010v1","page_url":"https://thequant.space/flowcharts/institutional-differences-crisis-shocks-and-volatility-str/","tags":["EGARCH","TGARCH","Volatility persistence","Fat-tailed returns","Institutional differences","Equities (Emerging Asian Markets)"]},{"paper_id":"2510.16066","title":"Cash Flow Underwriting with Bank Transaction Data: Advancing MSME Financial Inclusion in Malaysia","paper_date":"2025-10-17","authors":["Chun Chet Ng","Wei Zeng Low","Jia Yu Lim","Yin Yin 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under the Mean-Variance Criterion","paper_date":"2025-11-11","authors":["Jingyi Cao","Dongchen Li","Virginia R. 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Javier Sánchez-Vidal"],"topics":[],"asset_classes":["private-markets"],"methods":["econometrics-time-series"],"paper_type":"empirical","primary_category":"q-fin.GN","doi":null,"journal_ref":null,"quadrant":"Street Traders","math_complexity":2.5,"empirical_rigor":6.5,"hub_score":4.9,"code_url":null,"paper_url":"https://arxiv.org/pdf/2511.08591.pdf","page_url":"https://thequant.space/flowcharts/an-empirical-assessment-of-the-accounting-semi-identity-problems-pervasiveness/","tags":["Investment-Cash Flow Sensitivity","Accounting Identity","Corporate Finance","Econometrics","Specification Bias"]},{"paper_id":"2511.08602","title":"Dynamic Spatial Treatment Effects and Network Fragility: Theory and Evidence from the 2008 Financial Crisis","paper_date":"2025-11-02","authors":["Tatsuru Kikuchi"],"topics":["risk-management"],"asset_classes":["macro-multi-asset"],"methods":["network-graph","causal-inference","econometrics-time-series"],"paper_type":"empirical","primary_category":"q-fin.RM","doi":null,"journal_ref":null,"quadrant":"Holy Grail","math_complexity":8.5,"empirical_rigor":8.0,"hub_score":8.2,"code_url":null,"paper_url":"https://arxiv.org/pdf/2511.08602.pdf","page_url":"https://thequant.space/flowcharts/dynamic-spatial-treatment-effects-and-network-fragility-theory-and-evidence/","tags":["systemic risk","spatial propagation","network contagion","Laplacian spectral analysis","spatial difference-in-differences"]},{"paper_id":"2511.08606","title":"Data-driven Feynman-Kac Discovery with Applications to Prediction and Data Generation","paper_date":"2025-11-05","authors":["Qi Feng","Guang Lin","Purav Matlia","Denny Serdarevic"],"topics":["stochastic-control"],"asset_classes":["derivatives"],"methods":["stochastic-calculus","deep-learning","simulation","network-graph"],"paper_type":"methodological","primary_category":"q-fin.MF","doi":null,"journal_ref":null,"quadrant":"Holy Grail","math_complexity":9.0,"empirical_rigor":7.0,"hub_score":7.8,"code_url":null,"paper_url":"https://arxiv.org/pdf/2511.08606v1","page_url":"https://thequant.space/flowcharts/data-driven-feynman-kac-discovery-with-applications-to-predi/","tags":["SINDy","Backward stochastic differential equations","Risk-neutral measure","Feynman-Kac formula","Data-driven modeling","Derivatives"]},{"paper_id":"2511.08608","title":"When Reasoning Fails: Evaluating 'Thinking' LLMs for Stock Prediction","paper_date":"2025-11-05","authors":["Rakeshkumar H Sodha"],"topics":["nlp-llm","machine-learning","factor-investing"],"asset_classes":["equities"],"methods":["nlp-llm","econometrics-time-series"],"paper_type":"empirical","primary_category":"q-fin.ST","doi":null,"journal_ref":null,"quadrant":"Holy Grail","math_complexity":6.5,"empirical_rigor":8.0,"hub_score":7.4,"code_url":null,"paper_url":"https://arxiv.org/pdf/2511.08608v1","page_url":"https://thequant.space/flowcharts/when-reasoning-fails-evaluating-thinking-llms-for-stock-p/","tags":["Financial forecasting","Large language models","Cross-sectional ranking","Performance evaluation","Statistical testing","Equities"]},{"paper_id":"2511.08616","title":"Reasoning on Time-Series for Financial Technical Analysis","paper_date":"2025-11-06","authors":["Kelvin J. L. Koa","Jan Chen","Yunshan Ma","Huanhuan Zheng","Tat-Seng Chua"],"topics":["nlp-llm","machine-learning"],"asset_classes":["equities"],"methods":["nlp-llm","econometrics-time-series","reinforcement-learning"],"paper_type":"empirical","primary_category":"q-fin.ST","doi":null,"journal_ref":null,"quadrant":"Holy Grail","math_complexity":6.5,"empirical_rigor":8.0,"hub_score":7.4,"code_url":null,"paper_url":"https://arxiv.org/pdf/2511.08616v1","page_url":"https://thequant.space/flowcharts/reasoning-on-time-series-for-financial-technical-analysis/","tags":["Large Language Models (LLMs)","Technical Analysis","Time-Series Forecasting","Inverse Mean Squared Error (MSE)","Verbal Reasoning","Equities (Stocks)"]},{"paper_id":"2511.08621","title":"The LLM Pro Finance Suite: Multilingual Large Language Models for Financial Applications","paper_date":"2025-11-07","authors":["Gaëtan Caillaut","Raheel Qader","Jingshu Liu","Mariam Nakhlé","Arezki Sadoune","Massinissa Ahmim","Jean-Gabriel Barthelemy"],"topics":["nlp-llm"],"asset_classes":[],"methods":["nlp-llm","machine-learning"],"paper_type":"empirical","primary_category":"q-fin.ST","doi":null,"journal_ref":null,"quadrant":"Street Traders","math_complexity":1.5,"empirical_rigor":8.0,"hub_score":5.4,"code_url":"https://huggingface.co/collections/DragonLLM/llm-open-finance","paper_url":"https://arxiv.org/pdf/2511.08621v1","page_url":"https://thequant.space/flowcharts/the-llm-pro-finance-suite-multilingual-large-language-model/","tags":["Instruction Tuning","Financial NLP","Large Language Models (LLMs)","Financial Translation","Domain Adaptation","General Financial Services (NLP)"]},{"paper_id":"2511.08622","title":"Multi-period Learning for Financial Time Series Forecasting","paper_date":"2025-11-07","authors":["Xu Zhang","Zhengang Huang","Yunzhi Wu","Xun Lu","Erpeng Qi","Yunkai Chen","Zhongya Xue","Qitong Wang","Peng Wang","Wei Wang"],"topics":["machine-learning"],"asset_classes":[],"methods":["deep-learning","econometrics-time-series","machine-learning"],"paper_type":"empirical","primary_category":"q-fin.ST","doi":null,"journal_ref":null,"quadrant":"Street Traders","math_complexity":4.0,"empirical_rigor":8.0,"hub_score":6.4,"code_url":"https://github.com/Meteor-Stars/MLF","paper_url":"https://arxiv.org/pdf/2511.08622v1","page_url":"https://thequant.space/flowcharts/multi-period-learning-for-financial-time-series-forecasting/","tags":["Multi-period Learning","Self-Attention Mechanisms","Patch Embedding","Financial Time-Series Forecasting","Redundancy Filtering","Equities (Stocks)"]},{"paper_id":"2511.08658","title":"It Looks All the Same to Me: Cross-index Training for Long-term Financial Series Prediction","paper_date":"2025-11-11","authors":["Stanislav Selitskiy"],"topics":["machine-learning"],"asset_classes":["equities"],"methods":["deep-learning","network-graph","econometrics-time-series"],"paper_type":"empirical","primary_category":"q-fin.ST","doi":null,"journal_ref":null,"quadrant":"Street Traders","math_complexity":2.5,"empirical_rigor":7.0,"hub_score":5.2,"code_url":null,"paper_url":"https://arxiv.org/pdf/2511.08658v1","page_url":"https://thequant.space/flowcharts/it-looks-all-the-same-to-me-cross-index-training-for-long/","tags":["Cross-Market Prediction","Neural Networks","Financial Forecasting","Index Prediction","Transfer Learning"]},{"paper_id":"2511.08662","title":"Robust distortion risk metrics and portfolio optimization","paper_date":"2025-11-11","authors":["Peng Liu","Steven Vanduffel","Yi Xia"],"topics":["risk-management","portfolio-optimization"],"asset_classes":["macro-multi-asset"],"methods":["optimization"],"paper_type":"theoretical","primary_category":"q-fin.RM","doi":null,"journal_ref":null,"quadrant":"Lab 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Shrinkage Approximator (UPSA)","Ridge penalties","Cross-validation","Covariate shift","Average Oracle correlation eigenvalues","Equities / Portfolio Allocation"]},{"paper_id":"2511.10715","title":"HSBC until 1950: From its colonial cradle past the World Wars","paper_date":"2025-11-13","authors":["Christopher Mantzaris","Ajda Fošner"],"topics":[],"asset_classes":["macro-multi-asset"],"methods":[],"paper_type":"perspective-policy","primary_category":"econ.GN","doi":null,"journal_ref":null,"quadrant":"Philosophers","math_complexity":0.5,"empirical_rigor":1.5,"hub_score":1.1,"code_url":null,"paper_url":"https://arxiv.org/pdf/2511.10715.pdf","page_url":"https://thequant.space/flowcharts/hsbc-until-1950-from-its-colonial-cradle-past-the-world-wars/","tags":["banking history","colonial finance","institutional resilience","business history","Cross-Asset / Multi-Asset"]},{"paper_id":"2511.10999","title":"Governance, Risk, and Regulation: A Framework for Improving Efficiency in Kenyan Pension 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Boonen","Engel John C. Dela Vega"],"topics":["insurance-actuarial","stochastic-control"],"asset_classes":["insurance","equities"],"methods":[],"paper_type":"theoretical","primary_category":"math.OC","doi":null,"journal_ref":null,"quadrant":"Lab Rats","math_complexity":8.5,"empirical_rigor":2.5,"hub_score":4.9,"code_url":null,"paper_url":"https://arxiv.org/pdf/2511.11383.pdf","page_url":"https://thequant.space/flowcharts/optimal-dividend-reinsurance-and-capital-injection-strategies-for-collaborating/","tags":["excess-of-loss reinsurance","diffusion approximation","optimal capital injection","barrier strategy","ruin probability"]},{"paper_id":"2511.11416","title":"Enhancing Efficiency of Pension Schemes through Effective Risk Governance: A Kenyan Perspective","paper_date":"2025-11-14","authors":["Sylvester Willys 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Wu"],"topics":["stochastic-control","risk-management"],"asset_classes":[],"methods":["stochastic-calculus","network-graph"],"paper_type":"theoretical","primary_category":"q-fin.MF","doi":null,"journal_ref":null,"quadrant":"Lab Rats","math_complexity":8.5,"empirical_rigor":1.0,"hub_score":4.0,"code_url":null,"paper_url":"https://arxiv.org/pdf/2511.11909.pdf","page_url":"https://thequant.space/flowcharts/modeling-and-stabilizing-financial-systemic-risk-using-optimal-control-theory/","tags":["systemic risk","unsteady diffusion equation","algebraic Riccati equation","H-infinity control","Hamilton-Jacobi equation"]},{"paper_id":"2511.12093","title":"On the utility problem in a market where price impact is transient","paper_date":"2025-11-15","authors":["Lóránt Nagy","Miklós Rásonyi"],"topics":["stochastic-control","market-microstructure"],"asset_classes":[],"methods":["optimization"],"paper_type":"theoretical","primary_category":"q-fin.PM","doi":null,"journal_ref":null,"quadrant":"Lab 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regulations","Coherent risk measures","Monte Carlo simulation","Market Risk (Multi-asset)"]},{"paper_id":"2511.12490","title":"Discovery of a 13-Sharpe OOS Factor: Drift Regimes Unlock Hidden Cross-Sectional Predictability","paper_date":"2025-11-16","authors":["Mainak Singha"],"topics":["factor-investing"],"asset_classes":["equities"],"methods":["econometrics-time-series"],"paper_type":"empirical","primary_category":"q-fin.TR","doi":null,"journal_ref":null,"quadrant":"Street Traders","math_complexity":4.0,"empirical_rigor":7.0,"hub_score":5.8,"code_url":null,"paper_url":"https://arxiv.org/pdf/2511.12490v1","page_url":"https://thequant.space/flowcharts/discovery-of-a-13-sharpe-oos-factor-drift-regimes-unlock-hi/","tags":["Cross-sectional equity factor","Regime-conditional signal activation","Stock-specific drift regimes","Walk-forward validation","Risk-adjusted returns","Equity"]},{"paper_id":"2511.12763","title":"Impact by design: translating Lead times in flux into an R handbook with 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Intrinsic Time","Scaling laws","Poisson process","Hazard process","Market microstructure","Equity / General Financial Markets"]},{"paper_id":"2511.14980","title":"Selective Forgetting in Option Calibration: An Operator-Theoretic Gauss-Newton Framework","paper_date":"2025-11-18","authors":["Ahmet Umur Özsoy"],"topics":["options-derivatives","market-microstructure"],"asset_classes":["derivatives"],"methods":[],"paper_type":"methodological","primary_category":"q-fin.MF","doi":null,"journal_ref":null,"quadrant":"Lab Rats","math_complexity":6.5,"empirical_rigor":3.5,"hub_score":4.7,"code_url":null,"paper_url":"https://arxiv.org/pdf/2511.14980.pdf","page_url":"https://thequant.space/flowcharts/selective-forgetting-in-option-calibration-an-operator-theoretic-gauss-newton/","tags":["Option calibration","Machine unlearning","Selective forgetting","Nonlinear least-squares","Perturbation bounds","Options"]},{"paper_id":"2511.15214","title":"Corporate Earnings Calls and Analyst 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Lu"],"topics":["volatility","options-derivatives","risk-management"],"asset_classes":["derivatives"],"methods":["stochastic-calculus"],"paper_type":"empirical","primary_category":"q-fin.MF","doi":null,"journal_ref":null,"quadrant":"Holy Grail","math_complexity":6.5,"empirical_rigor":7.5,"hub_score":7.1,"code_url":null,"paper_url":"https://arxiv.org/pdf/2603.27501.pdf","page_url":"https://thequant.space/flowcharts/from-volatility-to-variance-a-skew-enhanced-sabr-model-and-its-empirical-study/","tags":["SABR model","implied volatility curve","Black implied volatility","skew-SABR","volatility smile","Options"]},{"paper_id":"2603.27940","title":"Stability of supermartingale optimal transport problems","paper_date":"2026-03-30","authors":["Shuoqing Deng","Gaoyue Guo","Dominykas Norgilas"],"topics":[],"asset_classes":["macro-multi-asset"],"methods":["stochastic-calculus"],"paper_type":"theoretical","primary_category":"math.PR","doi":null,"journal_ref":null,"quadrant":"Lab 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McAuliffe","Samuel Liew","Yuchao Li","Andrey Ushenin","Chihang Wang","Alexandros Tasos","Jack Pearce","Dimitris Tasoulis","Dimitri P. 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Xu","Yuheng Yan","Zijun Zeng","Bowen Zhang","Francesco Zhang"],"topics":["crypto-defi"],"asset_classes":["crypto"],"methods":["optimization"],"paper_type":"empirical","primary_category":null,"doi":null,"journal_ref":null,"quadrant":"Street Traders","math_complexity":3.5,"empirical_rigor":7.5,"hub_score":5.9,"code_url":null,"paper_url":"https://arxiv.org/pdf/2604.19956v1","page_url":"https://thequant.space/flowcharts/on-chain-peak-shaving/","tags":["Transaction Cost Economics","Execution Costs","Blockchain","Gas Fee Optimization"]},{"paper_id":"2604.20067","title":"Testing replication for an agent-based model of market fragmentation and latency arbitrage","paper_date":"2026-04-01","authors":["Ethan Ratliff-Crain","Colin M. 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cohomology","homological arbitrage","categorical filtrations","simplicial holonomy","probabilistic distortion","Cross-Asset / General Financial Mathematics"]},{"paper_id":"2605.01384","title":"SBCA: Cross-Modal BERT-driven Actor-Critic for Multi-Asset Portfolio Optimization","paper_date":"2026-05-01","authors":["Jinfeng Pan","Jiahao Chen"],"topics":["reinforcement-learning","portfolio-optimization","nlp-llm"],"asset_classes":["macro-multi-asset","equities"],"methods":["reinforcement-learning","nlp-llm","optimization","deep-learning"],"paper_type":"empirical","primary_category":null,"doi":null,"journal_ref":null,"quadrant":"Holy Grail","math_complexity":6.5,"empirical_rigor":7.5,"hub_score":7.1,"code_url":null,"paper_url":"https://arxiv.org/pdf/2605.01384v1","page_url":"https://thequant.space/flowcharts/sbca-cross-modal-bert-driven-actor-critic-for-multi-asset-portfolio-optimization/","tags":["deep reinforcement learning","cross-modal fusion","sentiment analysis","portfolio 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Aldridge"],"topics":[],"asset_classes":["macro-multi-asset"],"methods":["simulation","bayesian"],"paper_type":"methodological","primary_category":"econ.EM","doi":null,"journal_ref":null,"quadrant":"Holy Grail","math_complexity":7.5,"empirical_rigor":5.0,"hub_score":6.0,"code_url":null,"paper_url":"https://arxiv.org/pdf/2605.02085.pdf","page_url":"https://thequant.space/flowcharts/fast-monte-carlo/","tags":["Markov Chain Monte Carlo","variance reduction","Wasserstein distance","steady-state distribution","eigenvalue-based approximation","Cross-Asset / General Financial Mathematics"]},{"paper_id":"2605.02248","title":"Statistics of a multi-factor function from its Fourier transform","paper_date":"2026-05-01","authors":["Matthew A. Herman","Stephen Doro"],"topics":[],"asset_classes":[],"methods":[],"paper_type":"theoretical","primary_category":null,"doi":null,"journal_ref":null,"quadrant":"Lab Rats","math_complexity":8.5,"empirical_rigor":3.0,"hub_score":5.2,"code_url":null,"paper_url":"https://arxiv.org/pdf/2605.02248v1","page_url":"https://thequant.space/flowcharts/statistics-of-a-multi-factor-function-from-its-fourier-transform/","tags":["Fourier transform","m-Coefficient/Index Annihilation","finite abelian groups","moment statistics","N/A (Mathematical Framework)"]},{"paper_id":"2605.02286","title":"Empirical Evaluation of Deadline-Resolved Information Leakage on Documented Polymarket Insider Cases","paper_date":"2026-05-01","authors":["Maksym Nechepurenko"],"topics":[],"asset_classes":[],"methods":[],"paper_type":"empirical","primary_category":null,"doi":null,"journal_ref":null,"quadrant":"Holy 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portfolio selection","Rényi divergence","Information-theoretic finance","Blahut-Arimoto algorithm"]},{"paper_id":"2605.03310","title":"Coordination as an Architectural Layer for LLM-Based Multi-Agent Systems","paper_date":"2026-05-01","authors":["Maksym Nechepurenko","Pavel Shuvalov"],"topics":["nlp-llm","market-simulation"],"asset_classes":[],"methods":["nlp-llm","simulation"],"paper_type":"empirical","primary_category":null,"doi":null,"journal_ref":null,"quadrant":"Holy Grail","math_complexity":5.5,"empirical_rigor":7.5,"hub_score":6.7,"code_url":null,"paper_url":"https://arxiv.org/pdf/2605.03310v1","page_url":"https://thequant.space/flowcharts/coordination-as-an-architectural-layer-for-llm-based-multi-agent-systems/","tags":["Prediction markets","Multi-agent LLM coordination","Brier score decomposition","Agentic architectural design"]},{"paper_id":"2605.03703","title":"Scaling Limits of Bivariate Nearly-Unstable Hawkes Processes and Applications to Rough 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Chen"],"topics":["risk-management","machine-learning","insurance-actuarial"],"asset_classes":["insurance","esg-climate","equities"],"methods":["machine-learning","causal-inference"],"paper_type":"empirical","primary_category":"q-fin.MF","doi":null,"journal_ref":null,"quadrant":"Holy Grail","math_complexity":6.5,"empirical_rigor":8.0,"hub_score":7.4,"code_url":null,"paper_url":"https://arxiv.org/pdf/2605.04479.pdf","page_url":"https://thequant.space/flowcharts/esg-as-priced-crash-insurance-state-dependent-tail-risk-and-deconfounding/","tags":["Double Machine Learning","tail risk resilience","ESG","equity crash risk","state-dependent insurance","Equities"]},{"paper_id":"2605.04690","title":"Learning Time-Inhomogeneous Markov Dynamics in Financial Time Series via Neural Parameterization","paper_date":"2026-05-06","authors":["Jan Rovirosa","Jesse 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Bolotin"],"topics":["crypto-defi","options-derivatives"],"asset_classes":["credit","crypto","derivatives"],"methods":["simulation"],"paper_type":"empirical","primary_category":null,"doi":null,"journal_ref":null,"quadrant":"Holy Grail","math_complexity":7.5,"empirical_rigor":8.0,"hub_score":7.8,"code_url":null,"paper_url":"https://arxiv.org/pdf/2605.05089v1","page_url":"https://thequant.space/flowcharts/dynamic-collateral-control-for-permissionless-spot-perpetual-basis-trading/","tags":["DeFi basis trading","Collateral management","Perpetual futures","Liquidity frictions"]},{"paper_id":"2605.05140","title":"What Can Go Wrong During Caplet Stripping ?","paper_date":"2026-05-01","authors":["Fabien Le Floc'h"],"topics":["volatility","market-microstructure","fixed-income"],"asset_classes":["fixed-income"],"methods":["econometrics-time-series"],"paper_type":"empirical","primary_category":null,"doi":null,"journal_ref":null,"quadrant":"Street 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Kumar"],"topics":["volatility","options-derivatives","machine-learning"],"asset_classes":["derivatives"],"methods":["deep-learning","stochastic-calculus","network-graph"],"paper_type":"theoretical","primary_category":null,"doi":null,"journal_ref":null,"quadrant":"Lab Rats","math_complexity":8.5,"empirical_rigor":3.0,"hub_score":5.2,"code_url":null,"paper_url":"https://arxiv.org/pdf/2605.06688v1","page_url":"https://thequant.space/flowcharts/american-options-pricing-under-heston-model-via-curriculum-learning-in-coupled/","tags":["American Options","Stochastic Volatility","Physics-Informed Neural Networks","Heston Model"]},{"paper_id":"2605.06818","title":"Modeling Dynamic Correlation Matrices with Shrinkage Priors","paper_date":"2026-05-01","authors":["Daniel Andrew Coulson","David S. Matteson","Martin T. Wells"],"topics":["volatility","factor-investing"],"asset_classes":["equities"],"methods":["bayesian","stochastic-calculus"],"paper_type":"empirical","primary_category":"stat.ME","doi":null,"journal_ref":null,"quadrant":"Holy Grail","math_complexity":8.5,"empirical_rigor":6.5,"hub_score":7.3,"code_url":null,"paper_url":"https://arxiv.org/pdf/2605.06818v1","page_url":"https://thequant.space/flowcharts/modeling-dynamic-correlation-matrices-with-shrinkage-priors/","tags":["Dynamic correlation","Bayesian inference","Stochastic volatility","Factor models"]},{"paper_id":"2605.07352","title":"Corporate transparency and the disposition effect","paper_date":"2026-05-08","authors":["Siliu Chen","Fei Ren"],"topics":[],"asset_classes":["equities"],"methods":[],"paper_type":"empirical","primary_category":"q-fin.GN","doi":null,"journal_ref":null,"quadrant":"Street Traders","math_complexity":2.5,"empirical_rigor":6.5,"hub_score":4.9,"code_url":null,"paper_url":"https://arxiv.org/pdf/2605.07352.pdf","page_url":"https://thequant.space/flowcharts/corporate-transparency-and-the-disposition-effect/","tags":["disposition effect","corporate transparency","investor behavior","behavioral bias","Equities"]},{"paper_id":"2605.07558","title":"Stochastic Calculus and the Black-Scholes-Merton Model: A Simplified Approach","paper_date":"2026-05-08","authors":["Kuo-Ping Chang"],"topics":["options-derivatives"],"asset_classes":["derivatives"],"methods":["stochastic-calculus"],"paper_type":"methodological","primary_category":"q-fin.MF","doi":null,"journal_ref":null,"quadrant":"Lab Rats","math_complexity":5.5,"empirical_rigor":2.0,"hub_score":3.4,"code_url":null,"paper_url":"https://arxiv.org/pdf/2605.07558.pdf","page_url":"https://thequant.space/flowcharts/stochastic-calculus-and-the-black-scholes-merton-model-a-simplified-approach/","tags":["Black-Scholes-Merton model","option pricing","expected rate of return","underlying asset dynamics","Options"]},{"paper_id":"2605.08726","title":"The effect of investor-driven information diffusion on excess comovement: Evidence from retail and institutional investors in China and the United States","paper_date":"2026-05-09","authors":["Fei Ren","Miao-Miao Yi","Zhang-Hangjian Chen","Xiang Gao"],"topics":[],"asset_classes":["equities"],"methods":[],"paper_type":"empirical","primary_category":"q-fin.GN","doi":null,"journal_ref":null,"quadrant":"Street Traders","math_complexity":3.5,"empirical_rigor":8.0,"hub_score":6.2,"code_url":null,"paper_url":"https://arxiv.org/pdf/2605.08726.pdf","page_url":"https://thequant.space/flowcharts/the-effect-of-investor-driven-information-diffusion-on-excess-comovement/","tags":["excess comovement","information diffusion","investor behavior","lead-lag relationship","Equities"]},{"paper_id":"2605.09061","title":"A Market-Rule-Informed Neural Network for Efficient Imbalance Electricity Price Forecasting","paper_date":"2026-05-01","authors":["Runyao Yu","Julia Lin","Derek W. Bunn","Jochen Stiasny","Wentao Wang","Yujie Chen","Tara Esterl","Peter Palensky","Jochen L. Cremer"],"topics":["machine-learning","commodities-energy"],"asset_classes":["commodities-energy"],"methods":["deep-learning","network-graph"],"paper_type":"empirical","primary_category":null,"doi":null,"journal_ref":null,"quadrant":"Holy Grail","math_complexity":5.5,"empirical_rigor":8.0,"hub_score":7.0,"code_url":null,"paper_url":"https://arxiv.org/pdf/2605.09061v1","page_url":"https://thequant.space/flowcharts/a-market-rule-informed-neural-network-for-efficient-imbalance-electricity-price/","tags":["Electricity price forecasting","Neural networks","Market-rule priors","Energy trading"]},{"paper_id":"2605.09123","title":"The Engineering of Skew: A Path-Dependent Framework for Asymmetric Volatility Management","paper_date":"2026-05-01","authors":["Gregory A. Fanous"],"topics":["risk-management","volatility"],"asset_classes":[],"methods":[],"paper_type":"methodological","primary_category":null,"doi":null,"journal_ref":null,"quadrant":"Philosophers","math_complexity":4.5,"empirical_rigor":1.5,"hub_score":2.7,"code_url":null,"paper_url":"https://arxiv.org/pdf/2605.09123v1","page_url":"https://thequant.space/flowcharts/the-engineering-of-skew-a-path-dependent-framework-for-asymmetric-volatility/","tags":["Drawdown management","Risk management","Geometric compounding","Asymmetric volatility"]},{"paper_id":"2605.09310","title":"Beyond ESG Scores: Learning Dynamic Constraints for Sequential Portfolio Optimization","paper_date":"2026-05-01","authors":["Xin Li","Yan Ke","Longbing Cao"],"topics":["portfolio-optimization"],"asset_classes":["esg-climate"],"methods":["optimization"],"paper_type":"empirical","primary_category":null,"doi":null,"journal_ref":null,"quadrant":"Holy 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Long"],"topics":["nlp-llm"],"asset_classes":[],"methods":["nlp-llm"],"paper_type":"survey-review","primary_category":"cs.AI","doi":null,"journal_ref":null,"quadrant":"Street Traders","math_complexity":3.0,"empirical_rigor":8.0,"hub_score":6.0,"code_url":null,"paper_url":"https://arxiv.org/pdf/2606.08285","page_url":"https://thequant.space/flowcharts/execution-realism-and-reproducibility-in-llm-based-trading-systems-a-systematic/","tags":["LLM-based trading systems","Execution realism","Backtesting","Portfolio benchmarking","Reproducibility"]},{"paper_id":"2606.09003","title":"Proof of Stake economy under centralized exchanges--a mean field model","paper_date":"2026-09-23","authors":["Wenpin Tang"],"topics":["crypto-defi"],"asset_classes":["crypto"],"methods":["stochastic-calculus","game-theory"],"paper_type":"theoretical","primary_category":"q-fin.MF","doi":null,"journal_ref":null,"quadrant":"Holy 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Natarajan"],"topics":["reinforcement-learning"],"asset_classes":["equities"],"methods":["reinforcement-learning","stochastic-calculus","machine-learning"],"paper_type":"empirical","primary_category":"cs.LG","doi":null,"journal_ref":null,"quadrant":"Holy Grail","math_complexity":8.0,"empirical_rigor":7.0,"hub_score":7.4,"code_url":null,"paper_url":"https://arxiv.org/pdf/2609.08581v1","page_url":"https://thequant.space/flowcharts/alpharjm-reward-jump-memory-for-stochastic-return-guided-alpha-discovery/","tags":["alpha discovery","symbolic search","reinforcement learning","reward-jump memory","stochastic differential equations"]},{"paper_id":"2609.08605","title":"Numeraire Invariance of Entropy-Projected Martingale Measures","paper_date":"2026-09-08","authors":["Jan Vecer"],"topics":[],"asset_classes":[],"methods":["stochastic-calculus","econophysics-complexity"],"paper_type":"theoretical","primary_category":"q-fin.MF","doi":null,"journal_ref":null,"quadrant":"Lab 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F. Niewzwaag","Marijn G. S. Veth","Manuele Massei","Marcos R. 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Guo"],"topics":["machine-learning","portfolio-optimization"],"asset_classes":["equities"],"methods":["deep-learning","optimization","econometrics-time-series"],"paper_type":"empirical","primary_category":"cs.CE","doi":null,"journal_ref":null,"quadrant":"Holy Grail","math_complexity":7.5,"empirical_rigor":8.0,"hub_score":7.8,"code_url":null,"paper_url":"https://arxiv.org/pdf/2609.25617v1","page_url":"https://thequant.space/flowcharts/hierarchical-multi-task-learning-with-liquidity-aware-signals-for-stock/","tags":["stock price forecasting","deep learning","multi-task learning","liquidity-aware signals","portfolio optimization"]},{"paper_id":"2609.25965","title":"Modeling interest rate swap volatility with GARCH processes","paper_date":"2026-09-22","authors":["Michał Balcerek","Michał 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Soleimani"],"topics":["portfolio-optimization","factor-investing"],"asset_classes":["equities"],"methods":["nlp-llm","machine-learning"],"paper_type":"empirical","primary_category":"econ.EM","doi":null,"journal_ref":null,"quadrant":"Holy Grail","math_complexity":8.5,"empirical_rigor":7.5,"hub_score":7.9,"code_url":null,"paper_url":"https://arxiv.org/pdf/2609.26303v1","page_url":"https://thequant.space/flowcharts/target-alignment-dilution-and-forecast-selection-when-cross-sectional-forecasts/","tags":["forecast correlation","forecast diversity","portfolio selection","language models","equity rankings"]},{"paper_id":"2609.26349","title":"Optimal Investment and Consumption in Financial Markets with Integrated Variance Clocks","paper_date":"2026-09-22","authors":["Eduardo Abi Jaber","Florian Gutekunst","Martin Herdegen","David Hobson"],"topics":["volatility","stochastic-control"],"asset_classes":["derivatives"],"methods":["stochastic-calculus"],"paper_type":"theoretical","primary_category":"q-fin.MF","doi":null,"journal_ref":null,"quadrant":"Lab Rats","math_complexity":9.0,"empirical_rigor":2.0,"hub_score":4.8,"code_url":null,"paper_url":"https://arxiv.org/pdf/2609.26349v1","page_url":"https://thequant.space/flowcharts/optimal-investment-and-consumption-in-financial-markets-with-integrated/","tags":["optimal investment","consumption problem","stochastic clock","stochastic volatility","backward stochastic differential equation"]},{"paper_id":"2609.26445","title":"A Practical Guide on Graphical Model Validation","paper_date":"2026-09-22","authors":["Mario V. Wüthrich"],"topics":["insurance-actuarial"],"asset_classes":["insurance"],"methods":[],"paper_type":"theoretical","primary_category":"stat.ML","doi":null,"journal_ref":null,"quadrant":"Lab Rats","math_complexity":7.5,"empirical_rigor":3.0,"hub_score":4.8,"code_url":null,"paper_url":"https://arxiv.org/pdf/2609.26445v1","page_url":"https://thequant.space/flowcharts/a-practical-guide-on-graphical-model-validation/","tags":["model validation","actuarial modeling","calibration plots","Bregman losses","Gini scores"]},{"paper_id":"2609.26606","title":"Liquidity Provision and Rebate Design in Option Markets","paper_date":"2026-09-22","authors":["Samuel N. Cohen","Lyndon Drake","Zihan Guo","Christoph Reisinger"],"topics":["volatility","market-microstructure","options-derivatives"],"asset_classes":["derivatives"],"methods":["stochastic-calculus"],"paper_type":"empirical","primary_category":"q-fin.MF","doi":null,"journal_ref":null,"quadrant":"Holy Grail","math_complexity":9.0,"empirical_rigor":7.0,"hub_score":7.8,"code_url":null,"paper_url":"https://arxiv.org/pdf/2609.26606v1","page_url":"https://thequant.space/flowcharts/liquidity-provision-and-rebate-design-in-option-markets/","tags":["market making","rebate design","option markets","stochastic volatility","optimal strategies"]},{"paper_id":"2609.27404","title":"When Trust Attracts Fraud: AI and Trust Arbitrage","paper_date":"2026-09-23","authors":["Xieyu Yin","Fenghua Wen"],"topics":[],"asset_classes":[],"methods":["game-theory"],"paper_type":"theoretical","primary_category":"econ.GN","doi":null,"journal_ref":null,"quadrant":"Lab 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component","Low-dimensional linear dynamic subspace","Stock returns"]},{"paper_id":"2609.27632","title":"Compliant AI Infrastructure for Regulated Finance: A tiered multi-agent framework with DLT audit trails for financial operations in DACH","paper_date":"2026-09-23","authors":["Walter Kurz","Reinhard Magg"],"topics":[],"asset_classes":[],"methods":["simulation"],"paper_type":"perspective-policy","primary_category":"q-fin.GN","doi":null,"journal_ref":null,"quadrant":"Philosophers","math_complexity":3.0,"empirical_rigor":2.0,"hub_score":2.4,"code_url":null,"paper_url":"https://arxiv.org/pdf/2609.27632","page_url":"https://thequant.space/flowcharts/compliant-ai-infrastructure-for-regulated-finance-a-tiered-multi-agent/","tags":["Compliance-first architecture","Regulated finance","Governed policy compiler","Permissioned DAG","Assurance by construction"]},{"paper_id":"2609.27636","title":"Multi-Agent AI Architecture for Regulated Insurers: A generic AI framework under Solvency II and the AI Act in Austria and Germany","paper_date":"2026-09-23","authors":["Walter Kurz"],"topics":["insurance-actuarial"],"asset_classes":["insurance"],"methods":["game-theory","simulation","optimization"],"paper_type":"methodological","primary_category":"q-fin.GN","doi":null,"journal_ref":null,"quadrant":"Lab Rats","math_complexity":6.5,"empirical_rigor":3.0,"hub_score":4.4,"code_url":null,"paper_url":"https://arxiv.org/pdf/2609.27636","page_url":"https://thequant.space/flowcharts/multi-agent-ai-architecture-for-regulated-insurers-a-generic-ai-framework-under/","tags":["Multi-agent architecture","Regulated insurance","Risk pooling theory","Principal-Agent theory","Orchestrator agent"]},{"paper_id":"2609.27654","title":"FedIncome: Federated Learning for Income Estimation in Digital Lending Under Data Sovereignty Constraints","paper_date":"2026-09-23","authors":["Sultan Amed","Tanmay Sen","Sayantan Banerjee"],"topics":["risk-management"],"asset_classes":["credit"],"methods":["machine-learning"],"paper_type":"empirical","primary_category":"stat.ML","doi":null,"journal_ref":null,"quadrant":"Holy Grail","math_complexity":6.0,"empirical_rigor":8.0,"hub_score":7.2,"code_url":null,"paper_url":"https://arxiv.org/pdf/2609.27654","page_url":"https://thequant.space/flowcharts/fedincome-federated-learning-for-income-estimation-in-digital-lending-under/","tags":["Federated learning","Income estimation","Data-sharing constraints","LendingClub loans","Credit risk"]},{"paper_id":"2609.28463","title":"Market Completeness and Optional Projections under Restricted Information","paper_date":"2026-09-23","authors":["Levin David Schwab"],"topics":[],"asset_classes":[],"methods":["stochastic-calculus"],"paper_type":"theoretical","primary_category":"q-fin.MF","doi":null,"journal_ref":null,"quadrant":"Lab 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transport","Poincaré–Cartan form"]},{"paper_id":"2609.29108","title":"Functional Architecture of European Electricity Trading Markets: Requirements for AI Supported Trading Systems under Regulatory Constraints","paper_date":"2026-09-24","authors":["Walter Kurz","Wojtek Stricker"],"topics":["market-microstructure","commodities-energy"],"asset_classes":["commodities-energy"],"methods":["optimization","network-graph"],"paper_type":"perspective-policy","primary_category":"cs.AI","doi":null,"journal_ref":null,"quadrant":"Philosophers","math_complexity":3.0,"empirical_rigor":4.0,"hub_score":3.6,"code_url":null,"paper_url":"https://arxiv.org/pdf/2609.29108","page_url":"https://thequant.space/flowcharts/functional-architecture-of-european-electricity-trading-markets-requirements/","tags":["European electricity trading","AI-supported trading","Market-coupling mechanics","Constrained optimization","Fail-closed AI control logic"]},{"paper_id":"2609.29887","title":"Cost-Sensitive Online Window Size Selection for Portfolio Management","paper_date":"2026-09-24","authors":["Yi-Chen Liu","Chung-Han Hsieh"],"topics":[],"asset_classes":[],"methods":["optimization"],"paper_type":"theoretical","primary_category":"math.OC","doi":null,"journal_ref":null,"quadrant":"Lab Rats","math_complexity":8.5,"empirical_rigor":4.0,"hub_score":5.8,"code_url":null,"paper_url":"https://arxiv.org/pdf/2609.29887","page_url":"https://thequant.space/flowcharts/cost-sensitive-online-window-size-selection-for-portfolio-management/","tags":["cost-sensitive learning","online portfolio management","window size selection","online learning","tracking regret"]},{"paper_id":"2609.31345","title":"On the asymptotic shape of quantile surfaces","paper_date":"2026-09-30","authors":["Florian Gach","Simon Hochgerner"],"topics":[],"asset_classes":[],"methods":[],"paper_type":"theoretical","primary_category":"math.ST","doi":null,"journal_ref":null,"quadrant":"Lab 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Aldridge"],"topics":["market-microstructure","crypto-defi","high-frequency-trading"],"asset_classes":["crypto"],"methods":["game-theory"],"paper_type":"survey-review","primary_category":"econ.EM","doi":null,"journal_ref":null,"quadrant":"Philosophers","math_complexity":4.0,"empirical_rigor":3.0,"hub_score":3.4,"code_url":null,"paper_url":"https://arxiv.org/pdf/2609.33058","page_url":"https://thequant.space/flowcharts/evolution-of-market-microstructure-in-the-age-of-ai/","tags":["Market microstructure","Trading rules","Limit order books","High-frequency trading","Automated market makers","Not Applicable"]},{"paper_id":"2609.33470","title":"LiveOption: Evaluating LLM Agents in Structured Option Trading with Nonlinear Payoffs","paper_date":"2026-09-27","authors":["Haochen Luo","Yifan Li","Binh Minh An","Xiaolong Luo","Zhengzhao Lai","Yuan Zhang","Chen Liu"],"topics":["nlp-llm","market-simulation"],"asset_classes":["derivatives","equities"],"methods":["nlp-llm","simulation"],"paper_type":"empirical","primary_category":"cs.AI","doi":null,"journal_ref":null,"quadrant":"Holy Grail","math_complexity":6.0,"empirical_rigor":8.0,"hub_score":7.2,"code_url":null,"paper_url":"https://arxiv.org/pdf/2609.33470","page_url":"https://thequant.space/flowcharts/liveoption-evaluating-llm-agents-in-structured-option-trading-with-nonlinear/","tags":["Large language models (LLMs)","Multi-agent systems","Option trading","Structured sequential decision-making","Portfolio overlays","Derivatives"]},{"paper_id":"2609.33524","title":"EverMine: Dissecting the Self-Evolution of Research Capabilities in Long-Horizon Alpha Research","paper_date":"2026-09-27","authors":["Siyuan Li","Jiangfeng Zhang","Rui Yao","Weihua Qiu","Mingyang Xu","Zixuan Yuan"],"topics":["portfolio-optimization","factor-investing"],"asset_classes":[],"methods":["optimization","machine-learning"],"paper_type":"empirical","primary_category":"cs.AI","doi":null,"journal_ref":null,"quadrant":"Holy Grail","math_complexity":6.0,"empirical_rigor":7.0,"hub_score":6.6,"code_url":null,"paper_url":"https://arxiv.org/pdf/2609.33524","page_url":"https://thequant.space/flowcharts/evermine-dissecting-the-self-evolution-of-research-capabilities-in-long-horizon/","tags":["Self-evolving agents","Alpha discovery","Factor investing","Portfolio optimization","Long-horizon research"]},{"paper_id":"2609.33767","title":"Taming the Greeks: Option Portfolios with Inductive Biases","paper_date":"2026-09-27","authors":["Wee Ling Tan","Stephen Roberts","Stefan 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Discount Factor Portfolios","paper_date":"2026-09-28","authors":["Kelvin J. L. Koa","Xinyang Li","Ke-Wei Huang"],"topics":["portfolio-optimization"],"asset_classes":["macro-multi-asset"],"methods":["nlp-llm","deep-learning","optimization"],"paper_type":"empirical","primary_category":"cs.LG","doi":null,"journal_ref":null,"quadrant":"Holy Grail","math_complexity":7.5,"empirical_rigor":8.0,"hub_score":7.8,"code_url":null,"paper_url":"https://arxiv.org/pdf/2609.35086","page_url":"https://thequant.space/flowcharts/retrieval-augmented-diffusion-modeling-for-stochastic-discount-factor-portfolios/","tags":["Portfolio optimization","Stochastic discount factor (SDF)","Market state representations","Retrieval-augmented diffusion","Multimodal data","Multi-asset"]},{"paper_id":"2609.35359","title":"From Cointegration to Out-of-Sample Failure: A Pairs-Trading Case Study on PEP-KO","paper_date":"2026-09-28","authors":["Davide Graziano"],"topics":["statistical-arbitrage","options-derivatives"],"asset_classes":["equities"],"methods":["econometrics-time-series"],"paper_type":"empirical","primary_category":"q-fin.ST","doi":null,"journal_ref":null,"quadrant":"Holy Grail","math_complexity":6.5,"empirical_rigor":8.0,"hub_score":7.4,"code_url":null,"paper_url":"https://arxiv.org/pdf/2609.35359","page_url":"https://thequant.space/flowcharts/from-cointegration-to-out-of-sample-failure-a-pairs-trading-case-study-on-pep-ko/","tags":["Pairs trading","Cointegration","Mean-reversion","Hedge ratios","Sharpe ratio","Equities"]},{"paper_id":"2609.35744","title":"FinAutoRubric: Expert-Guided Automatic Rubric Generation for Evaluating Financial Research Agents","paper_date":"2026-09-28","authors":["Hoyoung Lee","Suyeol Yun","Jack Haverty","Yunju Cho","Meesong Kim","Daekyung Park","Sumin Kim","Jihoon Kwon","Jasmine Jia Geng","Andrew Chin","Yin Luo","Edward Tong","Yu Yu","Zach Golkhou","Minkyu Kim","Igor Halperin","Young Cha","Alejandro Lopez-Lira","Chanyeol Choi","Yongjae Lee"],"topics":["nlp-llm"],"asset_classes":[],"methods":["nlp-llm"],"paper_type":"perspective-policy","primary_category":"cs.AI","doi":null,"journal_ref":null,"quadrant":"Philosophers","math_complexity":0.0,"empirical_rigor":0.0,"hub_score":0.0,"code_url":null,"paper_url":"https://arxiv.org/pdf/2609.35744","page_url":"https://thequant.space/flowcharts/finautorubric-expert-guided-automatic-rubric-generation-for-evaluating/","tags":["Finance research agents","Evaluation rubrics","Large language models (LLMs)","Task bank","Automated grading","Not Applicable"]},{"paper_id":"2609.36405","title":"Finite-Horizon Reversible Investment under Multi-Factor Dynamics","paper_date":"2026-09-29","authors":["Junkee Jeon","Takwon Kim","Jinwan Park","A. Max Reppen"],"topics":["stochastic-control"],"asset_classes":[],"methods":["stochastic-calculus"],"paper_type":"theoretical","primary_category":"q-fin.MF","doi":null,"journal_ref":null,"quadrant":"Lab Rats","math_complexity":9.0,"empirical_rigor":4.0,"hub_score":6.0,"code_url":null,"paper_url":"https://arxiv.org/pdf/2609.36405","page_url":"https://thequant.space/flowcharts/finite-horizon-reversible-investment-under-multi-factor-dynamics/","tags":["Reversible investment","Capacity adjustment","Singular control","Double-obstacle problems","Free boundaries"]},{"paper_id":"2609.36631","title":"A Spread-Gated Hawkes-Flocking Model for Best Bid and Ask Dynamics, with an Application to Limit Order Placement","paper_date":"2026-09-29","authors":["Hyoeun Lee","Kiseop Lee"],"topics":["market-microstructure","high-frequency-trading"],"asset_classes":[],"methods":["econometrics-time-series","stochastic-calculus"],"paper_type":"empirical","primary_category":"q-fin.CP","doi":null,"journal_ref":null,"quadrant":"Holy Grail","math_complexity":8.0,"empirical_rigor":7.0,"hub_score":7.4,"code_url":null,"paper_url":"https://arxiv.org/pdf/2609.36631","page_url":"https://thequant.space/flowcharts/a-spread-gated-hawkes-flocking-model-for-best-bid-and-ask-dynamics-with-an/","tags":["Limit order book","Hawkes-flocking model","Bid-ask spread","Optimal order placement","Intraday data"]},{"paper_id":"2609.37108","title":"The Efficient Frontier from a LASSO Solver","paper_date":"2026-09-29","authors":["Thomas Schmelzer"],"topics":["portfolio-optimization"],"asset_classes":[],"methods":["machine-learning","optimization"],"paper_type":"empirical","primary_category":"q-fin.CP","doi":null,"journal_ref":null,"quadrant":"Holy 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impact","Order flow","Latent traders","Generalized Langevin equation","Square-root impact law"]},{"paper_id":"2609.37881","title":"Global Structure and Local Specifications in Sublinear Valuation","paper_date":"2026-09-29","authors":["Jongjin Park","David Criens","Hyungbin Park"],"topics":["stochastic-control"],"asset_classes":[],"methods":["stochastic-calculus"],"paper_type":"theoretical","primary_category":"q-fin.MF","doi":null,"journal_ref":null,"quadrant":"Lab Rats","math_complexity":9.0,"empirical_rigor":1.0,"hub_score":4.2,"code_url":null,"paper_url":"https://arxiv.org/pdf/2609.37881","page_url":"https://thequant.space/flowcharts/global-structure-and-local-specifications-in-sublinear-valuation/","tags":["Sublinear valuation rules","Uncertainty structures","Markovian framework","Hamilton-Jacobi-Bellman equation","Model recovery"]},{"paper_id":"2609.37903","title":"From Intraday Orderbook to Imbalance Price: Understanding Cross-Market Interaction","paper_date":"2026-09-29","authors":["Runyao Yu","Jochen L. Cremer","Pierre Pinson","Jalal Kazempour","Leo Semmelmann","Takuji Matsumoto","Derek W. Bunn"],"topics":["commodities-energy","high-frequency-trading"],"asset_classes":["commodities-energy"],"methods":[],"paper_type":"empirical","primary_category":"q-fin.CP","doi":null,"journal_ref":null,"quadrant":"Street Traders","math_complexity":4.0,"empirical_rigor":7.0,"hub_score":5.8,"code_url":null,"paper_url":"https://arxiv.org/pdf/2609.37903","page_url":"https://thequant.space/flowcharts/from-intraday-orderbook-to-imbalance-price-understanding-cross-market/","tags":["Electricity markets","Intraday trading","Balancing market","Price formation","Orderbook information"]},{"paper_id":"2609.37963","title":"Not All LPs Are Equal: The Active-Passive Gap in Automated Market Maker Liquidity Provision","paper_date":"2026-09-29","authors":["Agathe Sadeghi","Dingyue Liu","Ciamac Moallemi","Xin Wan","Brian Zhu"],"topics":["crypto-defi","market-microstructure"],"asset_classes":["crypto"],"methods":[],"paper_type":"empirical","primary_category":"q-fin.TR","doi":null,"journal_ref":null,"quadrant":"Holy Grail","math_complexity":6.5,"empirical_rigor":8.0,"hub_score":7.4,"code_url":null,"paper_url":"https://arxiv.org/pdf/2609.37963","page_url":"https://thequant.space/flowcharts/not-all-lps-are-equal-the-active-passive-gap-in-automated-market-maker/","tags":["Automated market makers (AMMs)","Liquidity provision","Uniswap","LP profitability","Adverse selection"]},{"paper_id":"2609.38765","title":"Multiperiod bond portfolio optimization with transaction costs using a Markov Decision process","paper_date":"2026-09-30","authors":["Balaji Ramachandran","Srikanth Iyer","Shashi Jain"],"topics":["fixed-income","reinforcement-learning","portfolio-optimization"],"asset_classes":["fixed-income"],"methods":["reinforcement-learning","optimization","econometrics-time-series"],"paper_type":"empirical","primary_category":"q-fin.CP","doi":null,"journal_ref":null,"quadrant":"Holy Grail","math_complexity":8.0,"empirical_rigor":7.0,"hub_score":7.4,"code_url":null,"paper_url":"https://arxiv.org/pdf/2609.38765","page_url":"https://thequant.space/flowcharts/multiperiod-bond-portfolio-optimization-with-transaction-costs-using-a-markov/","tags":["Bond Portfolio Optimization","Interest Rate Risk","Yield Curve Dynamics","Markov Decision Process","Bank Treasury"]},{"paper_id":"2609.39256","title":"Stochastic Knothe-Rosenblatt: Light-speed Calibration of Stochastic Local Volatility Models","paper_date":"2026-09-30","authors":["Mathias Beiglböck","Manuel Hasenbichler","Gudmund Pammer"],"topics":["volatility","options-derivatives"],"asset_classes":["derivatives"],"methods":["stochastic-calculus"],"paper_type":"empirical","primary_category":"q-fin.PR","doi":null,"journal_ref":null,"quadrant":"Holy Grail","math_complexity":9.0,"empirical_rigor":6.0,"hub_score":7.2,"code_url":null,"paper_url":"https://arxiv.org/pdf/2609.39256","page_url":"https://thequant.space/flowcharts/stochastic-knothe-rosenblatt-light-speed-calibration-of-stochastic-local/","tags":["European Options","Option Smiles","Martingale Calibration","Local Volatility","Stochastic Volatility"]},{"paper_id":"2609.39261","title":"Jacobian Rank Collapse in Decision-Focused Learning","paper_date":"2026-09-30","authors":["Aojie Yuan","Haiyue Zhang","Zijian Su"],"topics":[],"asset_classes":[],"methods":["optimization","machine-learning"],"paper_type":"empirical","primary_category":"cs.LG","doi":null,"journal_ref":null,"quadrant":"Holy 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