The integration of artificial intelligence into investment strategies has led to the rise of quantitative funds that outperform traditional benchmarks. By utilizing neural networks to process alternative data sources, such as satellite imagery and social media sentiment, investors can uncover hidden correlations that were previously inaccessible.
Robust alpha generation now relies on the ability to filter noise from signal within massive datasets. AI-driven models provide the computational power necessary to backtest complex scenarios and optimize portfolio allocations in real-time, ensuring that capital is deployed where it has the highest probability of return relative to risk.
About Enis
AI Engineer specializing in Machine Learning and LLMs. Combining Computer Engineering and Economics to build data-driven financial tools.