Paper: arXiv 2503.01886

Authors: Umair Zakir, Evan Daykin, Amssatou Diagne, Jacob Faile

Abstract

This study presents a comparative analysis of deep learning methodologies such as BERT, FinBERT and ULMFiT for sentiment analysis of earnings call transcripts. The objective is to investigate how Natural Language Processing (NLP) can be leveraged to extract sentiment from large-scale financial transcripts, thereby aiding in more informed investment decisions and risk management strategies. We examine the strengths and limitations of each model in the context of financial sentiment analysis, focusing on data preprocessing requirements, computational efficiency, and model optimization. Through rigorous experimentation, we evaluate their performance using key metrics, including accuracy, precision, recall, and F1-score. Furthermore, we discuss potential enhancements to improve the effectiveness of these models in financial text analysis, providing insights into their applicability for real-world financial decision-making.

Complexity vs Empirical Score

  • Math Complexity: 2.0/10
  • Empirical Rigor: 5.5/10
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper applies standard pre-trained NLP models (BERT, FinBERT, ULMFiT) to predict stock returns from earnings call transcripts using simple percentage price changes as targets. While the empirical setup and dataset collection are practical, the mathematical depth is minimal and the ML methodology relies on well-established off-the-shelf techniques without significant novelty.

Research Flowchart

  flowchart TD
    A[Research Goal: Leverage Deep Learning for Sentiment Analysis of Earnings Calls] --> B[Input Data: Large-Scale Earnings Call Transcripts]
    B --> C[Data Preprocessing & Text Cleaning]
    C --> D[Model Training & Optimization: BERT, FinBERT, ULMFiT]
    D --> E[Evaluation: Computational Efficiency & Metrics Accuracy, Precision, Recall, F1]
    E --> F[Key Outcome: Optimized Financial Sentiment Analysis for Investment & Risk Decisions]

Cited References (30)

  1. Alpha Vantage API (2024)
  2. S&P U.S. Indices Methodology — link
  3. ChromeDriver - WebDriver for Chrome (2024)
  4. Seeking Alpha (2024)
  5. NLTK: The Natural Language Toolkit (2009) — link
  6. Black Swan or Black Turkey? The State of Economic Knowledge and the Crash of 2007–2009 (2010) — link
  7. Efficient Capital Markets: A Review of Theory and Empirical Work (1970) — link
  8. An algorithm for suffix stripping (1980) — link
and 22 more
  1. Predicting Stock Market Using Natural Language Processing (2023)
  2. Stock Price Prediction using Sentiment Analysis and Deep Learning for Indian Markets (2022)
  3. Sentiment Analysis of Stocks Based on News Headlines Using NLP (2023)
  4. Tweet Sentiment Analysis to Predict Stock Market (2023) — link
  5. From Transcripts to Insights: Uncovering Corporate Risks Using Generative AI (2023)
  6. Stock Market Prediction Using Sentiment Analysis (2023)
  7. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019)
  8. BERT Repository (2020)
  9. Base BERT Implementation Source Code (2020)
  10. RoBERTa: A Robustly Optimized BERT Pretraining Approach (2019)
  11. Big Data: Deep Learning for Financial Sentiment Analysis (2018)
  12. FinBERT: Financial Sentiment Analysis with Pre-trained Language Models (2019)
  13. Universal Language Model Fine-tuning for Text Classification (2018)
  14. FinBERT: A Large Language Model for Extracting Information from Financial Text (2022)
  15. Machine Learning Text Classification & Language Modelling Using Fast.ai (2022) — link
  16. Universal Language Model Fine-tuning for Text Classification (2018) — link
  17. Longformer: The Long-Document Transformer (2020) — link
  18. Longformer Repository (2021)
  19. BERT and Longformer Sentiment Analysis for Stock Transcripts (2024)
  20. Finbert Sentiment Analysis for Stock Transcripts (2024)
  21. ULMFiT Sentiment Analysis for Earnings Call Transcripts (2024)
  22. dl-7643-finalproject-sentinel-stocks (2024)

0 of 30 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.