arXiv:2605.23953v1 Announce Type: cross Abstract: Accurate stock price forecasting has consistently remained a pivotal yet challenging FinTech task that underpins quantitative trading and investment decision making. Recent efforts have been dedicated to modeling various complex relationships among stocks in the stock market toward more reliable stock price forecasting.These methods depend heavily on strong static prior assumptions by modeling either temporal dependencies within individual stocks or spatial dependencies across different stocks based on predefined structures, while the complex m
The paper leverages recent advancements in AI, particularly game theory integration, to address a long-standing challenge in financial markets amid intense competition in FinTech.
Sophisticated stock price forecasting models have direct implications for quantitative trading, investment strategies, and the stability of financial markets, affecting a wide range of institutional investors.
The ability to model heterogeneous investor interactions more accurately using game theory could lead to more robust and less assumption-dependent stock prediction systems, potentially disrupting existing algorithmic trading approaches.
- · Quantitative Trading Firms
- · FinTech companies
- · AI/ML researchers in finance
- · Hedge Funds
- · Traditional algorithmic trading relying on static models
- · Retail investors without advanced tools
More accurate stock predictions lead to increased efficiency and competition in financial markets.
Widespread adoption of such models could lead to new forms of market manipulation or flash crashes if not properly regulated and understood.
The complexity of these models might obscure underlying economic realities, concentrating market power among those who can develop and deploy the most advanced AI.
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Read at arXiv cs.LG