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Developing Innovative International Asset Allocation Strategies Using Artificial Intelligence and Machine Learning Models

Bodaghi, Faraz | 2025

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 58283 (44)
  4. University: Sharif University of Technology
  5. Department: Management and Economics
  6. Advisor(s): Zamani, Shiva
  7. Abstract:
  8. On the one hand, the diminishing borders between countries in global financial markets, and on the other hand, the democratization and diversification of investment options due to technological advancements, have enabled investments in assets beyond one's home country. Furthermore, the emergence of multipolarity in the global economy, with the rise of economies challenging the dominance of Wall Street’s financial system, has led even American investors to shift their focus toward international markets. Geopolitical and geoeconomic transformations have also made the idea of global investment, while considering all associated risks and complexities, appear to be a prudent choice. Therefore, this research focuses on presenting global asset allocation strategies using artificial intelligence models to optimize the international allocation of Exchange-Traded Funds (ETFs). This study introduces innovative asset allocation frameworks leveraging advanced machine learning algorithms, such as CNN and LSTM, aimed at improving portfolio performance and determining optimal allocations in international markets, taking into account risks and returns. The thesis compares the performance of traditional asset allocation strategies, such as 60/40 and 80/20 portfolios, with deep learning-based models and demonstrates that the latter outperforms conventional methods in terms of risk-adjusted returns. Additionally, this research integrates macroeconomic factors and exchange rate risks to enhance the models, providing valuable insights into the effectiveness of artificial intelligence in wealth management and portfolio optimization. The results clearly indicate that deep learning models can effectively improve global asset allocation by dynamically adjusting portfolio weights according to evolving market conditions. The findings of this study offer a forward-looking approach to utilizing artificial intelligence in international asset allocation and aim to play a distinct practical and theoretical role in the field of financial management
  9. Keywords:
  10. Asset Allocation ; Machine Learning Model Deployment ; Machine Learning ; Currency Risk ; Corporate Wealth Management ; Capital International Allocation ; Artificial Intelligence

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