Fine-Tuning Large Language Models for Stock Return Prediction Using Newsflow

This paper delves into the realm of quantitative investing by exploring the use of large language models (LLMs) and their fine-tuning techniques for stock return forecasting with financial newsflow. The study emphasizes the importance of return forecasting in quantitative portfolio construction, highlighting its significance for tasks such as stock picking and portfolio optimization. Unlike traditional approaches that involve feature extraction and validation, this research directly incorporates LLMs to model the relationship between text representations and future stock returns. The paper specifically investigates the impact of different LLM design choices on return forecasting, focusing on encoder-only versus decoder-only LLMs, as well as bottleneck versus aggregated representations. Through experiments conducted on real financial news data across various investment universes and portfolios, several key findings emerge. Firstly, aggregated representations from LLMs' token-level embeddings generally enhance the performance of long-only and long-short portfolios. Secondly, in larger investment universes, decoder LLMs tend to lead to stronger portfolios compared to encoder-only models; however, performance variations are observed in smaller universes. Among the three LLMs studied (DeBERTa, Mistral, Llama), Mistral demonstrates more robust performance across different investment universes. Additionally, the research shows that return predictions derived from LLMs' text representations serve as a strong signal for portfolio construction, outperforming conventional sentiment scores. The paper also highlights several open questions for future research. For instance <fd>, it raises queries about the underperformance of encoder-only DeBERTa in large investment universes and explores reasons behind varying performance of DeBERTa in different small universes</fd>. The study suggests evaluating recently proposed large encoder-only LLMs as a potential follow-up research direction. Furthermore <fd>, within the decoder-only LLM family</fd>, further exploration is needed to understand why there are performance variations among models like Mistral and Llama across different investment universes. In conclusion, this paper contributes valuable insights into leveraging advanced language models for stock return prediction using financial newsflow data. It underscores the potential of utilizing LLMs' text representations as a powerful tool for enhancing quantitative portfolio construction strategies and signals a promising avenue for future research in this domain.

Introduction

Quantitative investing has gained significant traction in recent years, with the rise of advanced technologies and data-driven approaches. One key aspect of this field is return forecasting, which plays a crucial role in tasks such as stock picking and portfolio optimization. Traditional methods for return forecasting involve feature extraction and validation, which can be time-consuming and may not always capture the full complexity of financial markets.

In this research paper, titled "Large Language Models for Stock Return Forecasting with Financial Newsflow," the authors explore the use of large language models (LLMs) for return forecasting using financial news data. LLMs are powerful natural language processing (NLP) models that have shown impressive performance on various text-based tasks. The study specifically investigates the impact of different LLM design choices on return forecasting, shedding light on their potential applications in quantitative portfolio construction.

The Importance of Return Forecasting in Quantitative Investing

Return forecasting is a critical component of quantitative investing strategies. It involves predicting future stock returns based on historical data and market trends. This information is then used to make informed decisions about which stocks to buy or sell and how to optimize portfolio allocations.

The paper highlights that accurate return forecasts can significantly enhance investment performance by providing valuable insights into market movements. Moreover, it can help investors identify undervalued assets or avoid overpriced ones, leading to better risk-adjusted returns.

Integrating Large Language Models for Return Forecasting

Unlike traditional approaches that rely on feature extraction and validation techniques, this research directly incorporates LLMs into the process of modeling the relationship between text representations and future stock returns. The authors argue that this approach has several advantages over conventional methods:

• Efficiency: By leveraging pre-trained LLMs instead of manually extracting features from raw text data, this method reduces computational costs.
• Flexibility: LLMs can be fine-tuned for specific tasks, making them adaptable to different investment universes and portfolios.
• Robustness: LLMs have shown impressive performance on various NLP tasks, indicating their potential to capture the complexity of financial markets.

The Impact of Different LLM Design Choices on Return Forecasting

The paper investigates three key design choices for LLMs: encoder-only versus decoder-only models, bottleneck versus aggregated representations, and the choice of specific LLM architectures (DeBERTa, Mistral, and Llama).

Through experiments conducted on real financial news data across various investment universes and portfolios, several key findings emerge:

• In general, aggregated representations from LLMs' token-level embeddings enhance the performance of long-only and long-short portfolios.
• In larger investment universes, decoder-only models tend to lead to stronger portfolios compared to encoder-only models. However, performance variations are observed in smaller universes.
• Mistral demonstrates more robust performance across different investment universes compared to DeBERTa and Llama.

The Potential of Using Text Representations as a Signal for Portfolio Construction

The study also compares return predictions derived from LLMs' text representations with conventional sentiment scores commonly used in quantitative investing strategies. The results show that text representations serve as a strong signal for portfolio construction and outperform sentiment scores.

This finding highlights the potential of utilizing advanced language models for enhancing quantitative portfolio construction strategies. It also suggests that incorporating textual information into return forecasting can provide valuable insights not captured by traditional methods.

Open Questions for Future Research

While this research provides valuable insights into leveraging large language models for stock return prediction using financial newsflow data, it also raises several open questions for future research. For instance, the paper explores the underperformance of encoder-only DeBERTa in large investment universes and suggests evaluating recently proposed large encoder-only LLMs as a potential follow-up research direction.

Furthermore, within the decoder-only LLM family, further exploration is needed to understand why there are performance variations among models like Mistral and Llama across different investment universes.

Conclusion

In conclusion, this paper contributes valuable insights into utilizing advanced language models for stock return forecasting with financial newsflow data. It highlights the potential of incorporating LLMs' text representations as a powerful tool for enhancing quantitative portfolio construction strategies. The study also identifies promising avenues for future research in this domain, emphasizing the need to explore different design choices and evaluate newer LLM architectures. Overall, this research sheds light on the growing role of NLP techniques in quantitative investing and underscores their potential to drive better investment decisions.