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Harnessing AI in Quantitative Finance: Predicting GDP using Gradient Boosting, Random Forest, and Linear Regression Models

Authors: Farid Soroush

Predicting key macroeconomic indicators such as Gross Domestic Product (GDP) is a critical task in quantitative finance and economics. Precise forecasts of GDP can help in policy-making, investment decisions, and understanding the overall economic health of a country. Machine learning has emerged as a powerful tool in this domain, offering sophisticated techniques for modeling complex systems and making predictions. This project presents a comparative analysis of three machine learning models — Gradient Boosting Regressor, Random Forest Regressor, and Linear Regression — for predicting GDP. Our aim is to assess their performance and identify the model that provides the most accurate forecasts.

Comments: 7 Pages.

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Submission history

[v1] 2023-05-14 07:33:31

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