---
title: Generative AI in Capital Markets
url: https://www.ml-quant.com/papers/ssrn/5226562/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: SSRN 5226562
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5226562
featured: 2025-04-23
citations: unknown
topic: ML & AI Methods
---


# Generative AI in Capital Markets

AI use in financial analysis on Seeking Alpha platform boosts productivity and liquidity for undercovered firms, but provides less information to capital market participants than human articles.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5226562
- Identifier: SSRN 5226562
- Released: 2025-04-22
- First featured: Quant Letter No. 94 (2025-04-23): https://www.ml-quant.com/issues/2025-04-23/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: ML & AI Methods

## Related

- [CAT3D: Create Anything in 3D with Multi-View Diffusion Models](https://www.ml-quant.com/papers/arxiv/2405.10314/): 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.
- [Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models](https://www.ml-quant.com/papers/arxiv/2501.01423/): The paper proposes a new model, VA-VAE, that aligns the latent space with pre-trained vision foundation models, enabling faster convergence of Diffusion Transformers in high-dimensional latent spaces and achieving top performance on ImageNet 256x256 generation.
- [Inference-Time Scaling for Diffusion Models beyond Scaling Denoising Steps](https://www.ml-quant.com/papers/arxiv/2501.09732/): The research shows that increasing computation during inference-time can enhance the quality of samples produced by diffusion models, especially in image generation.
- [Adjoint Matching: Fine-tuning Flow and Diffusion Generative Models with Memoryless Stochastic Optimal Control](https://www.ml-quant.com/papers/arxiv/2409.08861/): The study presents Adjoint Matching, a new algorithm that enhances dynamical generative models by refining reward fine-tuning, leading to improved consistency, realism, and adaptability to unseen human preference reward models.
- [ShieldGemma: Generative AI Content Moderation Based on Gemma](https://www.ml-quant.com/papers/arxiv/2407.21772/): ShieldGemma is a safety content moderation model that excels in predicting safety risks such as explicit content and hate speech, surpassing models like LlamaGuard and WildCard.
- [T2V-CompBench: A Comprehensive Benchmark for Compositional Text-to-video Generation](https://www.ml-quant.com/papers/arxiv/2407.14505/): Text-to-Video Benchmark: TV-CompBench, a new benchmark for evaluating text-to-video generative models, shows that current models struggle with composing various elements into a video.
