How AI Conquers the Stock Market

The era of the 'Black Box' is over. We are now entering the era of the 'Expert Architect'.

Artificial intelligence (AI) has emerged as the key driving force behind financial market decision-making, but vague prompts like "find me profitable stocks" are simply destined to fail in the market. The success or failure of AI investment systems that generate true alpha depends not on the size of the model, but on how precisely human expert wisdom is embedded into the AI architecture.

1. Specificity Determines Success or Failure

The Magic of 'Fine-grained Tasks'

A recent study targeting Japan's TOPIX 100 index clearly demonstrates the impact of instruction precision on returns when directing LLMs (Large Language Models).

When given abstract (coarse-grained) instructions like "analyze the financial statements," AI exhibits phenomena of missing signals or abandoning interpretation during the reasoning process. In contrast, when provided with 'specific (fine-grained)' instructions that break down analytical steps to mimic expert workflows, system performance improved dramatically.

Notably, as portfolio size (N) expanded from 10 to 50 stocks, the performance gap widened even further between systems with specific designs and those without. This implies that in complex multi-asset management environments, sophisticated task design maximizes 'Signal Propagation' efficiency.

"Providing coarse-grained instructions to LLMs for complex tasks presents two major challenges... performance degradation... and the lack of interpretability."

2. Knowledge Quality Equals Returns

The Critical Relationship Between RAG and Data Precision

According to a study analyzing the top 2,500 large-cap stocks in the U.S. market, the accuracy of domain knowledge provided to AI through Retrieval-Augmented Generation (RAG) systems is the most powerful variable determining the Sharpe ratio. The principle of 'Garbage In, Garbage Out' applies mercilessly even to cutting-edge AI systems.

When incorrect documents (Broken KB) were provided in experiments, the Sharpe ratio plummeted to -0.109, destroying asset value. In contrast, when an accurately corrected knowledge base (Fixed KB) was provided and a Long-Short market-neutral strategy was executed, the Sharpe ratio surged to 1.27.

This data shows that no matter how excellent AI's reasoning capabilities are, inaccurate domain knowledge causes 'Alpha Decay'.

3. AI Has Already Become a 'Quant' on Its Own

Independently Discovered Investment Principles

A remarkably surprising fact is that AI, without explicit financial education, independently mastered core principles of modern quantitative finance by learning from tens of thousands of data points. Analysis of AI-generated features revealed the following three dominant patterns:

• Cross-sectional Ranking (100% adoption)

  • AI independently understood that 'relative positioning' rather than absolute stock prices is the essence of returns.
  • It grasped that long-short returns come from the 'spread' between stocks, not from absolute values.

• Volatility Normalization (93% adoption)

  • To respond to market 'regime shifts,' AI independently applied risk management techniques that reduce signal strength when volatility increases.

• Non-linear Interaction (80% adoption)

  • Beyond simply adding indicators, AI demonstrated the ability to read complex market states by multiplying or dividing multiple variables.

These discoveries suggest that AI logically grasps the structural order of markets, and this is not mere coincidence but a statistical inevitability.

4. 1+1 Is Greater Than 2

Multi-Agent Hierarchical Structure and Synergy

To operate like the best quant teams, AI should adopt a hierarchical structure: Analyst → Sector Agent → Portfolio Manager (PM Agent).

In this structure, the role of the Technical Agent was decisive. Technical agents with specific task designs served as the key driver that effectively conveyed subtle price signals to the PM—signals that would be easily overlooked in abstract systems.

The true strength of this system lies in 'Orthogonality'. Low correlation between agents with different perspectives maximizes portfolio diversification effects.

Quant Insight — The combination of technical and fundamental signals performs the 'magic of diversification' by reducing portfolio volatility while stably maintaining the Information Coefficient (IC).

5. AI's Feature Design: 14x More Complex Than Human's

Traditional academic factors consist of simple forms with 2–4 operations, but AI chains an average of 14.2 operations to generate complex features that transcend human intuition.

For example, the 'Overnight Gap' signal designed by AI goes far beyond simply looking at the difference between opening and closing prices. It passes through the following 6-stage pipeline:

1. Gap Calculation — Extract price deviations occurring outside trading hours
2. Vol-Normalization — Normalize according to current volatility regime
3. Trend Comparison — Compare short-term (5-day) and long-term (20-day) trends
4. Outlier Flagging — Detect historical extreme values (Z-score) and remove noise
5. Ranking — Assign relative rankings within sectors
6. Signal Chaining — Combine into final execution signal

Such sophisticated multi-stage pipelines exhibit much lower 'alpha decay' than simple momentum strategies, capturing the market's subtle inefficiencies.

Conclusion: Does AI Replace Humans, or Amplify Them?

The conclusion of these studies is clear. AI is not an automation tool that replaces humans, but a 'Hypothesis-generation Engine' that infinitely expands human hypothesis-generation capabilities.

In actual operational environments, 'Implementation Lag (T+1 to T+2)' occurs between signal generation and execution. However, precisely designed AI systems demonstrated much higher return persistence than traditional approaches even within such time lags.

The question is no longer "whether to use AI." "How to more precisely design and teach expert practical experience and quantitative logic into the AI architecture" will be the decisive battleground that determines true alpha.

References

• Miyazaki, K., Kawahara, T., Roberts, S., & Zohren, S. (2026). Toward Expert Investment Teams: A Multi-Agent LLM System with Fine-Grained Trading Tasks. arXiv:2602.23330
• Rasekhschaffe, K. C. (2026). Generative AI for Stock Selection. arXiv:2602.00196