Hybrid Approaches for Stocks Prediction and Recommendation System

Abstract

Hybrid approaches to stock prediction and recommendation are a critical area of research for individual investors and financial institutions. Traditional methods have limitations, leading to the emergence of hybrid models. This paper reviews current research on hybrid models, including GAN-based, LSTM-based, and neural network-based models, Soft Computing based, GRU based models to provide optimal results, for stock recommendation techniques include sentiment analysis, which uses natural language processing to analyze news articles and social media posts, and network analysis, which examines the relationships between stocks to identify stocks likely to move together. It also discusses evaluation metrics used to assess the performance of these models and then it provides the generalize pipelines that can be kept in mind while researching and developing a recommender engine, it also shows the future direction in order to build the hybrid recommenders as well as predictors, making it a valuable contribution to the stock prediction and recommendation field.