An efficient big data framework for validating the random walk hypothesis in high-frequency markets via neural networks and large language models

Key Points

• Neural networks demonstrate strong potential to validate the random walk hypothesis in trading tactics.
• Findings reveal improved accuracy in model predictions when utilizing large language models for data interpretation.
• Analysis of high-frequency market data shows significant insights into trading behavior and randomness.
• The study supports the need for advanced modeling techniques, indicating a shift in market analysis strategies.