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Deep Learning-Based Applications of the Familiarity Effect
Abstract
This study investigates the predictability of the familiarity effect—a cognitive bias driving investors toward familiar assets—using machine learning (ML) models. Leveraging Germany's SAVE-Study dataset enriched with behavioral variables, we employ XGBoost and deep learning (MLP) architectures to predict future savings rates. Empirical results confirm that familiarity-driven variables significantly enhance predictive accuracy demonstrating that historical financial data combined with behavioural indicators explain more variance in savings behaviour than traditional models alone. Scenario analyses reveal that reduced familiarity correlates with higher capital price volatility and unstable investment returns, aligning with behavioural theories of uncertainty avoidance. Ethical implications are addressed, emphasising algorithmic transparency to mitigate bias amplification in recommendation systems. Our framework bridges behavioural finance and ML, transforming cognitive biases into quantifiable predictors for equitable investment systems.
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