Predictive Models for Monetary Asset Price Evaluation: A Comparative Review

Abstract

This study focuses on analyzing and evaluating predictive techniques for asset price forecasting, centering attention on gold, real estate, and automobile markets. The paper explores numerous algorithms, techniques, methods and models utilized in foreseeing the values of these assets. A thorough appraisal is conducted that which present various procedures for forecasting the value of assets. This exploration compares the pros, cons, and performance metrics of the anticipating models applied in each discipline. Remarkable attention is granted to the forecasting ability of Convolutional Neural Networks (CNN), fuzzy rule-based systems, deep learning (DL) techniques, ensemble regression models, and other machine learning (ML) algorithms. Moreover, the tasks of data analysis, preprocessing and feature selection methods in boosting prediction accuracy is investigated. The review paper calls attention to the implications along with applications of error-free asset value forecasting, together with knowledge-based decision making, risk mitigation in addition to investment strategies. Moreover, it examines the challenges and limitations along with future directions in the domain, highlighting the demand for robust, compliant and interpretable forecasting models. By assessing and differentiating the approaches and outcomes of asset value prediction across contrasting fields, this review delivers important insights appropriate to researchers, professionals and decision-makers concerned in the dynamics and predictive potentials of these platforms.