FinCast: A New Paradigm for Stock Prediction Based on Multi-Round Reasoning

FinCast reframes stock prediction from the traditional single-round regression task into a multi-round LLM reasoning process. It makes decisions by reading news, comparing historical patterns, and integrating multi-source signals, aiming to address limitations of traditional methods such as information compression loss, black-box nature, and staticity. This represents a new direction for financial AI to shift from a "prediction machine" to a "research assistant".

Traditional machine learning methods treat stock prediction as an end-to-end regression/classification task, with four major limitations:

1. Information Compression Loss: Compressing rich information like news and financial reports into fixed-dimensional features, losing semantic details;
2. Lack of Interpretability: The black-box nature of deep learning conflicts with the financial decision-making need for "why";
3. Static Pattern Recognition: Difficult to adapt to dynamic market changes and unexpected events;
4. Single Decision Perspective: Ignores the multi-level, multi-step decision-making process in investment practice.

FinCast simulates the workflow of human analysts, breaking it down into four reasoning steps:

1. Information Collection and Reading: LLM understands the full semantics, causal relationships, credibility, and impact of news on stocks;
2. Historical Pattern Comparison: Retrieves similar historical scenarios, analyzes similarities/differences and stock price reaction patterns;
3. Multi-Signal Integration: Dynamically judges the relative importance of signals such as technical indicators, fundamentals, and sentiment;
4. Decision and Reasoning: Generates predictions (up/down probability, target range) + complete reasoning chain (arguments, uncertainties, assumptions).

Core differences from traditional methods:

Dimension	Traditional Methods	FinCast
Information Processing	Feature engineering compression	Native text understanding
Reasoning Process	Implicit black-box	Explicit multi-round reasoning
Interpretability	Low (post-hoc explanation)	High (natural language reasoning chain)
Adaptability	Requires retraining	Prompt engineering adjustment
Knowledge Integration	Limited to training data	Leverages LLM pre-trained knowledge

Inferred core technologies from project descriptions:

1. Retrieval-Augmented Generation (RAG): Builds a historical event database, uses semantic retrieval to get similar cases as context;
2. Tool Usage: Calls market APIs, news data sources, financial interfaces to obtain real-time data;
3. Chain of Thought (CoT): Explicit prompts for step-by-step reasoning, multi-agent division of labor or reflection mechanisms to correct reasoning;
4. Memory and Context Management: Maintains long-term memory, tracks prediction results and reflections, accumulates deep industry/company understanding.

Advantages:

1. Close to human analyst thinking (iterative reflection);
2. Dynamic adaptability (prompt adjustment can handle market changes);
3. Rich interactivity (users can ask about reasoning details);
4. Easy error diagnosis (locate issues via reasoning chain).

Challenges:

1. Hallucination risk (high cost of generating incorrect information);
2. Latency and cost (multi-round reasoning is slow and expensive, not suitable for high-frequency scenarios);
3. Evaluation complexity (need to balance prediction accuracy and reasoning rationality);
4. Overfitting to narratives (tendency to generate "nice-sounding" stories instead of accurate predictions).

Suitable Scenarios:

• Mid-to-long term investment decisions (fundamentals/event-driven/industry rotation);
• Research and education (analyst auxiliary tool, financial case teaching, strategy backtesting).

Unsuitable Scenarios:

• High-frequency trading (too high latency);
• Pure technical analysis strategies (LLM's advantages are not obvious);
• Latency-sensitive scenarios.

Industry Significance:

• Promotes financial AI from "prediction machine" to "decision assistant", enhancing human decision-making rather than replacing it;
• Explicit reasoning chain meets regulatory and investor requirements for interpretability.

Future Directions:

• Integrate quantitative models (GARCH, LSTM) for time series processing;
• Integrate knowledge graphs to provide structured financial knowledge;
• Multi-agent collaboration to simulate research team division.

FinCast outlines the possibility of introducing LLM reasoning capabilities into financial prediction. The core insight is: stock prediction should be multi-round reasoning rather than single-round regression. In complex financial markets, we may need AI systems that can read, think, compare, reflect, and explain judgments more than more complex regression models. Its actual effect remains to be verified, but it provides important ideas for financial AI research.