AI Solver for Hard Financial Decision Making

Healthcare business graph data and growth, Stethoscope with doctor's health report clipboard on table, Medical examination and doctor analyzing medical report on laptop screen.

This project aims to develop efficient AI solution methodologies for solving long-standing financial optimization problems and challenging issues that are emerging. The final deliverable will be a software system which includes a family of solution packages for different financial decision problems.

The past half-century has witnessed a remarkable advancement in modern financial decision theory and practice, both in enhancing our fundamental understanding of market randomness, and in enabling market participants to harness appropriate risk for a better return.

Still, many financial decision-making and risk management problems remain open and challenging, and are nonconvex in nature. Essentially, if we incorporate real world considerations into our financial decision-making models (for example, investors’ asymmetric risk attitude towards gain and loss), we will be surrounded by a nonconvex world. One example is the mean-Value-at-Risk (VaR) portfolio selection problem, which is both nonconvex and discontinuous. In fact, the real financial world generates endless lists of nonconvex optimization problems, including portfolio selection under high-moment risk measures (skewness and kurtosis for example), cardinality constrained portfolio selection, risk parity strategy, tax-loss harvesting, and fixed income portfolio.

A long list of efforts from optimization, statistics and machine learning has led to relatively efficient algorithms for solving nonconvex optimization problems: stochastic gradient descent, momentum regularization, variance reduction, and mini-batch optimization. Also neurodynamics-based portfolio optimization deserves in-depth investigation in its own right because of the distinctive complexities in depth and scale of financial engineering and management.

Share this content

Read More

Analytics for Digital Relationship Management

Address

Units 1101-1102 & 1121-1123,
Building 19W Science Park West Avenue,
Hong Kong Science Park,
Shatin, Hong Kong

Products & Solutions

People

About Us

Address

Copyright © 2023 Laboratory for AI-Powered Financial Technologies Ltd. All Rights Reserved.