---
title: Predicting Intraday Risk and Liquidity with News Analytics
url: https://www.ml-quant.com/papers/ssrn/4987091/
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 4987091
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4987091
featured: 2024-10-17
citations: unknown
topic: LLMs & Text
---


# Predicting Intraday Risk and Liquidity with News Analytics

The research investigates the correlation between the intensity of news arrival, volatility, and volume at an intraday frequency using a global dataset and natural language processing.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4987091
- Identifier: SSRN 4987091
- Released: 2023-06-30
- First featured: Quant Letter No. 70 (2024-10-17): https://www.ml-quant.com/issues/2024-10-17/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: LLMs & Text

## Related

- [Planning In Natural Language Improves LLM Search For Code Generation](https://www.ml-quant.com/papers/arxiv/2409.03733/): PLANSEARCH is a new search algorithm that creates diverse solutions for natural language problems, outperforming traditional methods in various benchmarks.
- [Fine-Tuning and Prompt Optimization: Two Great Steps that Work Better Together](https://www.ml-quant.com/papers/arxiv/2407.10930/): The article explores a method to enhance Natural Language Processing systems by simultaneously optimizing language model weights and prompting strategies, leading to significant improvements in tasks like multi-hop QA and mathematical reasoning.
- [AmbigNLG: Addressing Task Ambiguity in Instruction for NLG](https://www.ml-quant.com/papers/arxiv/2402.17717/): Task Ambiguity in NLG: AmbigNLG, a new task and dataset, tackles task ambiguity in instructions for Natural Language Generation, improving the alignment of generated text with user expectations and boosting the performance of Large Language Models.
- [No Train, all Gain: Self-Supervised Gradients Improve Deep Frozen Representations](https://www.ml-quant.com/papers/arxiv/2407.10964/): The FUNGI method improves transformer encoders' features using self-supervised gradients, enhancing performance in vision, natural language processing, and audio tasks and datasets.
- [SYNTHEVAL: Hybrid Behavioral Testing of NLP Models with Synthetic CheckLists](https://www.ml-quant.com/papers/arxiv/2408.17437/): SYNTHEVAL is a testing framework that uses large language models to generate tests for evaluating NLP models, particularly in sentiment analysis and toxic language detection.
- [Beyond Scale: The Diversity Coefficient as a Data Quality Metric for Variability in Natural Language Data](https://www.ml-quant.com/papers/arxiv/2306.13840/): The study introduces a measure called the diversity coefficient to formalize data quality in pre-training Large Language Models (LLMs), demonstrating its alignment with diversity and variability properties, and its usefulness in evaluating downstream model performance.