AI and Fintech in the Capital Markets

Price: $995   Register now

Recommended that you register at least two weeks in advance.

Course Features

  • Course date:12/18/2019
  • Course Duration: 1 Day
  • Level: Intermediate
  • Prerequisites: None
  • Method: Live & Virtual
  • Venue: MicroTek
  • Time: 9:00 am - 5:00 pm
  • Dress Code: Business Casual

Machine Learning, AI, & Fintech in the Capital Markets

The capital markets have changed forever, the machines are replacing decision making, order flow, risk management, valuation, and much more. Explore these changes and be prepared to add value to the transformation of the capital markets.

The AI impact on capital markets has never been profound as it is in the present times. AI has certainly taken the finance world, especially banking and investment services by storm. AI is a suite that comprises of a set of tools – like machine learning, natural language processing, deep neural networks, etc. that are impacting almost every industry, in the most efficient way. At its core, AI is essentially a set of technologies that are meant to augment or perform human tasks, without their intervention. Over a few decades, these technologies have evolved to sense, learn, comprehend, and act. Such a progression now enables systems and software to acquire, identify, recognize, and an analytical database (both structured and unstructured), derive insights, envision the process, and then put them into the real-time use cases. In context to the capital market, AI is enabling the machines to do algorithmic trading, qualitative analysis, automate trade execution, and manage risk. What turns AI set a disruption in almost every industry is its decision-making ability, which is based on cognitive learning. In contrast to perpetuating upon the programmed responses, AI overcomes the limitations, complexities, and challenges by teaching the system to learn through past experiences.

Despite being the hot buzzword on Wall Street these days, machine learning is still fairly misunderstood. It is not artificial intelligence itself, but rather a form of it in which computers fed extremely large data sets are able to learn as changes in that data occur without being explicitly programmed to do so. The data is just one part of the approach, what can be more challenging is making machine learning and data science a core capability among companies so that they instinctively take internal and external data sets and interpret it for patterns, risks, and opportunities.  Machine learning is shifting your trading counterparty to engineers and quants, it is critical you understand this evolution.

 

Who should attend

  • CFO, CCO, CIO, Audit, Trader, or Analyst at Hedge Funds
  • Strategy, Strategy Implementation, Risk, Sales & Trading, Prime Brokers, Program Leaders, COO, CRO, Product: Fixed Income, FX, Equities, Collateral, Treasury from Sell-Side Banks
  • Associate to Senior Vice President in Corporate Finance
  • Consultants, Technologists, Research Project Managers
  • Sales and Global Account Management Directors
  • Data Scientist, Machine Learning Specialists
  • Business Development and Product professionals at Market Utilities
  • SVP to Head of Marketing
  • Director of Operations
  • Project, Enterprise Change, and Release Managers

 

How you will benefit

  • Gain a profound understanding of what lies ahead of you in the rapidly changing Capital Markets driven by Machine learning.
  • Understand other new technology that is impacting the markets and what FinTech offering is next to impact the market.
  • Explore relevant risk factors of Machine learning that keep market participants up at night. Understand how to diagnose these risk factors in a volatile market.
  • Participants will be better equipped to understand the unique dynamics of the markets. How various factors push and pull affecting other areas of the market. How automation, big data, machine learning, regulation, collateral management, and high-frequency trading have an impact on market conditions.
  • Participants will address Big Data, Machine Learning, Automation, FinTech solutions, Trading workflow, and Market structure.

 

Challenge #1: I don’t understand what is driving Machine Learning in the market.

Participants will come away from this class understanding not only market structure but the factors that are affecting technology in the market and what to expect in the future in terms of Machine Learning, Big Data, and AI affecting our ecosystem.

Challenge #2: I don’t understand the new technology such as Big Data, blockchain, HFT, or cryptocurrency.

After attending our course participants will have a deep and profound understanding of the technology behind the cryptocurrency’s blockchain, applications, as well as the current dynamics of the cryptocurrency trading environment. Participants will also learn what to expect in the future with this revolutionary technology.

Challenge #3: What is happening with the future of regulation? How has the market changed? Has it affected the technology we use?

Participants will not only understand market structure conditions but they will also understand market risk conditions that will affect the market going forward. Participants will have a profound understanding of the future of regulation, what it means to them in their business, and what to expect in terms of market conditions because of these changes.

Module 1

Modern Market structure looking beyond 2020: The rise of alternative technology, marketplaces, and products such as exchange traded derivatives, and cryptocurrencies.

  • Exchanges, Clearinghouses, and Collateral
  • Exchange-traded & OTC derivative landscape
  • Big Data, AI, and machine learning in trading, finance, and operations

Module 2

HFT, Connectivity, & AI in Trading- Have we hit a wall? How competitors have reached critical mass

  • Combating HFT? IEX launches HFT proof exchange, reviewing the offering and why it works and why it doesn’t matter anymore.
  • Case Study: No more traders? How market leader JPM is automating almost their entire worldwide trading business – eventually
  • Case Study: Hedge Fund Renaissance & Artificial Intelligence greatest success story in the Markets – How Renaissance’s Medallion Fund Became Finance’s Blackest Box

Module 3

Big Data in the financial eco-system: Financial modeling, data governance, integration, NoSQL, batch and real-time computing and storage

  • Infrastructure and technology
  • New data sources
  • Modern data analysis: Structured / Unstructured data and new models

Module 4

Machine Learning Models: what is your best-fit use in your business?

  • Supervised learning, Unsupervised learning models, Reinforcement learning, Deep learning
  • Machine learning in analysis: Momentum and Mean Reversion, Sentiment Analysis, Asymmetric Trading Strategies
  • Machines at war (trading): Non-Linear Multi-Factor Models, High-Frequency Trading, Advanced Machine Learning

Module 5

Machine learning in finance – Opportunities and challenges

  • Algo-Grading 101, Interpretation
  • Data mining biases: overfitting, survivorship and data-snooping
  • Robust trading strategies: The future of machine learning in finance

Module 6

Crypto-currency evolution, use cases, exploring hype vs. sustainability

  • Crypto-currency trading, and the regulatory response
  • Ethereum and SMART contracts: The winning model for settlement in the future.
  • Case Study: Silk Road & Dread Pirate- the complete story- and how the illegal marketplace changed market and trading history

Module 7

Exchanges, Clearinghouses, Market Share & Industry battles, and future winners

  • Case Study: Quantile Technologies Ltd & compression. Reducing risk in the $600 trillion derivatives market.
  • Case study: OpenDoor – tackling the illiquidity problem with bond trading
  • Case Study: BitMEX, a cryptocurrency exchange changing market structure forever.

Module 8

Blockchain and other game-changing Fintech offerings

  • Blockchain live: ASX Ltd. processing equity transactions powered by Blockchain & Digital Asset Holdings
  • Your sandbox or mine? A review of current offerings and initiatives from Axoni, Digital Asset Holdings, Symbiont, R3 and Chain

Case Study: AirSwap- The worlds first decentralized exchange utilizing Blockchain technology

Instructor

  • Sol Steinberg is an  OTC Markets Subject Matter Expert and specializes in Risk Management, OTC derivatives, Market structure, Collateral, Trade Lifecycle, Valuation, Financial Technology Systems, Strategic Development, and Monetization.   Sol is the founding principle of his firm, OTC partners. OTC partners is a boutique value add firm that specializes in research, content, development.  Before …
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