Abstract
This review surveys how contemporary machine learning is reshaping financial and economic forecasting across markets, macroeconomics, and corporate planning. We synthesize evidence on model families, such as regularized linear methods, tree ensembles, and deep neural architecture, and explain their optimization (with gradient-based training) and design choices (activation and loss functions). Across tasks, Random Forest and gradient-boosted trees emerge as robust baselines, offering strong out-of-sample accuracy and interpretable variable importance. For sequential signals, recurrent models, especially LSTM ensembles, consistently improve directional classification and volatility-aware predictions, while transformer-style attention is a promising direction for longer contexts. Practical performance hinges on aligning losses with business objectives (for example cross-entropy vs. RMSE/MAE), handling class imbalance, and avoiding data leakage through rigorous cross-validation. In high-dimensional settings, regularization (such as ridge/lasso/elastic-net) stabilizes estimation and enhances generalization. We compile task-specific feature sets for macro indicators, market microstructure, and firm-level data, and distill implementation guidance covering hyperparameter search, evaluation metrics, and reproducibility. We conclude in open challenges (accuracy–interpretability trade-off, limited causal insight) and outline a research agenda combining econometrics with representation learning and data-centric evaluation.