Machine Learning Models Used in Finance

Regression, decision trees, neural networks, and ensembles show up constantly in financial machine learning. Here's what each one actually does, in plain terms.

Regression models: the starting point

A regression model estimates how a set of inputs relates to an outcome by fitting a weighted combination of them. Linear regression predicts a continuous number — like next quarter's expected revenue growth — as a straight-line combination of features. Logistic regression predicts a probability, such as the likelihood a loan defaults or a stock beats the market over the next month. Regressions are simple to interpret — you can point to exactly how much each input contributed — which makes them useful both as standalone tools and as a baseline that fancier models have to justify beating.

Decision trees

A decision tree splits data step by step using yes/no questions — "is the price-to-earnings ratio above 20? is momentum over the last month positive?" — narrowing down to a final prediction at the end of each branching path. A single tree is easy to read and visualize, but on its own tends to overfit: it can carve up the training data into very specific rules that don't hold up on new data. That weakness is exactly what ensemble methods are built to fix.

Ensemble methods

Random forests build hundreds of decision trees, each trained on a slightly different random slice of the data, and average their predictions. Gradient boosting builds trees sequentially, with each new tree specifically trying to correct the errors of the ones before it. Both approaches turn a collection of individually mediocre, overfit-prone models into a single, more stable prediction — which is why ensembles are a default choice for many tabular financial prediction problems, from credit scoring to factor-based stock ranking.

Neural networks

A neural network passes data through layers of interconnected nodes, each layer learning a transformation of the previous one's output, so the model can build up increasingly abstract representations of the input on its own rather than relying on features a person defined by hand. This is powerful for messy or sequential data — text, price paths over time, images — but it also means the model is harder to interpret and typically needs more data to train reliably than a regression or tree-based model. Our deep learning guide goes deeper on how this applies specifically to price forecasting.

Matching the model to the problem

• Small, clean datasets with a need for interpretability — regression or a single decision tree.
• Tabular data with complex, non-linear relationships — gradient boosting or random forests.
• Unstructured or sequential data — text, order flow, images — neural networks.

None of these categories is a hierarchy where "more advanced" beats "simpler." The right choice depends on the data available, how much the result needs to be explained to a risk or compliance function, and how much history exists to validate the model against — and even the best-fitting model is still describing the past, not guaranteeing the future.