Quant Insights
AI, Machine Learning and Risk
London & Online, 16th November 2018
4th Annual Conference
Brought to you by
CQF Institute and Wilmott
For Python Quants Bootcamp Series
London & Online, 13th - 15th November

2 Organizers

10 Talks

120 Live Tickets

200+ Online Tickets

About the Conference

Canary Wharf, London, E14 5GN

With the growth and development of artificial intelligence, the use of data and machine learning in finance has become a hot topic in the last few years. The Quant Insights conference will bring together leading industry practitioners to talk about the latest techniques they are using, incorporating AI and machine learning in trading, portfolio management and risk management.

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Conference Speakers & Talks

Paul Wilmott
Keynote

Dr. Paul Wilmott, President, CQF Institute
Dr. Paul Wilmott, President, CQF Institute

Paul is the founder of the Certificate in Quantitative Finance and Wilmott.com and he is internationally renowned as a leading expert on quantitative finance. His research work is extensive, with more than 100 articles in leading mathematical and finance journals, as well as several internationally acclaimed books on mathematical modeling and derivatives, including the best-selling Paul Wilmott On Quantitative Finance, published by John Wiley & Sons.

Miquel Noguer Alonso
Deep Learning in Finance: Prediction of Stock Return with Long Short-Term Memory Networks

Dr. Miquel Noguer Alonso, Chief Development Officer, GLOBAL AI
Dr. Miquel Noguer Alonso, Chief Development Officer, GLOBAL AI

Miquel is a financial markets practitioner with more than 20 years of experience in asset management. He is currently Chief Development Officer for Global AI a Big Data Company that uses State-of-the-Art Statistical and Artificial Intelligence models to produce actionable insights, signals and alternative data for institutional clients, including Investors, Governments and Corporations. He worked for UBS AG (Switzerland) 10 years as Executive Director. He acted a member of European Investment Committee. He worked as a Chief Investment Office and CIO for Andbank from 2000 to 2006. He started his career at KPMG.

Tony Guida
Machine Learning for Multi-Factor Equity Portfolios

Tony Guida, Senior Investment Manager - Quantitative PM
Tony Guida, Senior Investment Manager - Quantitative PM

Tony is Senior Investment Manager - Quantitative PM, managing multi-factor equity portfolios for the asset manager of a UK pension fund in London. Prior to that Tony was Senior Research Consultant for Smart Beta and Risk allocation at EDHEC RISK Scientific Beta, advising asset owners how to construct and allocate to risk premia. Before joining EDHEC Tony worked eight years at UNIGESTION as a Senior Research Analyst. He was a member of the Research and Investment Committee for Minimum Variance Strategies and he was leading Factor Investing research group for institutional clients. He is the editor and co-author of the forthcoming book: Practical Applications of Machine learning and Big Data for Quantitative Investment (Winter 2018). He holds Bachelor and Master degrees in Econometry and Finance from the University of Savoy, France.

Yves Hilpisch
The AI Machine – Solving the Last Mile Problem in Algorithmic Trading

Dr. Yves Hilpisch, Founder & Managing Partner, The Python Quants
The AI Machine - Solving the Last Mile Problem in Algorithmic Trading

This talk considers the consequences of recent advances in the field of Artificial Intelligence (AI) for finance in general and algorithmic trading in particular. The core of the talk is about building The AI Machine, an AI-powered, scalable algorithmic trading platform mainly using Python. It allows (retail) algorithmic traders to deploy their trading algorithms in robust and reliable fashion, taking care of data processing, signal generation, order execution, risk management and trading strategy life cycle management. The AI Machine solves the last mile problem for both algorithmic traders on the one hand and brokers on the other hand when it comes to deploying trading algorithms in a standardized way.

Dr. Yves Hilpisch, Founder & Managing Partner, The Python Quants

Yves is founder and Managing Partner of The Python Quants, a group focusing on the use of open source technologies for financial data science, artificial intelligence, algorithmic trading and computational finance. He is author of the books Python for Finance (O'Reilly, 2nd edition, 2018), Listed Volatility and Variance Derivatives (Wiley, 2017), Derivatives Analytics with Python (Wiley, 2015) and Python for Finance (O'Reilly, 2014). Yves lectures on computational finance at the CQF Program and on algorithmic trading at the EPAT Program. He is also the director of the first online training program leading to a University Certificate in Python for Algorithmic Trading. Yves has written the financial analytics library DX Analytics and organizes meetups, conferences and bootcamps about python for quantitative finance.

Karolos Korkas
From Machines to Robots: The Case of High-Dimensional Stress Tests in Risk and Algorithmic Trading
From Machines to Robots: The Case of High-Dimensional Stress Tests in Risk and Algorithmic Trading

Banks and financial institutions continuously face scrutiny by regulators over the development of efficient models to forecast capital sufficiency under adverse macroeconomic scenarios or determine an algorithm performance under extremely volatile markets in a high frequency trading environment. The curse of dimensionality is naturally embedded in these models and dealing with it requires taking a step further away from the traditional statistical methodologies. In this talk, I will show how to detect a period of high stress from a large dataset using machine learning techniques and discuss the prospects of AI in this context.

Dr. Karolos Korkas, Risk Quant, Citigroup Inc
Dr. Karolos Korkas, Risk Quant, Citigroup Inc

Karolos is a vice president in the Model Risk Management division of Citigroup overseeing model validations for various asset classes. Previously, he worked on the FX electronic trading desk of MUFG Bank as a quantitative researcher. There, he developed algorithms using statistical and machine learning techniques to extract meaningful patterns inside the big data produced in a high frequency trading environment. Karolos holds a PhD in Statistics from the London School of Economics and was a Fellow in Finance at the same school; he is also a published author and his research interests lie in the detection of change-points in big datasets and high dimensional risk management.

Nick Firoozye
Optimizing Dynamic Trading Strategies on Gaussian Returns
Optimizing Dynamic Trading Strategies on Gaussian Returns

Dynamic trading strategies, in the spirit of trend-following or mean-reversion, represent an only partly understood but lucrative and pervasive area of modern finance. Assuming Gaussian returns, we are able to derive closed-form expressions for the first four moments of the strategy’s returns, in terms of correlations between the random signals and unknown future returns. We demonstrate that positive skewness and excess kurtosis are essential components of all positive Sharpe dynamic strategies, which is well-known empirically; that orthogonal or total least squares (TLS) is more appropriate than OLS for maximizing the Sharpe ratio; derive standard errors on Sharpe ratios which are tighter than the commonly used standard errors from [Lo(2002)]; and derive standard errors on skewness and kurtosis of strategies, apparently new results. We introduce an over-fitting penalization on Sharpe ratios which is meant to be a better predictor of out-of-sample performance, contrasting this to other over-fitting techniques such as multiple-hypothesis testing, and apply this technique to model selection on the returns of over 1500 assets, showing that TLS together with the covariance penalty largely outperforms other methods with commonly used AIC for model choice and in terms of decreased over-optimism. Finally, we extend the work to optimize the utility of returns using nonlinear transforms of signals, considering as well standard errors of the resulting strategy design.

Dr. Nick Firoozye, Honorary Sr Lecturer, UCL and Head of Quant Strategy, Symmetry Investments
Dr. Nick Firoozye, Honorary Sr Lecturer, UCL and Head of Quant Strategy, Symmetry Investments

Nick is a mathematician & statistician with over 20 years of experience in the finance industry, in both buy and sell-side firms, largely in research. He started his career in Lehman doing MBS/ABS modeling, heading teams in portfolio strategy and EM quant, later taking a variety of senior roles at Goldman Sachs, and Deutsche Bank, and at the asset managers, Sanford Bernstein, and Citadel, in areas ranging from quantitative strategy, relative value strategy and trading, to fixed income asset allocation. He was previously MD and Head of Global Derivative Strategy, part of the QIS team at Nomura, and currently heads Quantitative Strategy at a hedge fund, Symmetry. He is also an Honorary Senior Lecturer in C.S. at University College London, focusing on Robust Machine Learning in finance. He recently co-authored a book, entitled Managing Uncertainty, Mitigating Risk, about the role of uncertainty and imprecise probability in financial crises, and he is writing a book on Algorithmic Trading Strategies based on his recent PhD and MSc courses on the same topic offered at UCL.

Conference Tickets

All tickets include 60 days of access to Video on Demand.

16th November

£245 Early Bird
£295 Standard

Live and Online

Book Now
Early Bird tickets are available until 21st October

Prices above exclude VAT of 20%

Discounts

Become a CQF Institute member

CQF Institute Premium Member: 50%
CQF Institute Basic members: 20%

Students: 50%
Bulk (3+ Tickets): 25%

To claim your discount code, email us at events@cqfinstitute.org

For Python Quants Bootcamp Series

Brought to you by the CQF Institute and The Python Quants

All tickets include 60 days of access to Video on Demand.

Yves Hilpisch
Dr. Yves Hilpisch, Managing Partner of The Python Quants Inc
13th - 15th November 2018, Live and Online
Fitch Learning, 55 Mark Lane, London, EC3R 7NE
Early Bird tickets are available until 21st October

Become a CQF Institute member

CQF Institute Premium Member: 30% Discount
CQF Institute Basic members: 10% Discount

Students: 25% Discount
Package (2+ Bootcamps): 15% Discount

To claim your discount code or package bookings, email us at events@cqfinstitute.org

Prices above exclude VAT of 20%

Conference Organizers

Quant Insights is presented by the CQF Institute & Wilmott
CQF Instiute

Part of Fitch Learning, the CQF Institute is a global membership platform for educating and building the quant finance community.

Fitch Learning

Part of the Fitch Group, Fitch Learning partners with clients to enhance knowledge, skills and conduct. Fitch Learning advises and builds learning solutions to accelerate the achievements of individuals and companies.

Wilmott

Wilmott is the leading resource for the quant finance community, comprised a website and discussion forum and Wilmott magazine.

Conference Sponsors

Silver Sponsor
QuantHouse

Venues

Conference

Fitch Ratings Auditorium,
30 North Colonnade,
Canary Wharf,
London,
E14 5GN

Bootcamp Series

Fitch Learning,
The Corn Exchange,
55 Mark Lane,
London,
EC3R 7NE