From Prediction to Decision: A Survey of Machine Learning Applications in Quantitative Finance

With the explosive growth of computer science and big data, quantitative finance is undergoing a huge shift from traditional econometrics to data-driven Artificial Intelligence (AI). This study aims to review the applications of Machine Learning (ML), Deep Learning (DL), and Reinforcement Learning (RL) in vital areas such as asset pricing, algorithmic trading and risk management. Research shows that Deep Neural Networks (DNNs) capture non-linear market patterns, Natural Language Processing (NLP) analyses unstructured data while providing superior sentiment signals compared to generic alternatives. Deep Reinforcement Learning (DRL) can effectively automate execution in dynamic market environments, which optimizes trading decisions. Similarly, Temporal Fusion Transformers (TFT) have emerged as a dominant architecture for multi-horizon time series forecasting, offering superior interpretability and accuracy over standard recurrent networks. These ML models greatly outperform traditional methods like CAPM and ARIMA in prediction accuracy and handling complex data. However, there are still challenges about the lack of interpretability ("black box") and overfitting. In the future, quantitative finance lies in combining "Explainable AI" (XAI).