Paper: arXiv 2508.16936

Authors: Hoyoung Lee, Wonbin Ahn, Suhwan Park, Jaehoon Lee, Minjae Kim, Sungdong Yoo, Taeyoon Lim, Woohyung Lim, Yongjae Lee

Abstract

Thematic investing, which aims to construct portfolios aligned with structural trends, remains a challenging endeavor due to overlapping sector boundaries and evolving market dynamics. A promising direction is to build semantic representations of investment themes from textual data. However, despite their power, general-purpose LLM embedding models are not well-suited to capture the nuanced characteristics of financial assets, since the semantic representation of investment assets may differ fundamentally from that of general financial text. To address this, we introduce THEME, a framework that fine-tunes embeddings using hierarchical contrastive learning. THEME aligns themes and their constituent stocks using their hierarchical relationship, and subsequently refines these embeddings by incorporating stock returns. This process yields representations effective for retrieving thematically aligned assets with strong return potential. Empirical results demonstrate that THEME excels in two key areas. For thematic asset retrieval, it significantly outperforms leading large language models. Furthermore, its constructed portfolios demonstrate compelling performance. By jointly modeling thematic relationships from text and market dynamics from returns, THEME generates stock embeddings specifically tailored for a wide range of practical investment applications.

Complexity vs Empirical Score

  • Math Complexity: 6.5/10
  • Empirical Rigor: 6.0/10 (capped from 8.0: transaction costs not modelled; see penalty flags)
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper employs advanced machine learning techniques like hierarchical contrastive learning and embedding fine-tuning, representing significant mathematical and methodological complexity. It is highly empirical, with a custom dataset (TRS), clear backtesting for portfolio performance, and quantitative comparisons to baseline models like LLMs.

Research Flowchart

  flowchart TD
  A["Research Goal"] --> B["Data Collection"]
  subgraph B ["Inputs"]
      B1["Thematic Text"]
      B2["Stock Returns"]
  end
  B --> C{"THEME Framework"}
  C --> D["Hierarchical Contrastive Learning"]
  D --> E["Align Themes & Stocks"]
  E --> F["Incorporate Returns"]
  F --> G["Refined Embeddings"]
  G --> H["Outcomes"]
  subgraph H ["Findings"]
      H1["Superior Asset Retrieval"]
      H2["High-Performance Portfolios"]
  end

Cited References (31)

  1. Linq-Embed-Mistral Technical Report (2024) — link
  2. SimStock : Representation Model for Stock Similarities (2023) — link
  3. Mapping Companies to Long-Term Themes Using Domain-Specific Language Models (2023) — link
  4. Learning Company Similarities from SEC Filings Using Transformer-Based Embeddings (2023) — link
  5. SETN: Stock Embedding via Text and Network for Thematic Portfolio Construction (2024) — link
  6. GPT-4 Technical Report (2023)
  7. BloombergGPT: A Large Language Model for Finance (2023) — link
  8. VoyageAI (May 25 version) (2025)
and 23 more
  1. FinMTEB: Finance Massive Text Embedding Benchmark (2025) — link
  2. Gemini (May 25 version) (2025)
  3. C-Pack: Packaged Resources To Advance General Chinese Embedding (2023) — link
  4. Unsupervised cross-lingual representation learning at scale (2019) — link
  5. Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks (2019)
  6. Augmented SBERT: Data Augmentation Method for Improving Bi-Encoders for Pairwise Sentence Scoring Tasks (2020)
  7. Multilingual E5 Text Embeddings: A Technical Report (2024) — link
  8. Jasper and Stella: distillation of SOTA embedding models (2025) — link
  9. Towards general text embeddings with multi-stage contrastive learning (2023) — link
  10. Improving Text Embeddings with Large Language Models (2023) — link
  11. Text Embeddings by Weakly-Supervised Contrastive Pre-training (2022) — link
  12. Generative Representational Instruction Tuning (2024) — link
  13. SFR-Embedding-Mistral:Enhance Text Retrieval with Transfer Learning (2024) — link
  14. Temporal Knowledge Graphs for Dynamic Financial Event Modeling (2023) — link
  15. Empirical Asset Pricing via Machine Learning (2020) — link
  16. FinLLM: Financial Large Language Models for Algorithmic Trading (2023) — link
  17. KGTransformer: Temporal Knowledge Graph Transformer for Financial Event Prediction (2023) — link
  18. LLMs analyzing the analysts: Do BERT and GPT extract more value from financial analyst reports? (2023)
  19. A survey of large language models for financial applications: Progress, prospects and challenges — → 2406.11903
  20. An Overview of Machine Learning for Asset Management. (2023)
  21. An Overview of Machine Learning for Portfolio Optimization. (2024)
  22. Your AI, Not Your View: The Bias of LLMs in Investment Analysis — → 2507.20957
  23. Lora: Low-rank adaptation of large language models. (2022)

2 of 31 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.