Reseña

The primary benefit of Machine Learning in finance is its ability to process vast, diverse datasets and uncover nonlinear relationships and interactions that traditional statistical models, such as simple linear regression, often miss. This leads to better risk management, more accurate asset pricing, and improved algorithmic trading strategies. While traditional time series models like ARIMA are statistical, ML methods such as Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTMs) are increasingly used for their ability to model complex temporal dependencies.