Assessment of Earnings Releases with a Large Language Model for Enhanced Returns with Five American Blue-Chip Stocks

Table of Contents

1 INTRODUCTION

1.1 Introduction to the Topic

1.2 Delimitation of the Thesis

2 EARNINGS TRADING STRATEGIES

2.1 Overview of Earnings Trading

2.1.1 Pre-Earnings Strategies

2.1.2 Post-Earnings Strategies

2.1.3 Conclusion about Earnings Trading

3 AMERICAN BLUE-CHIP STOCKS

3.1 Introduction to Blue-Chip Stocks

3.2 Benefits of American Blue-Chip Stocks

4 BASICS OF MACHINE LEARNING AND LARGE LANGUAGE MODELS

4.1 Fundamentals of Artificial Intelligence

4.1.1 Artificial Intelligence

4.1.2 Machine Learning

4.1.3 Deep Learning

4.1.4 Large Language Models

4.1.5 Importance of these Terms

4.2 Fundamentals of Machine Learning and LLMs

4.2.1 Supervised Learning

4.2.2 Unsupervised Learning

4.2.3 Reinforcement Learning

4.2.4 Neural Networks

4.3 Core Concepts of LLMs

5 EVALUATING THREE LLMS FOR SUITABILITY FOR EARNINGS TRADING

5.1 Overview of different LLMs

5.1.1 Bidirectional Encoder Representations

5.1.2 A Robustly Optimized BERT Approach

5.1.3 Generative Pre-trained Transformer

5.2 Evaluation of the three LLMs

6 ASSESSING EARNINGS RELEASES WITH THE CHOSEN LARGE LANGUAGE MODEL

6.1 Methodology to use an LLM to analyze Earnings Releases

6.1.1 Selection of five American Blue-Chip Stocks

6.1.2 Wording a Prompt to analyze Earnings Releases

6.1.3 Analyzing the Earnings Reports

6.1.4 Analyzing the Output of ChatGPT

7 ANALYSIS OF CHATGPT'S SENTIMENT RESPONSES

7.1 Assessment of ChatGPT's Sentiment Results

7.1.1 Amazon.com

7.1.2 The Coca-Cola Company

7.1.3 Walmart

7.1.4 Goldman Sachs

7.1.5 The Walt Disney Company

7.1.6 Overview of all stocks

7.2 Comparing the Sentiment Results with the Stock Movement post-announcement

7.3 Critical Evaluation of the Results

8 FUTURE RESEARCH

9 CONCLUSION ABOUT THE LLM-BASED EARNINGS TRADING EXPERIMENT

Objectives and Thematic Focus

This thesis explores the integration of Large Language Models (LLMs) into financial trading strategies, specifically by evaluating whether ChatGPT can analyze earnings reports to predict stock movements and provide actionable investment recommendations.

• The role of earnings releases in financial markets.
• Fundamental principles of Machine Learning and LLM architectures.
• Comparative evaluation of BERT, RoBERTa, and GPT models for financial sentiment analysis.
• Implementation of a prompt-based experiment using American blue-chip stocks.
• Quantitative assessment of sentiment accuracy compared to actual stock returns.

Excerpt from the Book

Summary of Chapters

1 INTRODUCTION: This chapter contextualizes the importance of trading technology in modern finance and presents the core research question regarding LLM-based earnings trading.

2 EARNINGS TRADING STRATEGIES: Provides an overview of earnings seasons and existing pre- and post-earnings trading strategies used to capitalize on market volatility.

3 AMERICAN BLUE-CHIP STOCKS: Defines blue-chip stocks and explains their stability and historical data availability as the foundation for the experimental setup.

4 BASICS OF MACHINE LEARNING AND LARGE LANGUAGE MODELS: Covers the theoretical background of AI, Machine Learning, and the architectural concepts of LLMs.

5 EVALUATING THREE LLMS FOR SUITABILITY FOR EARNINGS TRADING: Compares BERT, RoBERTa, and GPT, ultimately selecting ChatGPT as the most suitable model for the experiment.

6 ASSESSING EARNINGS RELEASES WITH THE CHOSEN LARGE LANGUAGE MODEL: Details the methodology for stock selection, prompt engineering, and the execution of the sentiment analysis experiment.

7 ANALYSIS OF CHATGPT'S SENTIMENT RESPONSES: Analyzes the empirical results, comparing ChatGPT's predictions against actual stock movements for five specific companies.

8 FUTURE RESEARCH: Suggests potential improvements for further studies, including broader data integration and advanced AI fine-tuning.

9 CONCLUSION ABOUT THE LLM-BASED EARNINGS TRADING EXPERIMENT: Summarizes the thesis findings, acknowledging both the potential and limitations of using LLMs for stock market predictions.

Keywords

Earnings releases, Large Language Models, ChatGPT, Sentiment analysis, Financial trading, Blue-chip stocks, Machine learning, Prompt engineering, Investment strategies, Market volatility, Artificial intelligence, Stock market predictions.

Frequently Asked Questions

What is the core focus of this bachelor thesis?

The thesis investigates the capability of Large Language Models, specifically ChatGPT, to analyze corporate earnings releases and generate sentiment-based trading signals to potentially enhance investment returns.

Which specific financial instruments are analyzed?

The study focuses on five prominent American blue-chip stocks: Amazon, Coca-Cola, Walmart, Goldman Sachs, and The Walt Disney Company.

What is the primary goal of the research?

The primary goal is to determine if an LLM-based approach to sentiment analysis can accurately predict stock price movements following earnings announcements.

Which scientific methodology is applied in the study?

The research employs a quantitative experimental approach, testing various LLM architectures for suitability before executing a prompt-based sentiment analysis on historical earnings reports and comparing outcomes with actual market performance.

What topics are covered in the main section of the work?

The main sections cover the theory of earnings trading, the fundamentals of AI and neural networks, a comparative evaluation of LLMs, and a detailed performance assessment of sentiment accuracy across different timeframes.

Which keywords best characterize this research?

Key terms include Large Language Models, Financial Sentiment Analysis, Earnings Releases, Algorithmic Trading, and Blue-chip stocks.

How were the prompts for the AI developed?

The author utilized prompt engineering to simulate an impartial financial advisor, iteratively refining the instructions to ensure the model output provided a consistent 'Positive', 'Negative', or 'Neutral' sentiment classification.

What were the final conclusions regarding ChatGPT's performance?

The study found that while ChatGPT demonstrates some predictive ability, its performance varies across different stocks and timeframes, highlighting the need for critical evaluation and the inclusion of human oversight in real-world trading scenarios.