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Topics:

Topics:

Machine Learning, AI, & FinTech in the Capital Markets Course
New York City: May 24th & 25th 2018

Financial Markets 2018: Rise of the Machines, the Next Frontier of Finance Course
New York City: June 7th & 8th 2018

Machine Learning & AI in Quantitative Finance Conference
New York City: June 27th, 28th & 29th 2018

Speakers

New York City: Machine Learning & AI in Quantitative Finance Conference, June 27th, 28th & 29th 2018 SPECIAL OFFER: 25% Super Early Bird Discount Until Friday May 18th 2018. When 2 colleagues attend the 3rd goes free!

New York City: Machine Learning & AI in Quantitative Finance Conference

Workshop Day: Wednesday June 27th

Main Conference + Workshop ($250 Discount)

Machine Learning, AI, & FinTech in the Capital Markets by Sol Steinberg

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 in the transformation of the capital markets.

The Course:

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. Artificial Intelligence 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 enable 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 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 up on the programmed responses, AI overcome 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.

Location

Downtown Conference Center
157 William Street
New York, NY 10038
USA
Tel: +1 212 618 6990
www.downtownmeetings.com


Course Modules

Module 1 

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

  • Exchanges, Clearing houses, 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 modelling, 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

How you will benefit:  

1. Gain a profound understanding of what lies ahead of you in the rapidly changing Capital Markets driven by Machine learning. 

2. Understand other new technology that is impacting the markets and what FinTech offering is next to impact the market. 

3. 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. 

4. Participants will be better equipped to understand the unique dynamics of the markets. How various factors push and pull, effecting other areas of the market. How automation, big data, machine learning, regulation, collateral management, and high-frequency trading have an impact on market conditions. 

5. Participants will address Big Data, Machine Learning, Automation, FinTech solutions, Trading workflow, and Market structure. 

Delegate challenges/Your solutions: 

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 A.I. affecting our ecosystem.

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

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

Challenge #3: “I don’t have a clue on what is happening with the future of regulation, how has the market changed? Has it effected 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.


Your Expert Trainers and Provider

Sol Steinberg is a 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 starting OTC Partners Sol was a senior executive at the world’s leading clearing house LCH.Clearnet.  Sol also spent nine years on the buy side and Citi, performing product development, risk management, and valuation for the OTC markets.

Sol has a wide-ranging network of asset managers, analytic providers, execution venues, regulatory, and government contacts.  He used his eco system to successfully commercialize analytics, data, and other non commercialized intellectual property and had significant monetization success. He brought to market several initiatives, including institutional and commercial risk engines such as SMART tool, Risk Explorer, Global Market Risk System for Citi: the largest VaR engine in the world from 2004 to 2006, as well as developing CCP2 – a derivative education & certification program for leading consultancies. Sol also contributed to OTC industry’s clearing and default management policies for the cleared OTC swap markets as well as contributed to industry standard risk analytics in times of low market rates.

Awards/Honors

Waters Magazine’s award “Best risk analytics initiative 2012” &  “Best risk analytics initiative  (Sell Side) 2013” 

FTF’s award for “Most cutting edge risk contribution 2013” for developing the SMART risk analytics tool.

Global nominee in 2012 for “Best Practices in Global Financial Risk Management” from PRMIA, Professional Risk Managers International Association.

BOOK NOW

Thursday June 28th 2018: Main Conference

08:30: Registration and Morning Welcome Coffee

Chair:
To be confirmed


09.00 – 10.00:  Keynote Speech: Machine Learning and AI in Finance: Applications, Cases and Research

  • Machine learning and deep learning applications in quantitative finance and risk management
  • Practitioners’ case studies
  • Research and development in deep learning

Presenter: Marcelo Labre: Executive Director, Morgan Stanley


09.45 – 10.30Deep Learning in Finance – LSTN’s 

  • Modern Data Analysis
  • Times Series Models Univariate
  • Linear Factor Models
  • Multivariate Time Series
  • Modern Financial Engineering
  • Long Short Term Memory Networks
    • Results
    • Conclusions 

Presenter: Miquel Noguer Alonso: Adjunct Assistant Professor, Columbia University 


10.30 – 11.00: Morning Break and Networking Opportunities


11.00 – 11.45: Model-free Option Pricing and Hedging by Reinforcement Learning

In discrete time, option hedging and pricing amount to sequential risk minimization. In particular, a discrete-time version of the Black-Scholes-Merton (BSM) option pricing model can be formulated as a problem of dynamic Markowitz optimization of an option replicating (hedge) portfolio made of an underlying stock and cash. This talk shows how this problem can be approached using Reinforcement Learning (RL). Once the problem is posed as an RL problem, option pricing and hedging can be done without any model for the underlying stock dynamics, using instead model-free, data-driven RL methods such as Q-learning and Fitted Q Iteration. As a result, both option price and hedge are obtained by a well-defined and converging maximization problem that uses only market prices and option trading data (inter-temporal re-hedges and hedge losses in the replicating portfolio) to find the optimal option hedge and price. The model can learn when re-hedges in data are suboptimal/noisy, or even purely random. This means, in particular, that our RL model can learn the BSM model itself, if the world is according to BSM.  

Computationally, the RL-based option pricing model is very simple, as it uses only basic linear algebra and linear regressions to compute the option price and hedge. The only tunable parameters in this approach are parameters defining the optimal hedge and price themselves. This approach does not need any model calibration (as there is no model anymore), and it automatically solves the volatility smile problem of the BSM model. We also discuss some extensions of this approach, including in particular an Inverse Reinforcement Learning setting, where inter-temporal losses from re-hedges are unobservable.        

Presenter: Igor Halperin: Research Professor of Financial Machine Learning, NYU Tandon School of Engineering


11.45 – 12.30: Machine Learning – Recent Trends and Applicability to Risk and Related Areas 

  • Supervised, unsupervised, Reinforcement
  • Deep learning, feature Learning, incremental learning
  • Predictive power and robustness 

Presenter: To be confirmed


12.30- 13.30: Lunch


13.30 – 14.15: Application of Natural Language Processing and Related Machine Learning Techniques at Large Commercial Banks

Increased digitalization of communication and recent advances in natural language processing allow us to satisfy new regulatory requirements and to advance automation in the financial industry.  But our industry has its own quirks and challenges – a unique, highly formalized parlance coupled with a lack of large sets of labeled data. We use neural nets and a variety of tools from statistical machine learning to help us solve these evolving problems.  Even more exciting, these methods can now be applied to pricing and risk management methods; fields that have largely stagnated over the last few decades, and that have not adapted to the reduced holding periods of risk by liquidity providers.  Comprehensive data policies and the ability to integrate probabilistic models on this data are preconditions for successful deployment of machine learning in capital markets.

 Presenter: Peter Decrem: Director, Citigroup


14.15 – 15.00: A Worked Example of Using Neural Networks for Time Series Prediction

Many publicly available examples of time series prediction with neural networks use fake or
random data. Other examples, particularly in finance, present poorly performing models. It is very hard to learn good practices when only presented with toy examples. Instead, this talk aims to teach the full process of using a neural network for time series prediction by walking through a real problem from start to finish.  

We will begin by explaining the concrete problem we would like to solve and how to frame our problem in a way that we can model. Once we understand our problem, we will discuss how to collect the needed data. We will discuss the process of reducing our input data into important features for the model to consume. We will then learn how to use Keras to implement our neural network. Once we have a working model, we will cover some tricks to improve its performance.

At every step, we will cover problems faced while working on this model. We will show how to use data visualization to aid in model development and catch problems early. We will also cover tips for using numpy to work with time series data efficiently.  

By the end of the talk, audience members will:  

  • Know how to frame a problem in a way that a neural network can model
  • Know how to think about feature selection
  • Be familiar with the Keras API for time series predictions
  • Understand that the hardest problems come before you even get to Keras

Presenter: To be confirmed


15.00 – 15.30: Afternoon Break and Networking Opportunities


15.30 – 16.15: Applying Machine Learning to Evaluate Systemic Risk and Contribution of Individual SIFIs

Presenter: Ksenia Shnyra: Senior Advisor, Deloitte   


16.15 – 17.15: Machine Learning & Ai in Quantitative Finance Panel

Moderator:

  • Luis Cota: Data Scientist, Thalesians

Panelists:

  • Miquel Noguer Alonso: Adjunct Assistant Professor, Columbia University
  • Igor Halperin: Research Professor of Financial Machine Learning, NYU Tandon School of Engineering 
  • Marcelo Labre: Executive Director

Topics: 

  • What is the current state of utilisation of machine learning in finance?
  • What are the distinct features of machine learning problems in finance compared to other industries?
  • What are the best practices to overcome these difficulties?
  • What’s the evolution of a team using machine learning in terms of day to day operations?
  • What is a typical front office ‘Quant’ skillset going to look like in three to five years time?
  • How do we deal with model risk in machine learning case?
  • How is machine learning expected to be regulated?
  • What applications can you list among its successes?
  • How much value is it adding over and above the “classical” techniques such as linear regression, convex optimisation, etc.?
  • Do you see high-performance computing (HPC) as a major enabler of machine learning?
  • What advances in HPC have caused the most progress?
  • What do you see as the most important machine learning techniques for the future?
  • What are the main pitfalls of using Machine Learning currently in trading strategies?
  • What new insights can Machine Learning offer into the analysis of financial time series?
  • Discuss the potential of Deep Learning in algorithmic trading?
  • Do you think machine learning and HPC will transform finance 5-10 years from now?
  • If so, how do you envisage this transformation?
  • Can you anticipate any pitfalls that we should watch out for 

BOOK NOW

Friday June 29th 2018: Main Conference

08:30: Morning Welcome Coffee

Chair:

To be confirmed


09.00 – 10.00: Keynote Speech 

Topics in Self-Learning Agents and Traditional Quantitative Models in Finance   

  • What can we draw from our experience of training and running an industry first self-learning agent for electronic order execution?
  • Will traditional hand-crafted heuristic- and quant-based execution algorithms go extinct within 10 years?
  • Does the success of ML and AI agents in finance indicate the eventual demise of traditional quantitative models?
  • Practical aspects of using feeder models and heuristics in AI agents for trading applications.
  • Do we have practical solutions for the equivalence puzzle in Neural Nets

Presenter: To be confirmed


10.00 – 10.45: Delivering Alpha: Artificial Intelligence in Capital Markers Investing 

  • Why artificial intelligence for capital markets investing?
    • Challenge 1: Data acquisition, integration, processing power
    • Challenge 2: Artificial intelligence and its subcomponents
  • Where should financial services professionals focus their effort?

Presenter: Michael Beal: CEO, Data Capital Management


10.45 – 11.15: Morning Break and Networking Opportunities


11.15 – 12.00: Trading Strategies Using a Mixture of Supervised and Reinforcement Learning 

Abstract: Machine learning is rapidly transforming the field of quantitative finance. In this talk, we discuss how two distinct subfields of machine learning, namely reinforcement learning and supervised learning, can be combined into a single model that harvests the power of reinforcement learning in handling multi-period problems with delayed rewards and costs, and simultaneously harvests the power of supervised-learning to learn the structure of a non-linear model with interactions. Our technique fuses the two within the framework of generalized policy iteration by generating training sets which are then used by the supervised learner to learn a better representation of the action-value function, which is then used to generate a better training set for the next iteration. We show that our method outperforms tabular Q-learning in a simulation involving trading a very illiquid asset, and can handle discrete as well as  continuous predictors. 

Presenter: Gordon Ritter: Senior Portfolio Manager, GSA Capital  


12.00 – 12.45Topic to be Confirmed 

Presenter: Leigh Drogen: Founder and CEO, Estimize


 12.45- 13.45: Lunch


13.45 – 14.30: Topic to be Confirmed

Presenter: Jared Broad: CEO, QuantConnect


14.30 – 15.15: Quantitative Factor Investing Strategies   

Presenter: ShengQuan Zhou: Quantitative Researcher, Bloomberg LP 


15.15 – 15.30: Afternoon Break


15.30 – 16.15: From Artificial Intelligence to Machine Learning, from Logic to Probability   

Applications of Artificial Intelligence (AI) and Machine Learning (ML) are rapidly gaining steam in quantitative finace. These terms are often used interchangeably. However, the pioneering work on AI by participants of the Dartmouth Summer Research Project — Marvin Minsky, Nathaniel Rochester, and Claude Shannon — was more symbolic than numerical, and often used the language of logic. Recent advances in ML — especially Deep Learning — are more numerical than symbolic, and often use the language of probability. In this talk we shall show how to connect these two worldviews.    

Presenter: To be Confirmed 

BOOK NOW

New York City: Machine Learning, AI, & FinTech Courses

New York City: Machine Learning, AI, & FinTech in the Capital Markets Course, May 24th & 25th 2018

New York City: Financial Markets 2018: Rise of the Machines, the Next Frontier of Finance Course, June 7th & 8th 2018

Location:

Downtown Conference Center
157 William Street
New York, NY 10038
USA
Tel: +1 212 618 6990
www.downtownmeetings.com


Your Expert Trainer 

Sol Steinberg is a 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 starting OTC Partners Sol was a senior executive at the world’s leading clearing house LCH.Clearnet.  Sol also spent nine years on the buy side and Citi, performing product development, risk management, and valuation for the OTC markets.

Sol has a wide-ranging network of asset managers, analytic providers, execution venues, regulatory, and government contacts.  He used his eco system to successfully commercialize analytics, data, and other non commercialized intellectual property and had significant monetization success. He brought to market several initiatives, including institutional and commercial risk engines such as SMART tool, Risk Explorer, Global Market Risk System for Citi: the largest VaR engine in the world from 2004 to 2006, as well as developing CCP2 – a derivative education & certification program for leading consultancies. Sol also contributed to OTC industry’s clearing and default management policies for the cleared OTC swap markets as well as contributed to industry standard risk analytics in times of low market rates.

Awards/Honors

Waters Magazine’s award “Best risk analytics initiative 2012” &  “Best risk analytics initiative  (Sell Side) 2013” 

FTF’s award for “Most cutting edge risk contribution 2013” for developing the SMART risk analytics tool.

Global nominee in 2012 for “Best Practices in Global Financial Risk Management” from PRMIA, Professional Risk Managers International Association.

New York City: Machine Learning, AI, & FinTech in the Capital Markets Course, May 24th & 25th 2018

  • 25% Super Early Bird Discount Until Friday April 27th 2018
  • When 2 colleagues attend the 3rd goes free!

Course Subtitle:

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 in the transformation of the capital markets.

The Course:

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. Artificial Intelligence 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 enable 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 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 up on the programmed responses, AI overcome 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.

This course is available In – House


How you will benefit:  

  1. Gain a profound understanding of what lies ahead of you in the rapidly changing Capital Markets driven by Machine learning. 
  1. Understand other new technology that is impacting the markets and what FinTech offering is next to impact the market. 
  1. 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. 
  1. Participants will be better equipped to understand the unique dynamics of the markets. How various factors push and pull, effecting other areas of the market. How automation, big data, machine learning, regulation, collateral management, and high-frequency trading have an impact on market conditions. 
  1. Participants will address Big Data, Machine Learning, Automation, FinTech solutions, Trading workflow, and Market structure. 

Delegate challenges/Your solutions:

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 A.I. affecting our ecosystem.

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

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

Challenge #3: “I don’t have a clue on what is happening with the future of regulation, how has the market changed? Has it effected 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.


Thursday May 24th, Workshop Agenda Day 1:

Module 1 

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

  • Exchanges, Clearing houses, 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 modelling, 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

Workshop Schedule: 09.00 – 17.30


Friday May 25th, Workshop Agenda Day 2:

Module 1

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 2

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 3 

Exchanges, Clearing houses, 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 4 

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

Workshop Schedule: 09.00 – 17.30

BOOK NOW

New York City: Financial Markets 2018: Rise of the Machines, the Next Frontier of Finance Course, June 7th & 8th 2018 

  • 25% Super Early Bird Discount Until Friday May 4th 2018
  • When 2 colleagues attend the 3rd goes free!

Course Subtitle:

Before the machines, markets were a place that humans with roughly equal information and reflexes set the prices of financial assets by buying and selling. After the machines took over, markets became dominated – in terms of volume, liquidity and pricing – by machines, artificial intelligence, and algorithms that operate in time frames of a millionth of a second. We will explore these impacts and future developments of technology in today’s & tomorrow’s capital markets.

The Course 

10 years ago, the Rise of the Machines (aka high frequency trading algorithms and artificial intelligence) completely altered the terrain of what we call the capital markets. Not only are today’s markets something the human traders of a generation ago would fail to recognize, they’re no longer a place where human actions of any sort have much of a remaining role. The digitalization of the workplace gives organizations a new set of options for getting work done. It’s time to move beyond alarmist rhetoric about market automation and consider how human-machine collaboration can deliver a higher level of productivity. The question to ask isn’t how many finance jobs will be replaced by artificial intelligence (AI) and robotics, but how work can be reconfigured in order to achieve the optimal integration of talent and machines. From liquidity improvements, to big data, to settlement technology, and blockchain. We are now in a new wave of automation, AI in the capital markets are here to stay.

This course is available In – House


How you will benefit:  

1. Gain a profound understanding of what lies ahead of you in the rapidly changing Capital Markets and Capital market structure.

2. Understand new technology that is impacting the markets and what FinTech is next to impact the market.

3. Explore relevant risk factors that keep market participants up at night. Understand how to diagnose and hedge these risk factors in a volatile market.

4. Participants will be better equipped to understand the unique dynamics of the markets. How various factors push and pull, effecting other areas of the market. How regulation, collateral management, and high-frequency trading have an impact on market conditions.

5. Participants will address the challenge of today’s market as well as what challenges are on the horizon.  

Delegate challenges/Your solutions:  

Challenge #1: “I don’t understand what is driving the market?”

Participants will come away from this class understanding not only market structure but the factors that are affecting the market and what to expect in the future in terms of market conditions, future regulation, and other important factors affecting our ecosystem.

Challenge #2: “I don’t understand the new technology such as blockchain or crypto currency.”

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

Challenge #3: “I don’t have a clue on what is happening with the future of regulation, & how is Brexit going to impact my life?”

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.


Thursday June 7th, Workshop Agenda Day 1:

Module 1

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

  • Exchanges, Clearing houses, and Collateral
  • Exchange traded & OTC derivative landscape
  • Crypto currencies dynamics, offerings, and eco system
  • Blockchain, AI, and machine learning in trading, finance, and operations

Module 2

Regulations, Donald Trump, Brexit, and Red Flag Risk Factors- Studying the impacts on the capital markets 

  • MiFID II: One of the most seismic regulatory shifts in history affecting everything from research to dark pools
  • Who will be Bitcoin’s ultimate regulator: Future state of Crypto currency trading regulation
  • The Global LEI Initiative – The LEI is part of a much bigger market structure solution
  • Deregulation: a lose-lose game that would have serious consequences for the stability of the world financial system 

Module 3

Collateral Tidal Wave: Collateral usage today, tomorrow, and impeding shortfall.

  • Account segregation history: MF Global, LSOC, CFTC, CME, and John Corzine
  • Instant Payment? Ripple & MoneyGram’s XRP digital currency
  • What can China do to help the global collateral exchange?
  • Exploring the impact and rule set for non-cleared margin regulation

Module 4

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

  • Crypto-currency trading, and the regulatory response
  • Case Study: Omega One ,leading crypto-asset agency brokerage for asset managers and institutional investors
  • 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

Workshop Schedule: 09.00 – 17.30


Friday May 25th, Workshop Agenda Day 2:

Module 1:

Exchanges, Clearing houses, 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.
  • The Future of Libor & ISDAfix: a Review of the Libor manipulation scandal and how  the rate will be assembled in the future

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.
  • Chicago Stock Exchange: The new hub for China stock trading?
  • 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: 

The future of financial crimes and prosecution, what is fair game? 

  • Lessons from the World’s largest Cryptocurrency Heists
  • Case Study: Snitching on Wall Street. Informant Double-Crossed the FBI
  • The CFTC &  DRW: Trading manipulation lawsuit &  “Banging-the-Close”
  • Is the VIX Being Gamed? VIX trading and ongoing manipulation in the volatility index market 

Module 4:

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
  • The Ethereum Blockchain & zero-knowledge proof:  Adding a level of privacy encryption
  • Case Study:  AirSwap- The worlds first decentralized exchange utilizing Blockchain technology

Workshop Schedule: 09.00 – 17.30

BOOK NOW

Sponsors

There are limited sponsorship opportunities for this event so please contact neil@wbstraining.com
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MoCaX Intelligence is a new-to-the-market algorithm that accelerates existing Risk Engines without the need for complex systems development or expensive hardware upgrades. MoCaX removes the pricing step bottle-neck that often uses over 90% of computational effort in existing engines and increases capabilities by several orders of magnitude with no loss of accuracy.

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The Thalesians are a think tank of dedicated professionals with an interest in quantitative finance, economics, mathematics, physics and computer science, not necessarily in that order.

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Over the years, financial professionals around the world have looked to Wiley and the Wiley Finance series with its wide array of bestselling books for the knowledge, insights, and techniques that are essential to success in financial markets. As the pace of change in financial markets and instruments quickens, Wiley continues to respond.

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