Data is the canvas of reality.
As a researcher with over three decades of consulting and industry experience, I integrate sustainable finance, modern portfolio theory, and artificial intelligence to develop innovative investment solutions.
I am a finance professor and hold the Commercial International Bank Endowed Chair at the American University in Cairo. I earned two MBAs from triple-crowned universities - Henley Business School (UK) and Frankfurt School of Finance & Management (Germany) - as well as a Doctor of Business Administration (DBA) from the University of Reading (UK). I regularly publish peer-reviewed research and books that synthesize my interdisciplinary perspectives. I complete my PhD in Business Analytics at the Mitchell College of Business at the University of South Alabama.
Noah Farhadi
Artificial Intelligence (AI) represents a transformative frontier in quantitative finance and data analytics. Through machine learning algorithms and neural networks, AI enables sophisticated pattern recognition and predictive modeling beyond traditional statistical approaches. My work focuses on developing and implementing AI solutions for financial applications, including market prediction, risk assessment, and portfolio optimization.
I specialize in supervised learning techniques, causal inference methodologies, and ensemble methods that combine multiple algorithms to enhance predictive accuracy. By leveraging both classical machine learning and deep learning architectures, I create robust models that can process complex, multi-dimensional financial data while accounting for market dynamics and behavioral factors. This research integrates cutting-edge AI innovations with established financial theory to advance both analytical capabilities and practical applications in investment management.
Post-modern Portfolio Theory seeks to construct optimal portfolios to maximize expected returns for a given level of risk. It uses mathematical models to analyze how investments as a collective group behave, quantifying the diversification benefits and risk reduction of combining different securities. Markowitz introduced the groundbreaking concepts of the efficient frontier and modern portfolio theory, fundamentally changing investment philosophy.
My research extends these foundations by incorporating advanced risk measures and behavioral aspects into portfolio optimization. I focus on developing frameworks that go beyond variance to capture downside risk, skewness, and extreme events in asset returns. This work integrates environmental, social, and governance (ESG) factors into portfolio construction, creating more comprehensive risk-return models that reflect modern market dynamics.
Sustainable Finance integrates environmental, social and governance (ESG) factors into financial decisions and economic activities. It incentivizes sustainable corporate practices by incorporating sustainability risks and opportunities into investment analysis, lending guidelines, insurance underwriting and financial advisory.
Key focus areas include green investments, social impact funds, ESG data analytics and corporate stewardship through shareholder advocacy and engagement. The sustainable finance movement aims to better align financial flows with sustainable development goals.
My research in this field focuses on quantifying ESG impact through innovative empirical methodologies. I develop frameworks that combine machine learning techniques with traditional financial metrics to assess how sustainability practices affect corporate performance. Through causal inference and predictive modeling, I investigate the relationship between environmental management, governance structures, and financial outcomes, particularly in emerging markets.
Data Analytics
My role entails envisioning and constructing AI models finance to serve current and future knowledge discovery through customized prediction, pipeline development, causal inference, and purpose-built networks.
With an AI-driven architectural mindset interwoven with incisive exploration, I probe extensive, multi-dimensional datasets to uncover financial trends, risks, and opportunities.
I continually formulate research questions to expose deeper insights and expand the body of knowledge. Concurrently, I investigate developing and emerging financial markets, nonlinear interrelationships, and causal inference.