Projects |

AI-Driven Automatic Wealth Management Solution

Sun, 31 Mar 2024 00:00:00 +0000

Period: April 2021 – March 2024
Funding: Innovation and Technology Fund (ITF), Hong Kong, Reference No. B/E003/20
Industry partner: Magnum Research Limited (AQUMON)

Problem

End-to-end digital wealth advisory requires integrating client profiling, goal-based portfolio construction, ongoing rebalancing, and monitoring within a scalable, auditable, and compliance-aware system. Existing approaches either rely on static rule-based logic or lack the modularity required for institutional deployment across diverse client segments and regulatory jurisdictions.

Method

The project developed a full-stack AI advisory architecture covering: (i) client profiling and suitability assessment using ML classifiers; (ii) goal-based portfolio construction integrating intertemporal allocation theory with modern estimation techniques; (iii) a hybrid-cloud data foundation supporting global asset universes and real-time feeds; (iv) high-fidelity backtesting with controls against overfitting; and (v) integration with execution infrastructure. Research components addressed model selection, uncertainty quantification in portfolio optimisation, and the design of modular research-validation-deployment pipelines.

Outcome

The project delivered a production-grade automated wealth management architecture deployed across multiple institutional client implementations. It established a reusable technical foundation for AQUMON’s robo-advisory and AI advisory services.

AI-Driven MPF Retirement Advisory System

Mon, 01 Jan 2024 00:00:00 +0000

Problem

Hong Kong’s Mandatory Provident Fund (MPF) system requires participants to actively select from a range of constituent funds within their employer-sponsored scheme, yet most participants lack the financial knowledge or time to make well-informed, dynamic fund-selection decisions. Retirement adequacy outcomes are systematically poor when fund allocation is not regularly reviewed.

Method

AQUMON developed an AI-driven MPF investment advisory system combining algorithmic goal-based personalisation, lifecycle and asset-liability management (ALM) informed optimisation, and explainable recommendation workflows. The system integrates personalised retirement-gap analytics, risk profiling, and constraint-aware fund selection within a compliance-ready architecture suitable for deployment in regulated insurance and pension distribution environments. Distributed computation enables real-time recommendation generation across large participant bases.

Deployment

The system has been deployed with multiple MPF trustees and insurance providers in Hong Kong, including AIA (integrated into the AIA+ mobile app) and BCT Group (MARIO, BCT’s market-first AI advisor). The platform is designed to support approximately seven million Hong Kong MPF participants, with cumulative assets under advice exceeding USD 10 billion.

Research connection

This project draws on doctoral and post-doctoral research in stochastic control and optimal withdrawal policy design. The production deployment extends those theoretical results into a regulated, real-world context.

ESG Quantitative Scoring System and ML-Driven Investment Research

Sun, 01 Jan 2023 00:00:00 +0000

Problem

Existing ESG ratings from major providers suffer from low cross-provider agreement, limited transparency, and methodological subjectivity. A more systematic, data-driven approach is needed that integrates structured financial data with unstructured news and disclosure content, updates at high frequency, and supports both index construction and active strategy development.

Method

AQUMON developed a four-layer evaluation framework — Pillar, Category, Topic, and Metric — covering environmental, social, and governance dimensions for a universe of listed companies. The system integrates structured financial and regulatory data with unstructured news, corporate disclosures, and sentiment signals using multimodal ML pipelines. Dynamic weight updating and cross-industry comparability were explicit design requirements. The scoring methodology was designed to satisfy IFRS S1 and IFRS S2 disclosure frameworks and supports both institutional investment mandates and corporate sustainability reporting.

Outcome

The system provides daily ESG score updates covering 3,000+ listed companies. Backtested long-only strategies combining ESG scores with fundamental signals demonstrated improved risk-adjusted performance relative to market benchmarks. The framework has been adopted by the HKSAR Government and multiple listed companies for sustainability reporting and investment mandate compliance.

Portfolio Management via Reinforcement Learning

Fri, 31 Dec 2021 00:00:00 +0000

Period: January 2019 – December 2021
Funding: Innovation and Technology Fund (ITF), Hong Kong, Reference No. UIM/365
Industry partner: Magnum Research Limited (AQUMON)

Problem

Standard portfolio rebalancing rules are based on fixed-weight targets and threshold-trigger mechanisms that do not adapt to evolving market conditions or explicitly account for transaction costs. A principled sequential decision-making framework is needed to learn rebalancing policies that remain effective under diverse market regimes and realistic cost structures.

Method

The portfolio rebalancing problem is formulated as a Markov Decision Process (MDP) with state variables capturing portfolio composition, market features, and risk indicators; actions corresponding to rebalancing decisions; and a reward function that penalises both tracking error and transaction costs. We explored value-based and policy-gradient reinforcement learning algorithms, including recurrent and sequence-based architectures that condition on the trajectory of market states. Emphasis was placed on sample efficiency, robustness to distribution shift, and deployability in a production rebalancing pipeline.

Outcome

The project produced a research-to-production foundation for RL-based portfolio management, with documented methodology, experimental benchmarks, and a reusable framework for systematic strategy development and evaluation at AQUMON.