Functional Clustering of Discount Functions for Behavioural Investor Profiling

Abstract

ABSTRACT Classical finance models are based on the premise that investors act rationally and utilise all available information when making portfolio decisions. However, these models often fail to capture the anomalies observed in intertemporal choices and decision‐making under uncertainty, particularly when accounting for individual differences in preferences and consumption patterns. Such limitations hinder traditional finance theory's ability to address key questions such as how personal preferences shape investment choices, what drives investor behaviour, and how individuals select their portfolios. One prominent contribution is Pompian's model of four Behavioural Investor Types (BITs), which links behavioural finance studies with Keirsey's temperament theory, highlighting the role of personality in financial decision‐making. Yet, traditional parametric models struggle to capture how these distinct temperaments influence intertemporal decisions, such as how individuals evaluate trade‐offs between present and future outcomes. To address this gap, the present study employs Functional Data Analysis (FDA) and functional clustering techniques to investigate temporal discounting behaviours, complemented by robustness analyses that account for heterogeneity in the temporal structure of discount functions. Our findings reveal substantial heterogeneity within each temperament, suggesting that investor profiles are far more diverse than previously thought. This refined classification provides deeper insights into the role of temperament in shaping intertemporal financial decisions, with relevant implications for financial advisors seeking to tailor strategies to individual risk preferences and decision‐making styles.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 1 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.