Simulation-Based Evaluation of Portfolio Optimization Algorithms for Robo-Advisory Systems

Chandra  Lukita; Meria Zakiyah Alfisuma; Natasha Leonie

doi:10.34306/itsdi.v7i2.722

Authors

Chandra Lukita University of Catur Insan Cendekia https://orcid.org/0009-0006-6035-8241
Meria Zakiyah Alfisuma University of Trunojoyo Madura
Natasha Leonie Mfinitee Incorporation https://orcid.org/0009-0001-7488-2224

DOI:

https://doi.org/10.34306/itsdi.v7i2.722

Keywords:

Robo-Advisory, Portfolio Optimization, Modern Portfolio Theory, Heuristic Models, Financial Algorithms

Abstract

This study investigates the performance of portfolio optimization algorithms in robo-advisory systems within the digital finance landscape. The research compares three approaches Modern Portfolio Theory (MPT), Post-Modern Portfolio Theory (PMPT), and a heuristic equal-weight model using a simulation-based computational framework with synthetic financial data under controlled market
conditions. Key evaluation metrics include Sharpe ratio, Sortino ratio, and maximum drawdown to assess risk-adjusted performance and downside protection. The results show that optimization-based models outperform the heuristic approach across all metrics. MPT achieves the highest Sharpe ratio (1.25), indicating strong overall risk-adjusted returns, while PMPT provides superior downside risk management with a higher Sortino ratio (1.60) and lower maximum drawdown (0.14). The heuristic model demonstrates the weakest performance due to its lack of adaptive allocation. These findings highlight the trade-offs between return optimization and risk sensitivity across different algorithms. Despite their effectiveness, the models are limited by reliance on historical data and simplified assumptions in the simulation environment. This study suggests that future robo-advisory systems should integrate artificial intelligence and behavioral finance to enhance adaptability, personalization, and decision transparency in dynamic market conditions.

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