Which machine learning models are commonly used in market research?

Linear and regularized models

Linear models, including regularized versions such as ridge and lasso, are simple, fast, and easy to inspect. In noisy markets their simplicity is a strength, because they are harder to overfit and their behavior can be understood. Many serious researchers start here and often stay here.

Tree ensembles

Methods such as random forests and gradient boosted trees handle non linear relationships and mixed inputs well, and they are popular for tabular market data. They can still overfit, and their outputs need careful validation, but they are a reasonable workhorse for structured features.

Neural networks and sequence models

Neural networks, including sequence models used on time series and language models used on text, are powerful but data hungry and prone to overfitting on the limited, noisy data markets provide. They shine most on large text or alternative data tasks rather than on raw short horizon price prediction.

This article is general educational information only. It is not financial, investment, or trading advice, and not a recommendation to buy or sell any instrument. Markets carry risk of loss, and past performance does not guarantee future results. Consult a qualified, licensed professional before making any decision.