Machine Learning & AI in Quantitative Finance Conference, London: 16th - 17th November 2017 & Blockchain Developments in Financial Markets Conference, London: 23rd & 24th November 2017
Dmytro Pershyn is a founder of Quantlife LTD – consulting company that focuses on DLT startups.Dmytro is early crypto adopter and Distributed Ledger Technology professional. He works as a Business Analyst, IT consultant and Investor with DLT startups and corporate innovators, in particular:• London Block Exchange• Zilliqa (top-40 by marketcap)• Venture Capital funds (NDA)
Massimo Morini: Head of Interest Rate and Credit Models, Banca IMIMassimo Morini is also Coordinator of Model Research. Massimo is Professor at Bocconi University and MSc Director at Milan Polytechnic, and he was Research Fellow at Cass Business School, London. He has published papers in journals including Risk Magazine, Mathematical Finance, and the Journal of Derivatives, and is the author of “Understanding and Managing Model Risk: A Practical Guide for Quants, Traders and Validators” and other books on credit, funding and interest rate modelling. Massimo holds a PhD in Mathematics.
Blockchain and Cryptocurrency have become an essential part of the Fintech field, gaining more and more traction in traditional finance. There are numerous Distributed Ledger Technology (DLT) startups, job openings and internal corporate projects. However, most finance professionals are not proficient in this domain area and there is a lack of consistent educational resources. The Distributed Ledger Technology in Finance Certificate (DLT) will close this gap.
The Distributed Ledger Technology in Finance Certificate (DLT) is a 16 week qualification comprising of 3 modules, 12 lecture weeks, 3 assignments, a practical final project and a final sit down 3 hour examination using our global network of examination centres. This course has been designed to empower individuals who work in or are seeking a career in Blockchain, Cryptocurreny or Distributed Ledger Technology in finance. The DLT is a career-enhancing professional qualification, that can be taken worldwide.
Week 1: Introduction to Cryptography
Introduction to Cryptography
Bitcoin Consensus Mechanism
Week 2: Decentralization and Smart Contracts
Previous lecture recap:
Bitcoin internal scripting mechanism
Week 3: Network Architecture
Previous lecture recap
Network governance structures
Week 4: Guest Lecture
Home assignment: white paper analysis for every student
Week 5: Home assignment review seminar
Week 6: DLT Domain Projects Overview and Research
Previous module recap
Internet of value
Types of tokens:
Project examples from every class:
Week 7: DLT Projects Business Models
Blockchain as a Service
Distributed Ledger as an enterprise solution
Week 8: Market Dynamics
Bitcoin as a starting point
Financial bubbles review & explanation
ICO market dynamics
Week 9: Guest Lectures
Home assignment due-diligence for a chosen project
Week 10: Home assignment review seminar
Week 11: Fintech Policy Overview (US, UK, EU, Malta)
Brief history of FinTech
Regulatory challenges faced by Fintech companies
Regulatory infrastructure of different jurisdictions
Week 12: Cryptocurrency Regulation (US, UK, EU, Malta)
Cryptocurrency as money
Cryptocurrency as commodity
Cryptocurrency as property
Week 13: Cryptocurrency Taxation (US, UK, EU, Malta)
General taxation practices
Taxation of Natural Persons
Taxation of Legal Entities
How to determine what you owe
Week 14: Guest Lecture on Regulation
Home assignment: DLT use-case idea research and pitch
Week 15: Home assignment review seminar
Final project: write a white-paper or business plan for a new DLT project or automation of a business process within an existing business.
Week 16: Final project review seminar
Distributed Ledger Technology in Finance Certificate (DLT) Examination
The Artificial Intelligence Finance Institute’s (AIFI) mission is to be the world’s leading educator in the application of artificial intelligence to investment management, capital markets and risk. We offer one of the industry’s most comprehensive and in-depth educational programs, geared towards investment professionals seeking to understand and implement cutting edge AI techniques.
Taught by a diverse staff of world leading academics and practitioners, the AIFI courses teach both the theory and practical implementation of artificial intelligence and machine learning tools in investment management. As part of the program, students will learn the mathematical and statistical theories behind modern quantitative artificial intelligence modeling. Our goal is to train investment professionals in how to use the new wave of computer driven tools and techniques that are rapidly transforming investment management, risk management and capital markets.
Artificial Intelligence and quantitative investment techniques are disrupting investment management in unprecedented ways. We are seeing the evidence all around us. In the hedge fund industry, 8 of the top 10 largest managers are now quantitatively focused. More and more fundamental managers are announcing their adoption of quantitative and AI techniques.
Investment professionals who know how to implement AI tools are in demand, yet there is a gap in training and education for those wanting to enter the field. Though it is not hard to find general AI classes and non-technical finance classes, it is not easy to find these two disciplines successfully integrated. Moreover, AI in investment management is different and more complex than AI applied to non-investment disciplines, and the tools are rapidly evolving.
By assembling a faculty comprised of the rare breed of academic thought leaders and practitioners of AI in finance, supplement with other global thought leaders in AI, AIFI seeks to help students use these cutting edge tools effectively in their investment careers. The certificate will demonstrate a world class education and competency in what will be the future of investment management that will be revered by investors and asset managers.
Artificial Intelligence in Investment Management Certificate
Lectures: New York City. March – June 2019
Days: Tuesday and Thursday. 06.00 pm – 09.00 pm
75 Hours: Lectures + Practice + Speakers
Evaluation: Projects + Final Exam
An optional Python and (relevant) mathematics refresher
will be provided at the beginning of the course.