Topic
ML & AI Methods
Machine-learning methods applied to finance: deep learning, boosting, RL and new architectures.
- Papers featured
- 1,107
- Last 12 months
- 41
- Cited 100+
- 60
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- SSRN
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Most cited
Featured papers in this topic with the most citations today.
- 5 Jun 20249,205cites
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
Sequence Modeling: Mamba, a neural network architecture that doesn't use attention or MLP blocks, provides faster inference and better performance in language, audio, and genomics than Transformers.
Machine learning
- 5 Jun 20241,953cites
Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality
The research identifies a link between state-space models and Transformers in deep learning, leading to the creation of a faster language modeling architecture, Mamba-2.
Machine learningIn International Conference on Machine Learning
- 22 May 20241,880cites
Octo: An Open-Source Generalist Robot Policy
Octo is a large transformer-based policy for robotic manipulation, trained on a vast dataset, that can be instructed via language or images and adapted to new domains.
Machine learningIn Robotics: Science and Systems Conference
- 24 Apr 20241,418cites
Mastering Diverse Domains through World Models
Algorithm Mastery: DreamerV3, a universal algorithm, excels in over 150 varied tasks, including diamond collection in Minecraft without human input, expanding the scope of reinforcement learning.
Machine learning
- 10 Jul 20241,173cites
SimPO: Simple Preference Optimization with a Reference-Free Reward
Simple Preference Optimization: SimPO improves reinforcement learning from human feedback by using the average log probability of a sequence as the implicit reward, enhancing training stability and computational efficiency.
Machine learningIn Neural Information Processing Systems
- 12 Jun 20241,169cites
Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction
The article discusses Visual AutoRegressive modeling (VAR), a new image learning method that outperforms diffusion transformers in terms of speed, image quality, and scalability.
Machine learningIn Neural Information Processing Systems
- 3 Jan 2024967cites
PixArt-α: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis
PIXART-$\alpha$, a Transformer-based text-to-image model, generates high-quality images at a low cost, reducing CO2 emissions and offering a cost-effective solution for the AIGC community.
Machine learningIn International Conference on Learning Representations
- 8 May 2024727cites
Fourier Neural Operator with Learned Deformations for PDEs on General Geometries
The study introduces geo-FNO, a new framework for solving partial differential equations on any geometry, proving to be faster and more accurate than standard and machine learning-based solvers.
Machine learningIn J. Mach. Learn. Res.
- 3 Apr 2024660cites
LightGaussian: Unbounded 3D Gaussian Compression with 15x Reduction and 200+ FPS
LightGaussian is a new method that converts 3D Gaussians into a more compact format, enhancing efficiency in real-time neural rendering and reducing storage needs.
Machine learningIn Neural Information Processing SystemsFeatured 2×
- 13 Dec 2023539cites
Gated Linear Attention Transformers with Hardware-Efficient Training
Efficient Training of Gated Linear Attention Transformers: The research introduces a more hardware-efficient version of gated linear attention Transformers that performs well against other models, especially in training on longer sequences.
Machine learningIn International Conference on Machine Learning
- 22 May 2024482cites
CAT3D: Create Anything in 3D with Multi-View Diffusion Models
Multi-View Diffusion Models: CAT3D is a novel technique for generating 3D scenes from any number of images, surpassing existing methods in speed and efficiency.
Machine learningIn Neural Information Processing Systems
- 8 May 2024446cites
Capabilities of Gemini Models in Medicine
Med-Gemini, an AI model for medical applications, outperforms previous models and human experts in medical benchmarks, indicating potential for real-world medical use.
Machine learning
Latest
- 25 Sep 20260cites
Propose, Don't Judge: An Anytime-Valid Referee for LLM Agents That Mine Investment Factors
Proposes a statistical referee that judges investment factors proposed by language-model agents using out-of-sample market outcomes, ensuring false-discovery control at any stopping time.
arXiv
- 25 Sep 20260cites
AlphaDiverse: Post-Training Local Quantitative Research Agents for Diverse Exploration in Alpha Factor Mining
Proposes a multi-agent system with post-training that automates alpha factor mining locally, using diverse research paths and joint optimization to broaden exploration while maintaining prediction quality.
arXiv
- 25 Sep 20260cites
Forecast-Dojo: Replayable Environments for Benchmarking and Training LLM Forecasting Agents
Introduces a replayable environment combining 1,568 resolved prediction-market questions with 18.8M dated news articles to benchmark and train language-model forecasting agents on historical data.
arXiv
- 25 Sep 20264fanfare
Artificial intelligence and financial markets
A survey examines how AI transforms information production, intermediation, and market structure, with implications for efficiency, competition and financial stability.
SSRN
- 25 Sep 20264fanfare
Label alchemy: Target engineering for improved stock selection
Reshaping the prediction target through location, scale and shape transformations raises long-short Sharpe from 0.68 to 1.69, with label choice mattering more than model choice.
SSRN
- 25 Sep 20263fanfare
Memorisation or Alpha? Detecting Look-Ahead Contamination in Cross-Sectional Equity Signals
Testing whether a large language model ranks stocks by forecasting or memory, the study finds a significant information-coefficient gap of 0.185 inside versus outside its training window, suggesting substantial look-ahead contamination.
SSRN
- 25 Sep 20263fanfare
Beta Recall, Alpha Recall, and a Contamination Detector that Needs No Labels * Measuring Training-data Leakage in LLM Equity Signals
The study measures recall versus forecasting in an LLM's stock rankings by comparing cross-sectional information coefficients inside and outside the training window.
SSRN
- 25 Sep 20263fanfare
Execution-Aware Alpha Mining: Teaching LLM Factor Agents to Account for Trading Costs
The paper builds a closed-loop system where an LLM proposes equity factors penalized for execution costs and shows that accounting for trading costs dramatically improves net performance.
SSRN
- 25 Sep 20264fanfare
Assessing the Benefits of Optimized Agentic AI Systems for Asset Pricing
Optimized AI systems analyzing earnings call transcripts double explained variation in stock returns versus standard benchmarks while improving interpretability through human-readable decision rules.
RePEc
- 25 Sep 20263fanfare
Predicting Financial Market Stress with Machine Learning
Tree-based machine learning models predict the full distribution of financial market stress 27% better than traditional time-series methods, with macro uncertainty and monetary policy expectations as key drivers.
RePEc
- 25 Sep 20263fanfare
Ex Machina: Financial Stability in the Age of Artificial Intelligence
Q-learning and large language model investors generate systematically different behaviors in fund redemption settings, with Q-learning showing excessive coordination and amplified fragility under default risk.
RePEc
- 3 Apr 20260cites
Economics of Human and AI Collaboration: When is Partial Automation More Attractive than Full Automation?
The paper discusses a model that suggests combining human effort with partial automation is usually cheaper and more effective than fully automating complex tasks.
arXiv
- 4 Mar 20261cites
The Inference Bottleneck: Antitrust and Neutrality Duties in the Age of Cognitive Infrastructure
The article explains that the rise of generative AI is changing competition by focusing on ongoing decision-making, which can lead to unfair practices in the market. It suggests a solution called Neutral Inference to promote fairness and transparency.
arXiv
- 2 Feb 20262cites
Bounded Normative Equivalence in Human-AI Cooperation: Group Behaviour, Not Partner Labels, Predicts Cooperation under Anonymous Aggregate Feedback
AI agents in group settings can influence cooperation just like humans do, showing that social norms can adjust for both AI and human participants.
arXiv
- 28 Dec 20255cites
Gingado: A Machine Learning Library Focused on Economics and Finance
ML for Economics: Gingado is a developing Python library that helps incorporate machine learning into economic research by enhancing datasets and evaluating models.
SSRNFeatured 2×
- 28 Dec 20250cites
Decoding the Unique Price Behavior in the Japanese Stock Market with Convolutional Neural Networks
Analyzing Japanese stock charts with CNN reveals predictive patterns for returns, independent of common momentum trends.
SSRNFeatured 2×
- 28 Dec 20251cites
The Banker in Your Social Network
The research indicates that social financial advice significantly boosts stock market participation, especially through close social ties.
SSRNFeatured 2×
- 19 Dec 20250cites
SigMA: Path Signatures and Multi-head Attention for Learning Parameters in fBm-driven SDEs
The SigMA neural architecture improves parameter estimation in stochastic differential equations using path signatures and self-attention, outperforming traditional methods across multiple datasets.
arXivIn Neurocomputing
- 19 Dec 20250cites
Deep Learning and Elicitability for McKean-Vlasov FBSDEs With Common Noise
A new numerical method combines elicitability and deep learning for McKean-Vlasov stochastic equations, enabling efficient training of neural networks without expensive simulations, tested successfully on financial models.
arXiv
- 19 Dec 20251cites
Adaptive Partitioning and Learning for Stochastic Control of Diffusion Processes
An adaptive reinforcement learning algorithm enhances learning in controlled diffusion processes by partitioning state-action spaces, providing theoretical guarantees and effective results in applications like portfolio selection.
arXiv
- 14 Dec 20251cites
Reinforcement Learning in Financial Decision Making: A Systematic Review of Performance, Challenges, and Implementation Strategies
Reinforcement learning improves financial decision-making by simplifying complex investment problems, emphasizing clear explanations and strong reliability over complex algorithms.
arXivFeatured 2×
- 14 Dec 20251cites
When Medical AI Explanations Help and When They Harm
AIgenerated explanations can improve decision-making when algorithms are right, but can mislead when they're wrong, highlighting a paradox in AI transparency for doctors.
arXiv
- 14 Dec 20251cites
Measuring Computer Science Enthusiasm: A Questionnaire-Based Analysis of Age and Gender Effects on Students' Interest
Research indicates that age affects students' interest in computer science more than gender, suggesting educational approaches should consider developmental changes to boost engagement.
arXiv
- 14 Dec 202514cites
The Adoption and Usage of AI Agents: Early Evidence from Perplexity
A study of AI agent use with the Comet browser reveals that personal productivity and learning are the main reasons users interact with these tools.
arXiv
- 1 Dec 20250cites
AI-Powered Direct Indexing: Exploring Thematic Universes for Enhanced Risk-Adjusted Returns
The research presents FINDALL, a search engine that effectively identifies relevant stocks for direct indexing, outperforming traditional ETFs with lower costs.
SSRNFeatured 2×
- 1 Dec 20250cites
The Risk-Adjusted Intelligence Dividend: A Quantitative Framework for Measuring AI Return on Investment Integrating ISO 42001 and Regulatory Exposure
Organizations using AI face challenges in traditional ROI calculations due to risks, leading to a new framework that includes risk assessments.
arXiv
- 1 Dec 20252cites
Standardized Threat Taxonomy for AI Security, Governance, and Regulatory Compliance
The use of AI in regulated industries exposes gaps between technical risk assessments and compliance, prompting a new taxonomy for AI risk evaluation and financial impacts.
arXiv
- 1 Dec 20250cites
Tacit Bidder-Side Collusion: Artificial Intelligence in Dynamic Auctions
Research shows that large language models can indirectly collaborate in Dutch auctions to increase prices, influenced by market structure.
arXiv
- 4 Nov 20253cites
The Economics of AI Training Data: A Research Agenda
Lays out data economics: why data is special, documents AI training-data deals, proposes a hierarchy of data units, and lists key research questions.
arXiv
- 4 Nov 20250cites
Estimating Nationwide High-Dosage Tutoring Expenditures: A Predictive Model Approach
Machine learning on incomplete ESSER plans estimates U.S. school districts spent about $2.2 billion on high‑dosage tutoring during COVID.
arXiv