AI-Driven Failed Trials in Investment Strategies: A Network Data Analysis Approach

Abstract

In recent years, the intersection of Artificial Intelligence (AI) and quantitative finance has sparked significant interest for formulating and guiding investment strategies. In contrast to the leading discourse focusing on AI success case studies, this paper addresses particularly "failed trials" driven by AI implementations for investment strategies and on the strategic use of AI to simulate and learn from such failures. Understanding the underlying factors that lead to under-performing AI-powered solutions for investment and the parameters used in AI simulations of failed trials is instrumental to guide future developments towards designing more resilient AI systems for investment. In this context, we introduce network data analysis as a powerful tool to enhance these models by capturing complex interdependencies and systemic risks within financial markets. Our study also addresses the broader implications of explainable AI and policy frameworks for AI-powered investment, emphasizing the need for transparency in finance AI-driven decision-making. Together, this paper proposes integrating advanced AI methodologies with network data analysis, while emphasizing explainability and policy orientation, therefore contributing holistically to both the academic discourse and practical applications of these technologies in risk management and investment optimization.

Keywords: AI, failed trials, quantitative finance, investment strategies, network data analysis, transparency