Probabilistic machine learning techniques have shown great promise for improving investment and financial decision making. As more data becomes available, these methods allow investors and firms to uncover subtle patterns and insights. However, applying machine learning to finance also comes with challenges. This article provides an overview of some key takeaways for using probabilistic machine learning successfully in finance.
ML can help quantify uncertainty and risk
While finance has relied heavily on statistical methods in the past, machine learning models like Bayesian neural networks provide better ways to model uncertainty and capture risk. This is critical for portfolio optimization, trade execution, risk management and more.
Garbage in, garbage out still applies
Machine learning models are only as good as the data used to train them. Cleaning and preprocessing financial data to remove biases and inaccuracies continues to be an essential step before applying any models.
Interpretability remains important
While some complex ML models can provide good performance, being able to understand and interpret the reasoning behind their outputs is still vital for practical use in finance. Methods to improve interpretability are an active area of research.
In summary, probabilistic machine learning brings useful new capabilities but also new challenges for financial applications. Success requires addressing the garbage in, garbage out problem, as well as finding the right balance between accuracy and interpretability.