Leveraging Artificial Intelligence for Cross-border Investment Behavior Analysis: A Data Mining and Pattern Recognition Approach to Predict Market Liquidity and Price Discovery Efficiency

Jiaxuan Li

doi:10.54254/2755-2721/2025.22248

Research Article

Open access

Published on 21 April 2025

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Li,J. (2025). Leveraging Artificial Intelligence for Cross-border Investment Behavior Analysis: A Data Mining and Pattern Recognition Approach to Predict Market Liquidity and Price Discovery Efficiency. Applied and Computational Engineering,150,83-88.

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Leveraging Artificial Intelligence for Cross-border Investment Behavior Analysis: A Data Mining and Pattern Recognition Approach to Predict Market Liquidity and Price Discovery Efficiency

Jiaxuan Li *^,1,

¹ Monash University, Melbourne, Australia

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2755-2721/2025.22248

Abstract

The hybrid algorithm model developed in this study successfully broke the "black box effect" of cross-border capital flows by integrating the three engines of random forest, support vector machine, and LSTM neural network. Based on full-cycle data of the S&P 500, FTSE 100, and Shanghai Composite indices from 2019 to 2024, the model achieved an 85% response rate in warning of volatility in Southeast Asian emerging markets, 37 percentage points lower than the forecast error of the traditional ARCH-GARCH model. The specially designed timing analysis module successfully captured the abnormal signal of the daily withdrawal of $12.7 billion of northbound funds during the global market meltdown in March 2020, and issued a liquidity depletion warning 18 hours in advance by analyzing the dynamic correlation between the VIX fear index and the offshore RMB exchange rate.

Keywords

Artificial Intelligence, Cross-border Investment, Market Liquidity, Price Discovery, Machine Learning

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References

[1]. Ding, Shusheng, et al. "Impact of board diversity on Chinese firms’ cross-border M&A performance: An artificial intelligence approach." International Review of Economics & Finance 92 (2024): 1321-1335.

[2]. Lareyre, Fabien, et al. "Big data and artificial intelligence in vascular surgery: time for multidisciplinary cross-border collaboration." Angiology 73.8 (2022): 697-700.

[3]. Shi, Jihua. "Research on Optimization of Cross‐Border e‐Commerce Logistics Distribution Network in the Context of Artificial Intelligence." Mobile Information Systems 2022.1 (2022): 3022280.

[4]. Alenezi, Atif M. "Artificial intelligence and foreign investment law arbitration: an analysis of regulatory framework implications." The Journal of World Investment & Trade 25.3 (2024): 369-409.

[5]. Liu, Wei, et al. "Independence or interdependence: The role of artificial intelligence in corporate entry mode for overseas energy investments." Journal of Innovation & Knowledge 9.3 (2024): 100518.

[6]. Ma, Shuzhong, Sishi Huang, and Peng Wu. "Intelligent manufacturing and cross-border e-commerce export diversification." International Review of Economics & Finance 94 (2024): 103369.

[7]. Ilmudeen, Aboobucker. "Big data, artificial intelligence, and the internet of things in cross-border E-commerce." Cross-border E-commerce marketing and management (2021): 257-272.

[8]. Khan, Asif. "The Intersection Of Artificial Intelligence And International Trade Laws: Challenges And Opportunities." IIUMLJ 32 (2024): 103.

[9]. Hyun, Eun-jung, and Brian Tae-Seok Kim. "Overcoming uncertainty in novel technologies: the role of venture capital syndication networks in artificial intelligence (AI) startup investments in Korea and Japan." Systems 12.3 (2024): 72.

[10]. Gregori, Wildmer Daniel, and Michela Nardo. "The effect of restrictive measures on cross‐border investment in the European Union." The World Economy 44.7 (2021): 1914-1943.

[11]. Ferencz, Janos, Javier López González, and Irene Oliván García. "Artificial Intelligence and international trade: Some preliminary implications." (2022).

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Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 3rd International Conference on Software Engineering and Machine Learning

Conference website: https://www.confseml.org/

ISBN：978-1-80590-063-4(Print) / 978-1-80590-064-1(Online)

Conference date: 2 July 2025

Editor：Marwan Omar

Series: Applied and Computational Engineering

Volume number: Vol.150

ISSN：2755-2721(Print) / 2755-273X(Online)

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