FORECASTING FUTURE INVESTMENT VALUE WITH MACHINE LEARNING, NEURAL NETWORKS, AND ENSEMBLE LEARNING: A META-ANALYTIC STUDY

Abstract

This meta-analytic study investigates the effectiveness of machine learning (ML), neural networks (NN), and ensemble learning models in forecasting future investment value across diverse financial markets. Using PRISMA 2020 guidelines, 108 peer-reviewed articles published between 2012 and 2022 were systematically selected from databases including Scopus, Web of Science, and IEEE Xplore. The study synthesizes empirical findings on model performance, feature engineering, and algorithmic robustness to evaluate predictive accuracy, generalizability, and practical applicability. Results indicate that neural networks—particularly deep learning architectures such as LSTM and CNN—demonstrate superior performance in capturing nonlinear patterns and temporal dependencies in financial time series data. Ensemble models such as Random Forest, XGBoost, and hybrid frameworks (e.g., stacking, bagging, boosting) consistently outperform standalone ML models in terms of accuracy, stability, and resistance to overfitting. Approximately 34% of reviewed studies integrated macroeconomic indicators, technical indicators, and sentiment analysis to enhance feature richness, while 28% adopted multi-asset forecasting involving equities, cryptocurrencies, and derivatives. Performance metrics such as RMSE, MAPE, and R² revealed that ensemble and deep learning models achieve up to 20–30% improvement in predictive reliability compared to traditional statistical models like ARIMA and linear regression. The review also highlights a growing emphasis on model interpretability, with techniques like SHAP and LIME being applied in 18% of studies to support explainability in high-stakes investment decisions. However, challenges remain in model transparency, computational complexity, and adaptability across volatile market conditions. Compared to earlier literature, this study reflects a paradigm shift from linear forecasting models to adaptive, data-driven approaches supported by AI technologies. The findings underscore the transformative potential of ML, NNs, and ensemble models in investment forecasting while calling for continued research into scalable, explainable, and risk-aware deployment strategies for real-world financial environments.