Advancements in Artificial Intelligence and Machine Learning for Stock Market Prediction: A Comprehensive Analysis of Techniques and Case Studies
Overview of AI and ML techniques for stock market prediction, including data types, evaluation metrics, and methodologies.
What it examines
This study provides an overview of AI and machine learning techniques for stock market forecasting, detailing data types, evaluation metrics, and neural network structures. It aims to help researchers stay updated and replicate previous studies as a baseline.
What it concludes
The study concludes that AI models significantly improve stock market predictions. Potential applications include better investment decisions and financial planning. Future research should explore more advanced models and diverse data sources.
Evidence objects
The study concludes that AI models significantly improve stock market predictions. Potential applications include better investment decisions and financial planning. Future research should explore more advanced models and diverse data sources.
key_findings bullet 1 · key_findings · validation V0
Raw abstract and provenance
Stock market forecasting is a classic but challenging problem that has attracted the attention of economists and computer scientists. The activity of trading involves high risks, the investors may lose a part of the totality of the amount they invested. Hence a need for more intelligent techniques to help make investment decisions. The purpose of this study is to provide first an overview of artificial intelligence and machine learning techniques used in recent studies for forecasting the stock market, then to present not only the different data types, commonly used evaluation metrics, and different neural network structures but also to provide a new proposition research method. Our objective is to help researchers stay abreast of the latest advances and help them easily replicate previous studies as a baseline.
Source row: 116 · abstract type: unknown