18 monthsProgramme duration
37,543 USDTuition Fee/year
SepStarting Month
Programme overview
Main Subject
Mathematics
Degree
MSc
Study Level
Masters
Study Mode
On Campus
Programme Aims
As a leading international financial centre, Hong Kong is home to many financial institutions and is a gateway to the world. To provide innovative and stable financial services in increasingly complex financial markets, professionals require advanced knowledge in mathematics, statistics, finance and programming. This programme aims to provide in-depth coursework-based training in quantitative and analytical methods, modelling techniques, financial concepts and programming skills that will allow our graduates to professionally and competitively steer decision making under different market conditions. In addition, this programme provides students with solid training in a variety of new skills and techniques demanded by the new era of financial technology including artificial intelligence, blockchain, cloud computing and big data. Our graduates will become leading professionals in the financial industry and related fields and will competently apply the advanced quantitative methods and latest techniques that characterise financial innovation and the development of financial technology.
Characteristics
The programme is hosted by the Department of Applied Mathematics and supported by the School of Accounting and Finance, meeting the multi-disciplinary nature of quantitative finance and financial technology. All of the courses are taught by world-leading experts in quantitative finance, data analytics, risk management and machine learning. The programme will equip graduates with solid knowledge of quantitative methods and proficient programming skills. The programme also provides professional and career training to help students to apply principles and methodologies to real-life problems in financial services and financial technology.
Programme overview
Main Subject
Mathematics
Degree
MSc
Study Level
Masters
Study Mode
On Campus
Programme Aims
As a leading international financial centre, Hong Kong is home to many financial institutions and is a gateway to the world. To provide innovative and stable financial services in increasingly complex financial markets, professionals require advanced knowledge in mathematics, statistics, finance and programming. This programme aims to provide in-depth coursework-based training in quantitative and analytical methods, modelling techniques, financial concepts and programming skills that will allow our graduates to professionally and competitively steer decision making under different market conditions. In addition, this programme provides students with solid training in a variety of new skills and techniques demanded by the new era of financial technology including artificial intelligence, blockchain, cloud computing and big data. Our graduates will become leading professionals in the financial industry and related fields and will competently apply the advanced quantitative methods and latest techniques that characterise financial innovation and the development of financial technology.
Characteristics
The programme is hosted by the Department of Applied Mathematics and supported by the School of Accounting and Finance, meeting the multi-disciplinary nature of quantitative finance and financial technology. All of the courses are taught by world-leading experts in quantitative finance, data analytics, risk management and machine learning. The programme will equip graduates with solid knowledge of quantitative methods and proficient programming skills. The programme also provides professional and career training to help students to apply principles and methodologies to real-life problems in financial services and financial technology.
Admission Requirements
Bachelor’s degree with honours in mathematics, statistics, business, finance, science, computer science, engineering, or the equivalent. Applicants with a Bachelor’s degree in other disciplines and an adequate background in mathematics or IT will also be considered.
Tuition fees
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