Research paper / RESOURCE PROFILE

LLMFactor: Extracting Profitable Factors through Prompts

Uses LLM prompting to extract explainable return-predictive factors from news text.

Research & Papers
arxiv.orgPublished Jun 16, 2024
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Screenshot unavailableResource information and source links remain available. LLMFactor: Extracting Profitable Factors through Prompts — reviewed source website screenshot
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Overview

Uses LLM prompting to extract explainable return-predictive factors from news text.

Topics & capabilities

reinforcement-learningalphapaperllm

Publication information

Publication title
LLMFactor: Extracting Profitable Factors through Prompts for Explainable Stock Movement Prediction
Authors
Meiyun Wang, Kiyoshi Izumi, Hiroki Sakaji
Published
Jun 16, 2024
Last publication revision
Jun 16, 2024
Subject areas
cs.CL, cs.AI, cs.CE
Publication source
arxiv.org · abs/2406.10811v1 ↗

Source: arXiv API · observed Oct 11, 2026.

Source links & provenance

https://arxiv.org/abs/2406.10811 ↗

Discovery references

References explain how this resource entered the collection. They do not represent an endorsement.

Explore related resources

Screenshot unavailableResource information and source links remain available. DeepIntoStreams/TSFM_Finance — reviewed source website screenshot
RepositoryChecked capture

DeepIntoStreams/TSFM_Finance

github.com

the official implementation of the paper “Re(visiting) Time Series Foundation Models in Finance”.

GitHub stars
31
Language
Jupyter Notebook
Code license
Apache-2.0
foundation-modeltime-seriesresearch-lab
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96 Authority 96 / 100 Cited by 2 resources in this directory 2 discovery sources Directory record updated: 2026-10-11 How scoring works →

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