Prompt Engineering 🤖 To Predict Financial Markets 💰

Article: https://arxiv.org/pdf/2304.07619

 What is ChatGPT?:

ChatGPT is an advanced language model developed by OpenAI that uses the GPT architecture for natural language processing tasks. It has been trained on a vast amount of text data to understand the structure and patterns of language. The GPT architecture is based on the Transformer model and has achieved state-of-the-art performance in various language tasks such as translation, summarization, question answering, and text completion.

The GPT architecture utilizes a multi-layer neural network and self-attention mechanisms to handle long sequences of text effectively. It is pre-trained on large text corpora using unsupervised learning, allowing it to learn language syntax and semantics. The model is then fine-tuned for specific language tasks. One of its notable features is the transformer block, which enables it to focus on relevant parts of the input through self-attention, leading to more accurate and coherent responses.

While ChatGPT excels in language-based tasks and can generate human-like text, it is important to note that it is not specifically trained for predicting stock returns or providing financial advice. Its capabilities in predicting stock returns would need to be tested separately, as its training primarily focuses on language understanding and generation.

 What Is Prompting?:

Prompts are short pieces of text that provide context and instructions for ChatGPT to generate a response. They can be simple or complex, depending on the task. The prompt serves as a starting point for ChatGPT's response generation process, where it analyzes the prompt's syntax and semantics to generate relevant and appropriate responses.

Prompts are crucial for enabling ChatGPT to perform various language tasks, including financial analysis. In this specific prompt, ChatGPT is asked to assume the role of a financial expert and evaluate the impact of a news headline on a company's stock price in the short term. The prompt instructs ChatGPT to answer "YES" if the news is good for the stock price, "NO" if it is bad, or "UNKNOWN" if uncertain. Additionally, ChatGPT is asked to provide a concise explanation in one sentence to support its answer. The prompt is designed to demonstrate ChatGPT's capabilities in financial analysis tasks.

An example prompt is provided, where ChatGPT is asked to evaluate a news headline about Oracle. Based on the prompt, ChatGPT generates a response indicating that the news is positive for Oracle. It provides reasoning that the fine against Rimini Street could potentially boost investor confidence in Oracle's ability to protect its intellectual property and increase demand for its products and services.

The example highlights the importance of considering context and carefully analyzing news headlines in financial decision-making, as the sentiment of the headline may differ from the model's interpretation.

Data Source: 

The Center for Research in Security Prices (CRSP) daily returns and news headlines from a leading data vendor. The sample period begins in October 2021 (as ChatGPT’s training data is available only until September 2021) and ends in December 2022. This sample period ensures that our evaluation is based on information not present in the model’s training data, allowing for a more accurate assessment of its predictive capabilities.

Recommendation: This is what they used as they have access to academic resources to do so. Find another data provider like Yahoo Finance to substitute for CRSP.

Here is the proposed prompt from the article:

Their Results:

The analysis demonstrates that sentiment scores generated by ChatGPT have statistically significant predictive power on daily stock market returns. The correlation between ChatGPT evaluation and subsequent daily returns of the stocks in the sample indicates the potential of ChatGPT as a tool for predicting stock market movements based on sentiment analysis. Comparing ChatGPT with traditional sentiment analysis methods, the study finds that when controlling for ChatGPT sentiment scores, the effect of other sentiment measures on stock market returns diminishes. This highlights ChatGPT's superiority in forecasting stock market returns compared to existing sentiment analysis methods. The advanced language understanding capabilities of ChatGPT contribute to its superior performance in predicting stock market returns. By capturing nuances and subtleties within news headlines, ChatGPT generates more reliable sentiment scores, leading to more accurate predictions. The results confirm the predictive power of ChatGPT sentiment scores and emphasize the potential benefits of incorporating large language models (LLMs) like ChatGPT into investment decision-making processes. ChatGPT outperforms traditional methods, enhancing quantitative trading strategies and providing a better understanding of market dynamics. The regression analysis, presented in Table 3, showcases the relationship between next-day stock returns and sentiment scores from ChatGPT and alternative sentiment analysis methods. The models include firm and date-fixed effects to account for unobserved firm characteristics and time-specific factors. The results highlight the predictability of small stocks, suggesting limitations to arbitrage that may impact the implementation and profitability of this strategy.