Abstract: Large Language Models (LLMs) have emerged as powerful tools in various applications, including financial markets analysis. They can outperform traditional methods in some aspects while facing limitations in others. Let's delve into two specific financial market applications to understand how LLMs can excel and fall short.
**1. Predicting Stock Returns using LLMs vs. Convolutional Neural Networks (CNNs)**
LLMs excel in analyzing and measuring language similarity. When applied to SEC Filings for companies listed on the S&P 500, LLMs can extract valuable insights from the textual data. They can identify nuanced sentiment, detect changes in tone or language that may signal financial trends, and provide contextual information about events or news that could impact stock performance. LLMs are adept at processing unstructured data and extracting relevant features from it, making them suitable for predicting stock returns.
On the other hand, CNNs, which are typically used for image and sequence data, can also add value by structuring the textual nature of SEC Filings. They might require extensive preprocessing and feature engineering to perform adequately. CNNs can outperform LLMs in terms of accuracy and efficiency when measuring language similarity in financial documents. However, they lack aspects in regards to topic extraction for additional insights.
**2. Removing Noisy Categories in Reddit Bitcoin Posts with ROBERTA and CHATGPT Sample Augmentation**
In this application, the goal is to improve the quality of data used for sentiment analysis or market sentiment tracking by eliminating noisy categories from Reddit Bitcoin posts. LLMs, such as ROBERTA, excel at text classification tasks. They can accurately classify Reddit posts into relevant and irrelevant categories, helping to filter out irrelevant or misleading information.
However, LLMs might underperform when it comes to understanding the nuanced context within these posts. They can classify posts based on surface-level keywords but may struggle with sarcasm, irony, or subtle language nuances. Additionally, LLMs might require substantial labeled data for training, which can be challenging to obtain in finance-related applications.
To address these limitations, sample augmentation with CHATGPT can be beneficial. CHATGPT can generate additional training data to supplement ROBERTA's classification abilities. By combining the strengths of LLMs for classification and natural language generation, this approach can effectively enhance the performance of the model in filtering noisy Reddit Bitcoin posts.
In conclusion, LLMs offer significant advantages in financial market applications, particularly in analyzing text data for sentiment analysis and language similarity measurement. However, they may require careful consideration of their limitations and potential synergies with other models or techniques to address specific challenges in the financial domain effectively.
Bio: Toby J. Wade is an accomplished leader with extensive experience at the convergence of financial markets and Machine Learning in roles across hedge funds, financial exchanges, and banking institutions. With a robust foundation in Artificial Intelligence (AI), Machine Learning (ML), and Natural Language Processing (NLP), Toby has overseen impactful initiatives related to alpha signal generation, portfolio construction, risk management, and trading. His expertise spans the development of advanced AI models, notably within the domain of NLP, and the adept application of ML to address intricate challenges within financial markets. Toby's academic journey includes coursework in econometrics at Oxford University, and he is currently in the final stages of a part-time PhD program in Statistics at LSE. His ongoing research explores the contrasting dynamics of tradition and transformative technologies, employing generative AI and Deep Learning NLP techniques for applications in the financial markets.
