Rand Low

Rand Low is Honorary Associate Professor at the University of Queensland and an Associate Professor of Quantitative Finance at Bond Business School.

Assoc. Professor Rand Low’s research areas are in asset and investments management, specifically correlation/dependence modelling, portfolio optimization, risk management, systematic trading strategies and commodities investing strategies. His work has been published in leading academic and industry journals such as Journal of Banking & Finance, Quantitative Finance, Journal of Empirical Finance, Journal of Investing, Journal of Commodities Markets, Resource Policy, and Journal of Risk. Rand is an avid supporter of Open Access Journals and multi-disciplinary research. He is an Editor of the Special Issue of Mathematics: Mathematical Models and Applications in Finance (Impact Factor: 2.6; Q1 Journal Ranking)

Prior to his PhD studies, Assoc Prof. Professor Low worked in control systems engineering and management roles for Honeywell for landmark engineering projects such as GOMA, SLQ, Brisbane Square, Mater Mothers' Hospital, St Andrews Hospital, and more where he achieved the Chartered Engineer designation from Engineers Australia. During his PhD studies, he won the GSITA Award and 3MT competitions. Upon completing his PhD, he received the Dean's Award for Research Higher Degree Excellence, a research fellowship on portfolio optimization & risk management techniques for financial crises and an Australia Awards - Endeavour fellow. He has been a visiting research fellow at the New York University - Stern School of Business, and an Australian Institute of Business and Economis (AIBE) Scholar He has also had Visiting Research Fellow appointments at the University of Strathclyde, Glasgow, UK and Sunway University, Kuala Lumpur, Malaysia.

Assoc. Prof Low research expertise has allowed him to successfully transition into industry as he has worked at the global headquarters of Bank of America Merrill Lynch and BlackRock in New York City. He led teams of quantitative researchers in building mathematical models for market/credit/operational risk, securities lending, structured products, asset-backed securities, and portfolio management. He has also defended quantitative model development practices on behalf of these institutions to US regulators such as the Federal Reserve (FED) and the Office of the Comptroller of Currency (OCC). He is worked on quantiative model stress-testing, model risk management practices, and model risk governance for major global financial institutions.

Assoc. Professor Low’s is interested in applying statistical and machine learning techniques in automating business processes and investments management in areas such as corporate credit ratings, robo-advisors, digital assets (i.e., cryptocurrencies, blockchain), commodities, and systematic active investment strategies.