AI analyzing stock market data

Decoding the Market: Can AI Predict Stock Prices and Explain Why?

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Lena Kashyap in Business & Economy May 2025 • 4 min read.

"Unveiling the potential of self-reflective AI in generating explainable stock market predictions."


For years, the stock market has been a complex puzzle, challenging even the most seasoned investors. Traditional methods often fall short of providing clear, actionable insights, leaving many to wonder if there's a better way to understand market movements. The rise of deep learning offered some solutions, but it also created new problems. While it could do some analysis it remained as black box models that lacked the transparency and explainability needed for practical application.

Enter Large Language Models (LLMs), a new breed of AI known for their ability to generate human-readable explanations. With their language skills, LLMs present a potential solution to this challenge. However, stock prediction is difficult for these types of AI because the varying chaotic impacts of social texts on stock prices are difficult to weigh and explain verbally.

A groundbreaking study from the National University of Singapore and Eastspring Investments introduces a novel framework called Summarize-Explain-Predict (SEP). This innovative approach utilizes a self-reflective AI agent and Proximal Policy Optimization (PPO) to enable LLMs to autonomously learn how to generate explainable stock predictions, promising a new era of transparency and understanding in the financial world.

How Self-Reflective AI Learns to Predict and Explain Stock Movements

AI analyzing stock market data

The SEP framework tackles the challenge of creating explainable stock predictions by dividing the process into three key components:

Here’s how each component contributes to the overall goal:

  • Summarize: This module leverages the summarization capabilities of LLMs to condense vast amounts of text data into concise summaries of factual information.
  • Explain: A self-reflective agent iteratively refines its predictions and explanations through verbal self-reflection, learning from past mistakes to improve future accuracy.
  • Predict: A specialized LLM, fine-tuned using Proximal Policy Optimization (PPO), generates confidence-based predictions and explanations based on the insights gained during the reflective process.
This innovative approach allows the AI to teach itself how to make explainable stock predictions without relying on human experts for annotated data. The self-reflective agent learns to explain past stock movements through a self-reasoning process, while the PPO trainer optimizes the model to generate the most likely explanations given the input texts.

The Future of AI in Stock Market Analysis

The SEP framework represents a significant step forward in the quest for explainable AI in stock market analysis. By combining the power of LLMs with self-reflection and reinforcement learning, this approach offers a promising path towards more transparent and understandable investment insights. As AI continues to evolve, we can expect even more sophisticated models that not only predict stock movements but also provide clear, concise explanations for their decisions, empowering investors to make more informed choices.

Everything You Need To Know

1

What is the Summarize-Explain-Predict (SEP) framework, and why is it important?

The Summarize-Explain-Predict (SEP) framework introduces a novel approach using self-reflective AI to generate explainable stock predictions. This framework is a significant advancement because it bridges the gap between complex algorithms and understandable insights. It utilizes Large Language Models (LLMs) to not only predict stock movements but also provide clear explanations for their decisions. This enhanced transparency empowers investors to make more informed choices, a vast improvement over traditional methods and black box models which lacked transparency.

2

How does the self-reflective agent in the SEP framework function?

The SEP framework's Explain component utilizes a self-reflective agent. This agent iteratively refines its predictions and explanations through verbal self-reflection. The agent learns from past mistakes to improve future accuracy. This self-reasoning process is key, allowing the AI to teach itself how to make explainable stock predictions without human intervention.

3

What are the main components of the Summarize-Explain-Predict (SEP) framework?

The three main components of the SEP framework are Summarize, Explain, and Predict. The Summarize module uses the summarization capabilities of Large Language Models (LLMs) to condense vast amounts of text data into concise summaries. The Explain component employs a self-reflective agent to iteratively refine predictions and explanations. The Predict component uses a specialized LLM, fine-tuned with Proximal Policy Optimization (PPO), to generate confidence-based predictions and explanations.

4

What role does Proximal Policy Optimization (PPO) play in the SEP framework?

Proximal Policy Optimization (PPO) is a reinforcement learning technique used within the SEP framework. Specifically, it is used to fine-tune the LLM in the Predict component. The PPO trainer optimizes the model to generate the most likely explanations based on input texts. This optimization helps the model generate more accurate and reliable predictions, crucial for generating valuable investment insights.

5

How does the SEP framework represent a step forward in stock market analysis?

The significance of the SEP framework lies in its potential to transform stock market analysis by providing explainable AI. The framework combines the power of Large Language Models (LLMs), self-reflection, and reinforcement learning. This approach offers a path toward more transparent and understandable investment insights. As AI continues to evolve, we can expect more sophisticated models that offer clear, concise explanations, empowering investors to make informed decisions. This is a departure from earlier deep learning models that lacked transparency.

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