Advanced Deep Learning Techniques for Analyzing Earnings Call Transcripts: Methodologies and Applications

• URL: http://arxiv.org/abs/2503.01886v1
• Date: Thu, 27 Feb 2025 00:28:43 GMT
• Title: Advanced Deep Learning Techniques for Analyzing Earnings Call Transcripts: Methodologies and Applications
• Authors: Umair Zakir, Evan Daykin, Amssatou Diagne, Jacob Faile,
• Abstract summary: The objective is to investigate how Natural Language Processing can be leveraged to extract sentiment from large-scale financial transcripts.<n>We examine the strengths and limitations of each model in the context of financial sentiment analysis.<n>Through rigorous experimentation, we evaluate their performance using key metrics, including accuracy, precision, recall, and F1-score.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Language Modeling for the Future of Finance: A Quantitative Survey into Metrics, Tasks, and Data Opportunities [4.974815773537217]
  Recent advances in language modeling have led to growing interest in applying Natural Language Processing techniques to financial problems. To examine this trend, we review 374 NLP research papers published between 2017 and 2024 across 38 conferences and workshops. We evaluate these papers across 11 qualitative and quantitative dimensions, identifying key trends such as the increasing use of general-purpose language models.
  arXiv  Detail & Related papers  (2025-04-09T21:02:12Z)
• Demystifying Domain-adaptive Post-training for Financial LLMs [79.581577578952]
  FINDAP is a systematic and fine-grained investigation into domain adaptive post-training of large language models (LLMs)<n>Our approach consists of four key components: FinCap, FinRec, FinTrain and FinEval.<n>The resulting model, Llama-Fin, achieves state-of-the-art performance across a wide range of financial tasks.
  arXiv  Detail & Related papers  (2025-01-09T04:26:15Z)
• EVOLvE: Evaluating and Optimizing LLMs For Exploration [76.66831821738927]
  Large language models (LLMs) remain under-studied in scenarios requiring optimal decision-making under uncertainty. We measure LLMs' (in)ability to make optimal decisions in bandits, a state-less reinforcement learning setting relevant to many applications. Motivated by the existence of optimal exploration algorithms, we propose efficient ways to integrate this algorithmic knowledge into LLMs.
  arXiv  Detail & Related papers  (2024-10-08T17:54:03Z)
• A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challenges [60.546677053091685]
  Large language models (LLMs) have unlocked novel opportunities for machine learning applications in the financial domain. We explore the application of LLMs on various financial tasks, focusing on their potential to transform traditional practices and drive innovation. We highlight this survey for categorizing the existing literature into key application areas, including linguistic tasks, sentiment analysis, financial time series, financial reasoning, agent-based modeling, and other applications.
  arXiv  Detail & Related papers  (2024-06-15T16:11:35Z)
• AlphaFin: Benchmarking Financial Analysis with Retrieval-Augmented Stock-Chain Framework [48.3060010653088]
  We release AlphaFin datasets, combining traditional research datasets, real-time financial data, and handwritten chain-of-thought (CoT) data. We then use AlphaFin datasets to benchmark a state-of-the-art method, called Stock-Chain, for effectively tackling the financial analysis task.
  arXiv  Detail & Related papers  (2024-03-19T09:45:33Z)
• Towards reducing hallucination in extracting information from financial reports using Large Language Models [1.2289361708127877]
  We show how Large Language Models (LLMs) can efficiently and rapidly extract information from earnings report transcripts. We evaluate the outcomes of various LLMs with and without using our proposed approach based on various objective metrics for evaluating Q&A systems.
  arXiv  Detail & Related papers  (2023-10-16T18:45:38Z)
• Instruction Tuning for Large Language Models: A Survey [52.86322823501338]
  We make a systematic review of the literature, including the general methodology of supervised fine-tuning (SFT)<n>We also review the potential pitfalls of SFT along with criticism against it, along with efforts pointing out current deficiencies of existing strategies.
  arXiv  Detail & Related papers  (2023-08-21T15:35:16Z)
• Instruct-FinGPT: Financial Sentiment Analysis by Instruction Tuning of General-Purpose Large Language Models [18.212210748797332]
  We introduce a simple yet effective instruction tuning approach to address these issues. In the experiment, our approach outperforms state-of-the-art supervised sentiment analysis models.
  arXiv  Detail & Related papers  (2023-06-22T03:56:38Z)
• PIXIU: A Large Language Model, Instruction Data and Evaluation Benchmark for Finance [63.51545277822702]
  PIXIU is a comprehensive framework including the first financial large language model (LLMs) based on fine-tuning LLaMA with instruction data. We propose FinMA by fine-tuning LLaMA with the constructed dataset to be able to follow instructions for various financial tasks. We conduct a detailed analysis of FinMA and several existing LLMs, uncovering their strengths and weaknesses in handling critical financial tasks.
  arXiv  Detail & Related papers  (2023-06-08T14:20:29Z)
• Beyond Classification: Financial Reasoning in State-of-the-Art Language Models [0.0]
  Large Language Models (LLMs) have demonstrated remarkable ability in complex multi-step reasoning tasks. This research presents a comprehensive investigation into the potential application of LLMs in the financial domain. The ability to generate coherent financial reasoning first emerges at 6B parameters, and continues to improve with better instruction-tuning or larger datasets.
  arXiv  Detail & Related papers  (2023-04-30T04:36:05Z)
• Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models [51.3422222472898]
  We document the capability of large language models (LLMs) like ChatGPT to predict stock price movements using news headlines. We develop a theoretical model incorporating information capacity constraints, underreaction, limits-to-arbitrage, and LLMs.
  arXiv  Detail & Related papers  (2023-04-15T19:22:37Z)
• A Comprehensive Review on Summarizing Financial News Using Deep Learning [8.401473551081747]
  Natural Language Processing techniques are typically used to deal with such a large amount of data and get valuable information out of it. In this research, embedding techniques used are BoW, TF-IDF, Word2Vec, BERT, GloVe, and FastText, and then fed to deep learning models such as RNN and LSTM. It was expected that Deep Leaming would be applied to get the desired results or achieve better accuracy than the state-of-the-art.
  arXiv  Detail & Related papers  (2021-09-21T12:00:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.