What Is a Fat Model
A fat model is a financial or statistical model that includes many variables, parameters, or assumptions. It contrasts with a thin model, which uses fewer inputs and simpler structure. Fat models are common when analysts want to capture detailed behavior, complex interactions, or multiple risk factors in a single framework.
Where Fat Models Are Used
In finance, fat models appear in asset pricing, risk management, and portfolio construction. They may incorporate macroeconomic indicators, sector exposures, company fundamentals, and alternative data. In data science, fat models are often used in machine learning, where many features are included to improve predictive accuracy on complex datasets.
Advantages and Risks of Fat Models
Fat models can better capture real-world complexity and improve fit when data is rich. However, they also increase the risk of overfitting, where the model performs well on historical data but poorly on new observations. They require more data, stronger computational resources, and careful validation to remain reliable and interpretable.
