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Welcome to The Machine Learning Institute Certificate in Finance (MLI) Start Date: Tuesday 23rd April 2019

Quantitative finance is moving into a new era. Traditional quant skills are no longer adequate to deal with the latest challenges in finance. The Machine Learning Institute Certificate offers candidates the chance to upgrade their skill set by combining academic rigour with practical industry insight.

The Machine Learning Institute Certificate in Finance (MLI) is a comprehensive six-month part-time course, with weekly live lectures in London or globally online. The MLI is comprised of 2 levels, 6 modules, 24 lecture weeks, lab assignments, a practical final project and a final sit down 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 machine learning in finance. Throughout our unique MLI programme, candidates work with hands-on assignments designed to illustrate the algorithms studied and to experience first-hand the practical challenges involved in the design and successful implementation of machine learning models. The MLI is a career-enhancing professional qualification, that can be taken worldwide.

Speakers

WELCOME TO THE MACHINE LEARNING INSTITUTE CERTIFICATE IN FINANCE (MLI)

WELCOME TO THE MACHINE LEARNING INSTITUTE CERTIFICATE IN FINANCE (MLI)

Welcome to The Machine Learning Institute Certificate in Finance (MLI)

Prior to registration you mustApply Online via the MLI Certificate website. Fill in the application form and we will then contact you for the next step.

Quantitative finance is moving into a new era. Traditional quant skills are no longer adequate to deal with the latest challenges. The Machine Learning Institute Certificate offers candidates the chance to upgrade their skill set by combining academic rigour with practical industry insight.

The Machine Learning Institute Certificate in Finance (MLI) is a comprehensive six-month part-time course, with weekly live lectures in London or globally online. The MLI comprises of 2 levels, 6 modules, 24 lecture weeks, lab 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 machine learning quantitative finance. Throughout our unique MLI programme, candidates work with hands-on assignments designed to illustrate the algorithms studied and to experience first-hand the practical challenges involved in the design and successful implementation of machine learning models. The MLI is a career-enhancing professional qualification, that can be taken worldwide.

Next Cohort Starts: Tuesday 23rd April 2019

  • SUPER EARLY BIRD DISCOUNT: 25% Discount until 22nd February 2019*
  • VOLUME DISCOUNT: If 2 or more people from your institution wish to take The MLI Certificate please contact us
  • REGIONAL OFFERS: Get in touch for offers in your geographic region

*Not to be used in conjunction with other offers

Visit the MLI Certificate Website

MLI LEVELS 1 & 2:

  • Primer in Mathematical Methods
  • Primer in Python Programming for Machine Learning:
  • 24 Lecture Weeks
  • Six Modules
  • FINAL PROJECT
  • FINAL EXAMINATION

MLI LEVEL 1: 

  • Primer in Mathematical Methods
  • Primer in Python Programming for Machine Learning:
  • Module 1 – Supervised Learning
  • Module 2 – Unsupervised Learning:
  • Module 3 – Practitioners Approach to ML:
  • Level 1: LAB ASSIGNMENTS

MLI LEVEL 2: 

  • Module 4 – Neural Networks:
  • Module 5 – Deep Learning:
  • Module 6 – Advanced Topics:
  • Level 2: LAB ASSIGNMENTS
  • FINAL PROJECT
  • FINAL EXAMINATION

Please note that candidates must pass MLI Levels 1 and 2 to be become fully MLI certified.

Level 1: Machine Learning Institute Certificate in Finance


Dates:

  • Level 1 Starts: Tuesday 23rd April 2019

Python Primer: Python for Data Science and Artificial Intelligence

Date: Tuesday 9th April 2019

Live and Online: 09.00 – 17.00 GMT

Overview

Python is the de factolingua franca of data science, machine learning, and artificial intelligence. Familiarity with Python is a must for modern data scientists.

Your course is designed to take you from the very foundations to state-of-the-art use of modern Python libraries.

You will learn the fundamentals of the Python programming language, play with Jupyter notebooks, proceed to advanced Python language features, learn to use distributed task queues (Celery), learn to work with data using NumPy, SciPy, Matplotlib, and Pandas, examine state-of-the-art machine learning libraries (Scikit-Learn, Keras, TensorFlow, and Theano), and complete a realistic, real-life data science lab.


Syllabus:

  • The fundamentals of the Python programming language and Jupyter notebooks
    • Jupyter notebooks
    • The Python syntax
    • Data types, duck typing
    • Data structures: lists, sets, and dictionaries
    • Data types

  • Advanced Python features; distributed tasks queues with Celery
    • List comprehensions
    • Lambdas
    • Objects
    • The Global Interpreter Lock (GIL)
    • Multithreading and multiprocessing
    • Distributed task queues with Celery

  • Python libraries for working with data: NumPy, SciPy, Matplotlib, and Pandas
    • Multidimensional arrays in NumPy
    • Linear algebra and optimisation with SciPy
    • Data visualisation in Matplotlib
    • Time series data
    • Dealing with Pandas DataFrames

  • Machine Learning with Scikit-Learn; Deep Learning with Keras, TensorFlow, and Theano
    • Overview of machine learning
    • Introduction to Scikit-Learn
    • Keras and TensorFlow
    • Introduction to Theano

Level 1 Starts: Tuesday 23rd April 2019


Module 1 – Supervised Learning:

In this module, the concepts related to algorithmically learning from data are introduced. The candidates are given an early taste of a supervised machine learning application before going through the fundamental building blocks starting from linear regression and classification models to kernels and the theory underpinning support vector machines and then to the powerful techniques of ensemble learning.

The module includes a combination of theoretical and hands-on lab assignments:

Module 1 Supervised Learning Learning from Data 23-Apr-19
Module 1 Supervised Learning Linear Models 30-Apr-19
Module 1 Supervised Learning Kernel Models 7-May-19
Module 1 Supervised Learning Ensemble Learning 14-May-19

Module 2 – Unsupervised Learning:

An important and challenging type of machine learning problems in finance is learning in the absence of ‘supervision’, or without labelled examples.

In this module, we first introduce the theoretical framework of hidden variable models. This family of models is then used to explore the two important areas of dimensionality reduction and
clustering algorithms.

There are theoretical and applied lab assignments with financial data sets.

Module 2 Unsupervised Learning Introduction 21-May-19
Module 2 Unsupervised Learning Dimensionality Reduction 28-May-19
Module 2 Unsupervised Learning Clustering Algorithms 4-Jun-19
Module 2 Unsupervised Learning Applications 11-Jun-19

Module 3 – Practitioners Approach to ML:

This module focuses on the practical challenges faced when deploying machine learning models within a real life context.

Each session in this module covers a specific practical problem and provides the candidates with guidance and insight about the way to approach the various steps within the model development cycle, from data collection and examination to model testing and validation and results interpretation and communication.

Module 3 Practitioner’s Approach Problem Setup and Data Pipeline 18-Jun-19
Module 3 Practitioner’s Approach Feature Engineering 25-Jun-19
Module 3 Practitioner’s Approach Exploration, Maximum Value Hypothesis 2-Jul-19
Module 3 Practitioner’s Approach Model Tuning 9-Jul-19

LAB ASSIGNMENTS:

Throughout the programme, candidates work on hands-on assignments designed to illustrate the algorithms studied and to experience first hand the practical challenges involved in the design and successful implementation of machine learning models.

The data sets and problems are selected to be representative of the applications encountered in finance. The following are examples of the type of topics to be covered in the lab and project work:

  • Quantitative Trading Strategies
  • Market News and Sentiment Analysis
  • Algorithmic Trading
  • High Frequency Strategies
  • Outlier Detection
  • Market Risk Management
  • Credit Rating
  • Default Prediction
  • Portfolio Management (‘Robo-Advisors’)
  • Fraud Detection and Prevention

Level 2: Machine Learning Institute Certificate in Finance


Dates:

  • Level 2 Starts: Tuesday 16th July 2019
  • Examination: Tuesday 26th November 2019
  • Final Project Hand in Friday 3rd January 2020

Module 4 – Neural Networks:

Neural Network models are an important building block to many of the latest impressive machine learning applications on an industrial scale.

This module aims to develop a solid understanding of the algorithms and importantly, an appreciation for the main challenges faced in training them. The module starts with the perceptron model, introduces the key technique of backpropagation before exploring the various regularisation and optimisation routines. More advanced concepts are then covered in relation to the next module on Deep Learning.

Although we cover the theoretical foundations of Neural Networks, the emphasis of the assignments will be on hands-on lab work where the candidates are given the opportunity to experiment with the techniques studied on financial and non-financial data sets.

Module 4 Neural Networks Perceptron Model 16-Jul-19
Module 4 Neural Networks Backpropagation 23-Jul-19
Module 4 Neural Networks Regularisation and Optimisation 30-Jul-19
Module 4 Neural Networks Network Architectures 6-Aug-19

Module 5 – Deep Learning:

Deep Learning has been the driving engine behind many of the recent impressive improvements in the state of the art performance in large scale industrial machine learning applications.

This module can be viewed as a natural follow-up from the previous module on Neural Networks. First, the background and motivations for transitioning from traditional networks to deeper architectures are explored. Then the module covers the deep feedforward architecture, regularisation for deep nets, advanced optimisation strategies and the CNN Architecture.

The assignments of this module will be highly practical with ample opportunity to experiment on financial and non-financial data sets and become familiar with the latest open-source deep learning frameworks and tools.

Module 5 Deep Learning Motivation and Examples 3-Sep-19
Module 5 Deep Learning Deep Feedforward 10-Sep-19
Module 5 Deep Learning Regularisation for Deep Nets 17-Sep-19
Module 5 Deep Learning Advanced Optimisation Strategies 24-Sep-19

Module 6 – Advanced Topics:

In this module, candidates will be exposed to a selection of some of the latest machine learning and AI topics relevant to the financial services industry.

Financial timeseries data presents particular challenges when it comes to applying machine learning techniques. These challenges and approaches to deal with them will be covered.

Also, building on the previous module, deep models for timeseries based on the RNN architecture and Long Short-Term Memory will be presented.

Since the lectures are delivered by industry practitioners from leading institutions, the candidates will be encouraged to use the solid technical foundations built throughout the programme to interact and confidently debate about the problems and approaches presented.

Module 6 Advanced Topics Advanced Topic 1 1-Oct-19
Module 6 Advanced Topics Advanced Topic 2 8-Oct-19
Module 6 Advanced Topics Advanced Topic 3 15-Oct-19
Module 6 Advanced Topics Advanced Topic 4 22-Oct-19

LAB ASSIGNMENTS:

Throughout the programme, candidates work on hands-on assignments designed to illustrate the algorithms studied and to experience first hand the practical challenges involved in the design and successful implementation of machine learning models.

The data sets and problems are selected to be representative of the applications encountered in finance. The following are examples of the topics to be covered in the lab and project work:

  • Quantitative Trading Strategies
  • Market News and Sentiment Analysis
  • Algorithmic Trading
  • High Frequency Strategies
  • Outlier Detection
  • Market Risk Management
  • Credit Rating
  • Default Prediction
  • Portfolio Management (‘Robo-Advisors’)
  • Fraud Detection and Prevention

FINAL EXAMINATION: 

DATE: Tuesday 26th November 2019

Candidates will sit a formal 3-hour examination on a laptop. The exam is held in London for UK students and using our global network of examination centres for overseas students. 


FINAL PROJECT:

DATE: Friday 3rd January 2020

At the end of the programme, candidates apply the theoretical and practical skills acquired to a real world application within the financial services industry.

The assessment will take into account the quality and the originality of the work as well as the clarity of its presentation.

The 2nd Machine Learning & AI in Quantitative Finance Conference USA: New York City 14th - 16th November

The 2nd Machine Learning & AI in Quantitative Finance Conference USA: New York City 14th – 16th November

SPECIAL OFFER When two colleagues attend the 3rd goes free!

20% Early Bird Discount until Friday October 5th 2018

BROCHURE


Pre-Conference Workshop day: Wednesday November 14th 2018

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: 09.00 – 17.30

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.


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

Location

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


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.

Main Conference Day 1:

 

08.30 – 09.00: Registration and Morning Welcome Coffee


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

MARCELO LABRE: Executive Director, Morgan Stanley


10.00 – 10.45: Deep Learning and Computational Graph Techniques for Derivatives Pricing and Analytics

We review some new approaches from research and literature and Wells Fargo’s work to apply deep learning techniques and computational graph techniques (including algorithmic differentiation) to the solution of high-dimensional forward-backward SDE and PDE in derivative pricing, present some fundamental ideas, applications to derivatives pricing and analytics with some results, and some current and planned extensions

BERNHARD HIENTZSCH: Director, Head of Model, Library, and Tools Development for Corporate Model Risk, Wells Fargo


10.45 – 11.15: Morning Break and Networking Opportunities

11.15 – 12.00: Deep 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

MIQUEL NOGUER ALONSO: Adjunct Assistant Professor, COLUMBIA UNIVERSITY


12.00 – 12.45: 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. 

IGOR HALPERIN: Research Professor of Financial Machine Learning, NYU Tandon School of Engineering


12.45 – 13.45: Lunch


13.45 – 14.30: Machine Learning Models for Corporate Bond Default, Recovery in Default, and Relative Value

 

TERRY BENZSCHAWEL: Founder and CEO, Lambda Financial, LLC.


14.30 – 15.15: Using Natural Language Processing (NLP) to Analyze Earning Call Transcript

 

ARIK BEN DOR: Managing Director and Head of Quantitative Equity Research, Barclays


15.15 – 15.45: Afternoon Break and Networking Opportunities

15.45 – 16.30: Applying Machine Learning to Evaluate Systemic Risk and Contribution of Individual SIFIs

 

 KSENIA SHNYRA: Senior Advisor, Deloitte


16.30 – 17.30: Machine Learning & Ai in Quantitative Finance Panel

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.
  • Discuss quantum computing in quant finance:
    • Breakthroughs
    • Applications
    • Future uses

Panellists:

  • MARCELO LABRE: Executive Director, Morgan Stanley
  • BERNHARD HIENTZSCH: Director, Head of Model, Library, and Tools Development for Corporate Model Risk, Wells Fargo
  • ARIK BEN DOR: Managing Director and Head of Quantitative Equity Research, Barclays
  • IGOR HALPERIN: Research Professor of Financial Machine Learning, NYU Tandon School of Engineering
  • MIQUEL NOGUER ALONSO: Adjunct Assistant Professor, COLUMBIA UNIVERSITY
  • TERRY BENZSCHAWEL: Founder and CEO, Lambda Financial, LLC.
  • JOSEPH SIMONIAN: Director of Quantitative Research, Portfolio Research & Consulting Group, Natixis Investment Managers

08.30 – 09.00 Morning Welcome Coffee

09.00 – 09.45: Keynote Speech “AI-driven ESG / SDG Strategies for Investment and Risk Management” 

The key discussion points will include:

  • Using AI to quantify unstructured data on ESG/SDG factors and associated non-financial risks
  • The use of Natural Language Processing and ESG/SDG taxonomies to quantify textual data in multiple languages
  • Ranking and benchmarking stocks based on ESG/SDG factors to implement Thematic, Long-Short and ESG/SDG-Tilted investment strategies
  • The relevance of SDG and non-financial risk factors for Alpha Research, Fiduciary Duty, Materiality Assessments, Country Risk and Risk Management

RICHARD V. ROTHENBERG: Global AI Corporation & Research Affiliate, Lawrence Berkeley National Laboratory


09.45 – 10.30: How Data Science is Impacting Multi-Asset Investing
  • How Data Science is Impacting Multi-Asset Risk Measurement
  • How Data Science is Impacting Multi-Asset Trading Strategies
  • How Data Science is Impacting Multi-Asset Model Portfolio Programs

JOSEPH SIMONIAN: Director of Quantitative Research, Portfolio Research & Consulting Group, Natixis Investment Managers


10.30 – 11.00: Morning Break and Networking Opportunities


11.00 – 11.45: 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.

GORDON RITTER: Senior Portfolio Manager, GSA Capital


11.45 – 12.30: Opportunities and Challenges of Machine Learning in Quantitative Investment and Wealth Management

I describe specific opportunities and challenges of leveraging machine learning within context of quantitative investment and wealth management. Such applications include forecasting of financial time series, classification of alternative data, identification of market regimes, clustering-based portfolio diversification, assessment of risk factors.

CRISTIAN HOMESCU: Director, Portfolio Analytics Bank of America Merrill Lynch


12.30 – 13.30: Lunch


13.30 – 14.00: Using Machine Learning to Forecast Realized Volatility

 

PETER CARR: Professor and Dept. Chair of FRE Tandon, New York University


14.00 – 14.45: “Risks and Regulatory Framework around using AI Models”

 

AMIT SRIVASTAV:Executive Director, Quantitative Analytics Group (Model Risk), Morgan Stanley


14.45 – 15.00: Afternoon Break


15.00 – 15.45: 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?

MICHAEL BEAL: CEO, Data Capital Management


15.45 – 16.30: Factor Investing Using Volatility Data & Machine Learning
  • Equity factors
  • Volatility surface
  • Style investing

SHENGQUAN ZHOU:Quantitative Researcher, Bloomberg LP

Sponsors

There are limited sponsorship opportunities for this event so please contact neil@wbstraining.com
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