Application and Challenge of Artificial Intelligence in Stock Investment

Rujing Guo

doi:10.62177/apemr.v2i2.288

Authors

Rujing Guo School of Xingzhi, Xi'an University of Finance and Economics

DOI:

https://doi.org/10.62177/apemr.v2i2.288

Keywords:

Artificial Intelligence, Stock Investing, Market Volatility

Abstract

This paper examines the application and challenges of AI in stock investing. AI is transforming stock investing through data mining, predictive modeling, and trading decisions. It processes multi-source data, captures real-time market sentiment, and identifies investment opportunities using deep learning models. The widespread use of automated trading systems and intelligent advisors has enhanced trading efficiency and returns. However, AI also faces challenges such as algorithmic "black boxes," model failures, and system issues.Through case studies, this paper analyzes the practical impact of AI in data mining, predictive modeling, and automated trading, and discusses the constraints of technology application. It proposes a technology optimization path combining data enhancement and cross-validation, and designs an auditable, transparent decision-making mechanism. Additionally, the paper explores the potential of quantum computing and blockchain in finance, offering theoretical insights and practical guidance for the industry to navigate the opportunities and challenges of intelligent investing.

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