Portfolio Construction Under Behavioral Distortions and Narrow Framing: A Machine Learning Approach

This paper develops a portfolio construction methodology integrating behavioral finance principles with machine learning to model how cognitive biases systematically alter asset allocation decisions. We introduce a Distorted Value Transformation framework wherein investors apply linear and non-linear value functions to individual asset returns before aggregating, exhibiting narrow framing from mental accounting bias. Using Random Forest regression, we quantify asset importance under three distinct investor personae, namely Cumulative Prospect Theory investors (loss aversion, diminishing sensitivity), Loss-Averse investors (asymmetric loss weighting), and Markowitz investors (risk-seeking preferences). Our empirical analysis of a multi-asset portfolio spanning traditional instruments and major cryptoassets (2015–2025, T = 2580 daily observations) reveals behavioral distortions produce systematic reweighting: CPT and LA investors substantially reduce exposure to high-volatility assets (Bitcoin allocation increases from 13.77% to 20.57% under CPT; XRP decreases from 17.82% to 13.51%), reflecting perceptions that volatile assets contribute disproportionately to negative experiences. Markowitz investors concentrate heavily on high-skewness cryptoassets (40.22% in XRP). Behaviorally constructed portfolios exhibit lower volatility (75.43% vs. 78.19% annualized) and reduced drawdowns versus undistorted benchmarks, albeit with foregone upside 29,732% vs. 51,005% cumulative return in the crypto-only scenario). These findings demonstrate that returns perceived through behavioral lenses via segregation rather than integration deviate systematically from rational benchmarks. Our framework provides a tractable method for modeling heterogeneous investor behavior and how psychological factors shape asset allocation.