INTRODUCTION

Advances in data science, machine learning and artificial intelligence mean that problems in asset management, algorithmic trading and computational finance can now be tackled with open-source programming tools and packages running on easily scalable infrastructures in the cloud.

Machine Learning algorithms can help asset managers synthesize extremely large data sets and reveal patterns, trends within. Yet few asset managers go further down the path to explore and implement these techniques. Some of the biggest financial institutions, hedge funds and asset management firms are already using Python to implement portfolio management, trading and risk management systems.

While these tools are useful, they cannot replace human judgement. Professionals who are already in the asset management industry can, however, complement their domain expertise with technical knowledge in data science, which will help them to make better, faster decisions in managing their investment portfolios.

LEARNING OUTCOMES: 

1. Apply different machine learning algorithms and deep learning techniques
2. Use the various techniques to build an optimal investment portfolio
3. Use the various Python and Jupyter Notebooks, which can serve as templates and frameworks that can be adjusted to the individual requirements of the participants, for example, with regard to the data that is used

PRE-REQUISITES: 

• This programme is designed to be technical and will deep dive into niche technical capabilities to equip participants with the necessary domain expertise.
• Due to the technical nature of the programme, this is suitable for portfolio managers, data analysts or quantitative analysts who like to understand the various Machine & Deep Learning Algorithm and use these approaches to build optimal investment portfolio.
• Participants should have a good foundation in Python programming or willing to learn the foundation of Python for Finance prior to joining the programme*

*Complimentary preparatory course (online) made available as part of the programme. 

PARTICIPANTS' FEEDBACK

"Useful for beginners to get an overview of how AI/ML can be applied in asset management." - VP, Equity Porfolio Manager, Local Fund House

"Highly relevant skillset and practical application to actual use cases." - Director, Investment Risk & Performance Analytics, Local Fund House

TYPE OF COURSE

Synchronous virtual training

COURSE SCHEDULE [UPDATED AS OF 22 DEC 2022]

The programme will be conducted across 10 sessions of 1.5hours classes.  Click on "Enrol Now" to register for the January run of classes. 

Modern Portfolio Theory:

• Mon, 30 Jan, 4.30pm to 6.00pm (SGT)
• Tue, 31 Jan, 4.30pm to 6.00pm (SGT)
• Thu, 2 Feb, 4.30pm to 6.00pm (SGT)

Artificial Intelligence for Finance

• Fri, 3 Feb, 4.30pm to 6.00pm (SGT)
• Thu, 9 Feb, 4.30pm to 6.00pm (SGT)
• Fri, 10 Feb, 4.30pm to 6.00pm (SGT)
• Thu, 16 Feb, 4.30pm to 6.00pm (SGT)

Machine Learning for Asset Management

• Fri, 17 Feb, 4.30pm to 6.00pm (SGT)
• Thu, 23 Feb, 4.30pm to 6.00pm (SGT)
• Fri, 24 Feb, 4.30pm to 6.00pm (SGT)

Click here to download brochure.

FEE*

Three (3) modules at $2000.00 per module.

FTS Funding (capped at $500) per module for Singapore Citizens/PR sponsored by eligible companies.

*FTS-Eligible
This programme is recognised under the IBF Financial Training Scheme (IBF FTS) and is eligible for IBF FTS claims subject to all eligibility criteria being met.

Please note that in no way does this represent an endorsement of the quality of the training provider or programme. Participants are advised to assess the suitability of the programme and its relevance to participants’ business activities or job roles.

The IBF FTS is available to eligible entities based on the prevalent funding eligibility, quantum and caps. IBF FTS claims may only be made for recognised programmes with specified validity period. Please refer to www.ibf.org.sg for more information.