Enhancing Market Forecast Accuracy through Ensemble Predictive Modeling and Behavioral Data Integration
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V1I6P103Keywords:
Ensemble Learning, Market Forecasting, Behavioral Data Integration, Predictive Analytics, Sentiment Analysis, Machine Learning, Economic Modeling, Time-Series Forecasting, Model StackingAbstract
This paper proposes an ensemble-based behavioral forecasting framework that extends traditional market prediction methods beyond purely quantitative indicators such as price movements, macroeconomic variables, and historical patterns. The research is inspired by the increasing influence of sentiment-based and behavioral forces within the ever-changing and emotionally charged markets and combines consumer sentiment, search intensity on the internet, and social media presence with macro-level psychological indicators in a single predictive framework. The framework is a multi-layer ensemble of machine learning based on econometric models. Sentiment extraction pipelines, attention-based neural encoders, and normalized behavioral indices are used to transform noisy, unstructured behavioral data into robust predictive signals. Linear econometric models are used to capture long-term trends, tree based learners are used to model nonlinear interactions, and neural networks are used to identify latent behavioral patterns, stacking and model averaging are used to reduce overfitting and enhance generalization. Simulations and experiments on both simulated and actual-world data sets of equities, commodities and retail-demand series show that behavioral-enhanced ensembles are always superior to single-ML and conventional econometric baselines in the short and medium-term predictions. Error-decomposition indicates that the difference in forecast variance decreases with the addition of behavioral features, and forecasting turning points in the market is better by up to 27% during economic uncertainty periods, trending regimes, and event-driven cycles. The explainability analysis using SHAP also indicates that, in most cases, behavioral indicators are among the most important in the ensemble. The paper contributes a scalable methodology encompassing data preprocessing, mixed-type normalization, hyperparameter tuning workflows, and resilient stacking architectures suitable for turbulent market conditions. It offers a rigorous methodological foundation for behavior-driven forecasting in finance, retail analytics, and macroeconomic applications, and outlines future directions including multimodal behavioral data integration and guidelines for ethical, privacy-aware deployment.
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