Measuring ESG Content in Financial Text with Frozen LLM Activations: Concept Vectors and Linear Probing

From Word Counts to Context: Topic Models for Asset Pricing

News may reveal systematic risk, but whether its context enhances the construction of systematic risk factors is still unclear. We seek to test whether utilizing a sentence transformer represents an improvement over techniques such as Latent Dirichlet Allocation (LDA) in the coherence of topic term lists generated from...

Master Modeler: Learning Institutional Portfolio Construction with Large Language Models

Investment institutions exert substantial influence on asset prices, liquidity, and market stability, making the ability to forecast their portfolio adjustments both academically and practically important. We study modeling investment institutions by fine-tuning large language models (LLMs) to predict next-quarter chan...

Quantifying material risks from textual disclosures in financial statements using LLMs and LLM agents – A case study on physical risks

Benchmarking Embedding Models for ESG Data

The use of Environmental, Social, and Governance (ESG) data is fundamental for modern corporate accountability, sustainability reporting, and financial decision-making. Embedding models have emerged as a powerful approach for transforming unstructured ESG text into numerical representations suitable for downstream natu...

FinRegQA-EU: Corruption-Based Preference Data for Grounded EU Financial Regulatory Question Answering

Large Language Models (LLMs) struggle with region-specific factual knowledge, particularly in financial regulation. While benchmarks such as CFinBench, provide broad coverage of financial knowledge in other regions, no comparable resource exists for European financial regulation. We close this gap by proposing an end-t...

Online Reviews in SKU-level Demand Forecasting Using Global Cross-Learning LSTM

The findings support Proxy Theory by indicating that review volume and valence can provide leading information before sufficient transaction data accumulate and inform replenishment and safety-stock decisions by reducing underforecasting during demand activation.