Evaluating AI’s Impact on Asset Pricing and Derivatives: A Cross-Country Analysis

Abstract

This study explores the effect of AI-based techniques on asset pricing and derivatives through a cross-country analysis of monetary markets in the US, the Unified Realm, Canada, and Australia. Using fictitious data and SmartPLS for analysis, the examination inspects how AI approaches, including AI, profound learning, and support learning, influence gauging accuracy and asset valuation contrasted with customary monetary models. The discoveries propose that nations with higher reception paces of AI techniques experience upgraded prescient execution and more exact pricing of assets and derivatives. In particular, profound learning models beat customary strategies in determining stock market patterns. In spite of these bits of knowledge, the review recognizes impediments like the dependence on fictitious data, expected challenges in model interpretability, and fluctuating levels of AI reception across various districts. The examination features the requirement for future investigations to investigate a more extensive scope of geologies, consolidate certifiable data, and address functional execution challenges.