Agentic data science with the Data Agent Kit
cloud.google.com·7h ago
AI Research
arXiv CS.AI·Sep 7·5 min read
Artificial Intelligence in Equity and Crypto Markets: Progress, Profitability Evidence, and the Limits of Automated Investing
TL;DR
Artificial intelligence (AI) is increasingly integrated into investment processes across equity and crypto markets, yet evidence of consistent profitability remains limited. This review highlights the challenges in translating AI advancements into reliable financial returns.
✦ Why It Matters
Evaluate your AI investment strategies against the outlined challenges to improve profitability evidence.
Key Takeaways
- Assess AI tools for specific investment workflows.
- Implement rigorous testing to validate profitability claims.
- Monitor for temporal contamination in data.
- Consider separate strategies for different crypto market types.
How It Works
The review utilizes an alpha-translation chain framework, which requires that point-in-time data leads to stable signals, feasible positions, and risk-adjusted returns. This structured approach helps in evaluating the effectiveness of AI models in generating excess returns.
⚠ The Catch
Despite advancements, the review identifies specific limitations such as predictor decay and the lack of robust, audited live-capital records, which undermine the reliability of AI-driven investment strategies.