---
title: News Text Analysis
url: https://www.ml-quant.com/papers/repec/oup-rfinst-v-36-y-2023-i-12-p-4759-4787/
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: RePEc:oup:rfinst:v:36:y:2023:i:12:p:4759-4787.
source_url: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fhdl.handle.net%2F10.1093%2Frfs%2Fhhad042%3Bh%3Drepec%3Aoup%3Arfinst%3Av%3A36%3Ay%3A2023%3Ai%3A12%3Ap%3A4759-4787.
featured: 2024-10-17
citations: unknown
topic: LLMs & Text
---


# News Text Analysis

A study reveals that a pricing model based on news text from The Wall Street Journal is more effective in predicting investment opportunities than traditional models, using topic modeling and latent factor analysis.

- Source: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fhdl.handle.net%2F10.1093%2Frfs%2Fhhad042%3Bh%3Drepec%3Aoup%3Arfinst%3Av%3A36%3Ay%3A2023%3Ai%3A12%3Ap%3A4759-4787.
- Identifier: RePEc:oup:rfinst:v:36:y:2023:i:12:p:4759-4787.
- Released: 2023-09-04
- 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

- [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://www.ml-quant.com/papers/arxiv/2402.03300/): Advancing Math Reasoning in Language Models: DeepSeekMath7B is a new language model that uses web data and Group Relative Policy Optimization for advanced mathematical reasoning, scoring high on the MATH benchmark.
- [Mistral 7B](https://www.ml-quant.com/papers/arxiv/2310.06825/): Superior Language Model: Mistral 7B v0.1 is a language model with 7 billion parameters that excels in reasoning, mathematics, and code generation, and has a version specifically designed to follow instructions.
- [Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters](https://www.ml-quant.com/papers/arxiv/2408.03314/): The research investigates enhancing Large Language Models' (LLMs) performance using more test-time computation, suggesting a compute-optimal scaling strategy based on prompt difficulty.
- [Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling](https://www.ml-quant.com/papers/arxiv/2412.05271/): The paper presents InternVL 2.5, a sophisticated multimodal large language model that performs well on various benchmarks, exceeding 70% on the MMMU benchmark.
- [AWQ: Activation-aware Weight Quantization for On-Device LLM Compression and Acceleration](https://www.ml-quant.com/papers/arxiv/2306.00978/): The study suggests Activation-aware Weight Quantization (AWQ), a hardware-friendly method for quantizing large language models that reduces error and improves performance on various benchmarks.
- [s1: Simple test-time scaling](https://www.ml-quant.com/papers/arxiv/2501.19393/): The research presents a method called budget forcing, which uses a small dataset to achieve test-time scaling and improved reasoning performance in language modeling, particularly in competition math questions.