TRANSFORMING ASSET ALLOCATION THEORY THROUGH ROBO-ADVISORY AND ARTIFICIAL INTELLIGENCE

Farid Muhammad Rofifudin (Universitas 'Aisyiyah Surakarta)
Muhammad Rizqi Alriansyah Manurung (Universitas Muhammadiyah Gresik)
Syamsul Bakhtiar (Universitas Jendral Soedirman)

Jurnal Riset Entrepreneurship
Vol. 9 No. 2 (2026): (Edisi Agustus) Volume 9, Nomor 2, Tahun 2026

This study synthesises the global literature on the transformation of traditional asset-allocation theory, particularly Modern Portfolio Theory (MPT), toward decision models based on Artificial Intelligence (AI) and Robo-Advisory. Using a Systematic Literature Review (SLR) with the PRISMA 2020 protocol, 46 Scopus-indexed articles (2016–2026) were analysed. The findings document a paradigm shift from static mean-variance optimisation toward Deep Reinforcement Learning (DRL), hybrid FNN–DRL architectures, and the integration of alternative data such as ESG sentiment, Vision-Language climate perception, and digital assets. The study proposes an emerging "Algorithmic Portfolio Theory" and underlines the need for Explainable AI to secure accountability and investor trust in increasingly autonomous asset management.