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In practical applications, Intelligent Alpha pairs fundamental data—such as earnings, revenue, and market capitalization—with qualitative factors that the models may not inherently understand. The models can offer insights into why specific stocks are chosen for portfolios, thereby enriching the decision-making process.
Testing and Portfolio Construction
A unique aspect of Intelligent Alpha's approach is its perspective on backtesting. Clinton expressed skepticism about the traditional backtesting methodology used in quantitative finance, stating that the inherent limitations of AI models, including their data cutoff dates, make conventional backtesting less effective. Instead, the focus is on real-time analysis and adapting to current market conditions.