Background

This event is co-located with Applying Data Science, AI and ML to Industry and Commerce

AI, Machine Learning and Sentiment Analysis Applied to Finance London

Artificial Intelligence is deemed to be the main driver of the 4th Industrial Revolution. Investment in AI has grown at a phenomenal rate with companies investing $26-39bn in 2016. Adoption in 2017, however, remains low. As a result, this has spurred companies from every industry to seize the trend and innovate – from virtual assistants to cyber security to fraud detection and much more. The majority of C-level executives have identified and agree that AI will have an impact on their industry. However, only 20% of C-level executives admit they have already adopted AI technology in their businesses, according to research conducted by McKinsey. So, there is plenty of scope for change and improvement. The Finance industry is anticipated to lead the way in adoption of AI with a significant projected increase in spending over the next three years.

Until recently, practitioners have faithfully relied upon neo-classical models to measure performance, whether it’s in financial organisations or marketing corporations. AI is the new technology that offers an automated solution to these processes. It has the capability to replicate cognitive decisions made by humans and also remove behavioural bias adherent to humans.

Machine learning and sentiment analysis are specific techniques that are applied in AI. These techniques are maturing and rapidly proving their value within businesses. In order to process and understand the masses of data out there, machine learning and sentiment analysis have become essential methods that open the gateway to data analytics. To keep up with the ever-expanding datasets, it is only natural that the techniques and methods with which to analyse them must also improve and update.

This conference will help you to demystify the buzz around AI and differentiate the reality from the hype. Learn about how you can benefit from the unprecedented progress in AI technologies at this conference. Participants will be presented with real insights on how they can exploit these technological advances for themselves and their companies.

Attend this event and earn GARP/CPD credit hours.

Unicom has registered this program with GARP for Continuing Professional Development (CPD) credits. Attending this program qualifies for 14 GARP CPD credit hours. If you are a Certified Financial Risk Manager (FRM®), please record this activity in your Credit Tracker.

Topics Covered Include:

  • Fundamentals and applications of machine learning and deep learning
  • Pattern classifiers, Natural Language Processing (NLP) and AI applied to data, text, and multi-media
  • Sentiment scores combined with neo-classical models of finance
  • Financial analytics underpinned by qualitative and quantitative methods
  • Predictive and normative analysis applied to finance
  • Behavioural and cognitive science
  • The future of AI and its impact on industries


Why participate?

  • Hear from leading subject experts from UK, US, Europe and India/Hong Kong
  • Programme includes the latest state-of-the-art research, practical applications and case studies
  • Expect technical and in-depth presentations and discussions; we like to stimulate your brain cells!
  • Excellent networking opportunities throughout the days with all participants, including presenters, investors and exhibitors.

Meet your peers from around the world

Countries:

♦ Australia
♦ Austria
♦ Japan
♦ Netherlands
♦ Brazil
♦ Canada
♦ Korea
♦ UK
♦ China
♦ France
♦ Lithuania
♦ USA
♦ Germany
♦ Hong Kong
♦ Portugal
♦ Spain
♦ India
♦ Ireland
♦ Switzerland
♦ Italy

♦ Israel

Industry Sectors:

♦ Academia
♦ Data Analytics
♦ Hedge Funds
♦ Asset Management
♦ Finance & Insurance
♦ Investment & Trading
♦ Banking
♦ Financial Technology Research

Companies:

♦ AIG
♦ Alfa Algorithms
♦ Baloise
♦ Barclays
♦ BlackRock
♦ Bloomberg
♦ Brain
♦ Citi Group
♦ EDF Trading
♦ Federal University of Alfenas
♦ Folger Hill Asset Management
♦ Freemont Management SA
♦ HSBC
♦ IBM
♦ InterCom Group
♦ iRage
♦ Liaison
♦ Macquaire
♦ MarketyPsych
♦ NEUROTECHNOLOGY
♦ NN Investment Partners BV
♦ Northwestern Mutual
♦ OptiRisk Systems
♦ OTPP
♦ RavenPack
♦ RF Capital
♦ Royal Bank of Canada
♦ Rutgers University
♦ Santa Clara University
♦ Sentient Investment Management
♦ Sentifi
♦ Seojing University
♦ SMBC Aviation Capital

♦ TMX
♦ UBS
♦ University of Milano-Bicocca, Italy
♦ University of Porto
♦ USP
♦ Walnut Algorithms
♦ WU Vienna University

Call for Participation

We are inviting speakers – thought leaders, subject experts and start up entrepreneurs – to share their knowledge and enthusiasm about their work and their vision in the field of AI, Machine Learning, Sentiment Analysis and Deep Learning.

We understand that successful projects are written up as “White Papers”. Please share these with us. But projects that did not achieve their targets – “Black Papers” – are of interest to us too. They can be a very important topics of discussion / panels that you can present. Talk to us about both, we welcome your input.

Please complete the speaker’s response form and submit a proposal to present at this event.

Programme

Programme under development

  • 08:00 -

    Registration and Coffee

  • 08:45 -

    Introduction and Welcome - Professor Gautam Mitra, OptiRisk Systems/UCL (Programme Chair)

    Speakers:

    Gautam Mitra

  • Session Chairperson: Edward Fishwick, Managing Director and Global Co-Head of Risk & Quantitative
    Analysis at BlackRock -

    Speakers:

    Edward Fishwick

  • JOINT COMMON SESSION WITH AI & ML APPLIED TO INDUSTRY & COMMERCE -

  • 09:00 -

    How I survived the AI winter (& plan to survive the next one)

    James Luke, Distinguished Engineer, Public Sector, IBM

    James has been delivering Artificial Intelligence solutions that solve real problems for over 25 years. In this presentation, the presenter will dig through the hype and use real examples to explain what it takes to deliver working AI solutions.

    Speakers:

    James Luke

  • 09:30 -

    News Sentiment Everywhere!

    Peter Hafez, Head of Data Science, RavenPack

    In order to maintain an edge in the marketplace, asset managers are to a large extent turning to unstructured content for alpha creation, using NLP and text analysis techniques. In addition, more and more managers are expanding their mandate, trading global portfolios, to ensure more scalable strategies. As part of his presentation, Peter will showcase how news sentiment can be a valuable input to such process.

    Speakers:

    Peter Hafez

  • 10:15 -

    Hierarchical Natural Language Representation Using Deep Learning

    Nishant Chandra, Data Science Leader, AIG Science

    Deep learning has created a revolution in the natural language processing domain and corporations are leveraging it in various ways. The technology barrier is significantly reduced with open source technologies that are easy to configure and use. Several generic open source tools are available in machine learning, including deep learning, which can be customized for natural language processing. This presentation will help the audience to go beyond generic NLP problem solving by leveraging deep learning, customizing it for their industry. Specifically, they’ll learn that:

    ♦ Sentiment doesn’t have to be positive, negative or neutral but it can be extracted from the conversation
    ♦ Summarization doesn’t have to be entire document but only certain context
    ♦ Text classification doesn’t have to be exactly text/phrase/spelling based but can also include variation of acronym and synonym
    ♦ NLP can be applied broadly, and complex use cases can be built through intelligent iteration on simple examples.

    Speakers:

    Nishant Chandra

  • 10:45 -

    Introduction to Sponsors

  • 10:50 -

    Coffee

  • 11:15 -

    Enhanced Trading Strategy using Sentiment and Technical Indicators

    Xiang Yu, Chief Business Development Officer, and Gautam Mitra, CEO/Director, OptiRisk Systems/UCL

    We compute daily trade schedules using a time series of historical equity price data and applying the powerful mathematical concept of Stochastic Dominance. In contrast to classical mean-variance method this approach improves the tail risk as well as the upside of the return. In our recent research we have introduced and combined market sentiment indicators and technical indicators to construct enhanced RSI and momentum filters. These filters restrict the choice of asset universe for trading. Consistent performance improvement achieved in back-testing vindicates our approach.

    Speakers:

    Gautam Mitra

    Xiang Yu

  • 11:45 -

    Blowing Bubbles: Quantifying How News, Social Media and Contagion Effects Drive Speculative Manias

    Richard Peterson, CEO, MarketPsych Data

    In this talk Dr. Richard Peterson describes how media analytics are providing new insights into the origins and topping process of asset price bubbles. Examples from price bubbles including the China Composite, cryptocurrencies, housing, and many others will be explored. Recent mathematical models of bubble price action will be augmented with sentiment analysis. Attendees will leave with new models for identifying and taking advantage of speculative manias and panics.

    Speakers:

    Richard Peterson

  • 12:15 -

    Social Trading – Developing Signals from Social Sentiment

    Pierce Crosby, Director of Business Development and Revenue Strategy for StockTwits

    StockTwits is the largest independent social network setup for investors and traders to talk about investing. In addition to covering 8,300 stocks per year, the network also discusses 1,500+ alternative assets, including FX, futures, fixed income, privative companies, ETFs/indexes, and cryptoassets. With a dataset that stretches back to 2009, the network becomes a rich dataset for both quantitative investing as well as model development. In this talk, we will discuss the methodology behind developing an NLP-based social signal, as well as some of the academic studies run in parallel with this research. We will also discuss some of the ways in which it is being deployed in markets today.

    Speakers:

    Pierce Crosby

  • 12:45 -

    Lunch

  • JOINT COMMON SESSION WITH AI & ML APPLIED TO INDUSTRY & COMMERCE -

  • 13:45 -

    Big is beautiful: How data from email receipts can help predict company sales

    Jakub Kolodziej, Quantitative Research Senior Associate Analyst, Macquarie Research

    Macquarie analyse a large dataset of email receipts that covers the purchases of more than two million US customers. The data, sourced from QUANDL, contains weekly information on all the items purchased by each individual consumer from a large set of companies including Amazon, Walmart and Apple. In particular, for each product Macquarie gives a description, its likely classification in terms of broad goods categories, price paid, number of units, shipping costs, any discounts received and many more fields. Consumers opt in to share information available from their email accounts with a data vendor. The data is anonymised but each consumer is assigned a unique identifier which allows them to follow individual purchase histories over time and infer a profile.Using Amazon.com as a case study, they show that the data can generate real-time forecasts of quarterly sales that are at least as accurate as consensus. It is, however, in combining analyst insights and big data that they find the most significant improvement in predictive power. They also highlight the possibilities opened by this kind of large-scale database for a truly quantamental approach to equity valuation. Finally, they describe the technological solutions adopted to overcome the challenges posed by a dataset that can reach hundreds of millions of rows for a single firm.

    Speakers:

    Jakub Kolodziej

  • 14:15 -

    Bringing Data to Life at the Bank of England

    Lyndsey Pereira-Brereton, Data Visualization Editor, Bank of England

    With the explosion in the amount of data and the burden of information overload, how can we get the most out of our data and communicate this effectively? In my talk I will show how the Bank of England is using data visualisation to see through the data fog and better communicate our findings.

    Speakers:

    Lyndsey Pereira-Brereton

  • 14:45 -

    Panel Session: Alternative Data

    Moderator: Gautam Mitra; Panellists:

  • 15:30 -

    Tea

  • 16:00 -

    The Application of AI to Quantitative Systematic Strategies, Opportunities and Risks

    Guillaume Vidal, CEO, Walnut Algorithms

    ♦ How machine learning fits into systematic strategies
    ♦ What are the pros and cons of using machine learning in quant finance
    ♦ Building an infrastructure that enables machine learning research within the company
    ♦ Debunking the myth of superintelligence in finance

    Speakers:

    Guillaume Vidal

  • 16:30 -

    Including News Data in Forecasting the Macroeconomic Performance

    Asger Lunde, Director, Copenhagen Economics and Professor of Economics, Aarhus University

    This paper studies forecasting of Chinese macroeconomic time series using a large number of prediction variables. We investigate what is the extent of improvement of forecasts when news sentiment indexes are included among the predictors. Due to large number of predictors we summarize them with a smaller subset of indexes that are build with principal component analysis. An approximate dynamic factor model is then fit on these indexes and used for 3-, 6- and 12-month-ahead forecasts for 4 Chinese macroeconomic time series (Balance of Payments, Exchange rate with US dollar, GDP and Unemployment rate). In total we use 132 predictors from various sources ranging from 2000 through 2017. The results suggest that forecasts obtained with this method outperform univariate autoregressions and in shorter prediction horizon news indexes improve the forecasts.

    Speakers:

    Asger Lunde

  • 17:00 -

    Asset Classification Based on Machine Learning Techniques

    Francesco Cricchio, CEO, Brain and Matteo Campellone, Executive Chairman, Brain

    Brain has developed a set of models based on machine learning methods to statistically classify assets that are more likely to have a positive/negative return over the following time period. Input data can be conventional series (fundamentals, financial time series) or non conventional series such as, for instance, sentiment indicators or signals coming from other proprietary models. This approach can be used for multi-stock trading strategies as well as for tactical asset allocation models.

    Speakers:

    Francesco Cricchio

    Matteo Campellone

  • 17:30 -

    Drinks Reception and Networking

  • Session Chairperson (morning): Professor Gautam Mitra, OptiRisk Systems/UCL -

    Speakers:

    Gautam Mitra

  • 08:55 -

    Welcome and Introduction to Day 2 - Professor Gautam Mitra, OptiRisk Systems/UCL

    Speakers:

    Gautam Mitra

  • 09:00 -

    AI-Machine Learning and Deep Learning in FinTech

    Sanjiv Das, Professor of Finance and Data Science, Santa Clara University

    In This talk we define and characterize the business of FinTech by identifying 10 salient areas of influence. We then analyse one area, namely AI, and examine how it is changing the landscape of finance through FinTech applications.

    ♦ What is FinTech?
    ♦ Example of AI in FinTech.
    ♦ Predicting markets with AI.
    ♦ The transformation of data use with AI.
    ♦ The future of labor markets in the finance industry

    Speakers:

    Sanjiv Das

  • 09:30 -

    Enhanced prediction of sovereign bond spreads through Macroeconomic News Sentiment

    Christina Erlwein-Sayer, Senior Quantitative Analyst and Researcher, OptiRisk Systems

    Sovereign bond spreads are modelled taking into account macroeconomic news sentiment. We investigate sovereign bonds spreads of European countries and enhance the prediction of spread changes by including news sentiment. We conduct a correlation and rolling correlation analysis between sovereign bond spreads and accumulated sentiment series and analyse changing correlation patterns over time. These findings are utilised to monitor sovereign bonds, predict spread changes in an ARIMAX model and highlight changing risks. The results are integrated in the SENRISK tool, a DSS for Bond Risk Assessment.

    Speakers:

    Christina Erlwein-Sayer

  • 10:00 -

    Mining News Topic Codes With Sentiment

    Ivailo Dimov, Quant Researcher, Bloomberg

    Stories on the Bloomberg newsfeed are tagged with "topic codes" containing information about their origin, subject matter, or other characteristics. One might expect that sentiment analysis of news stories may be enhanced by taking into account these topic codes, but the sheer number of topic codes is an obstacle to doing so systematically.

    In this talk, we present evidence that some groups of topic codes are indeed associated with stronger sentiment impact on stock prices than others, and discuss a method to condense the mass of topic codes by identifying and retrieving latent factors which may be interpreted as broad themes shared by groups of topic codes.

    Speakers:

    Ivailo Dimov

  • 10:30 -

    Coffee

  • 11:00 -

    Finding Alpha Signals with Artificial Intelligence + Influencer Analysis + Big Data

    Anders Bally, CEO and Founder, Sentifi

    This presentation is about how new AI methodologies like Deep Learning, the maturing Big Data Technologies and the fast emerging Information Sharing Culture can help investors to more efficiently discover, monitor and potentially predict Asset Valuation Drivers.

    Speakers:

    Anders Bally

  • 11:30 -

    How to measure intangible assets - the missing factor for value investing

    Andreas Zagos, Intracom GmbH

    Intangible assets cover up to 84% of the company value in tech companies. The question is how to measure the intangible assets, namely patents and utiliy models. Intracom will present their indicator based approach on pattern recognition on big data for determining monetary values of patent portfolios - the "IP value factor". The monetary value was used for backtests on different indexes and the results of those tests will be presented. The "IP value factor" is uncorrelated and generates alpha in sector neutral backtests.

    Speakers:

    Andreas Zagos

  • 12:00 -

    The State of The Art in New Sentiment Visualization

    Jordan Mizrahi, CEO and Founder, FIRST TO INVEST

    Combining news sentiment approach and visual interactive displays, help end-users rapidly sort through volumes of companies’ news events to identify key insights faster and in easy to use, for any level in the organization. The visual analytics provide access to broader view on companies’ sentiment scoring, not just on a singular event, but rather of time-line perceptive, comparison to the competitors, sectoral and industry scoring and even markets differentiation.

    Speakers:

    Jordan Mizrahi

  • 12:30 -

    Lunch

  • JOINT COMMON SESSION WITH AI & ML APPLIED TO INDUSTRY & COMMERCE -

  • 13:30 -

    Panel Session: Does AI Beat Classical Models?

    Moderator: Gautam Mitra; Panellists:

  • Session Chairperson (afternoon): Dr Ronald Hochreiter, Vienna University of Economics and Business -

    Speakers:

    Ronald Hochreiter

  • 14:15 -

    How AI Can Predict Crypto Assets by Using Sentiment

    Stan Maer, COO, Cryptics.tech

  • 14:45 -

    Contemporary Deep Learning Methods for Building Investment Models Based on Graphical Time-series Representations

    Ronald Hochreiter, Docent & CEO, WU Vienna University of Economics and Business & Academy of Data Science in Finance

    AI and Machine Learning methods can be used to generate investment decisions successfully. A clever combination of Data Science methods with methods from the field of Decision Science (Prescriptive Analytics) may lead to even more successful models. In this talk a general outline for such a successful methodological combination will be presented as well as a concrete novel Deep Learning investment model which is based on graphical TTR series representations instead of using time-series directly. It will be shown how important Feature Engineering for Deep Learning in Finance actually is.

    Speakers:

    Ronald Hochreiter

  • 15:15 -

    Tea

  • 15:45 -

    Rapid Conditioning of Risk Estimates Using Quantified News Flows

    Christopher Kantos, Senior Equity Risk Analyst, Northfield

    In December of 2017 Northfield introduced the first commercially available factor risk models that incorporates computerized analysis of news text directly into volatility risk forecasts for individual stocks, corporate bonds, industry groups and ETFs based on market indices. Market events in early 2018 provided several excellent examples of why we believe that Risk Systems That Read® is the most significant innovation in factor risk models in more than three decades. We will illustrate show how recent news events drove financial market outcomes for Wynn Resorts, Wynn Macau, Facebook and Wanda Hotels (HK). Each day the content of thousands of news articles are now part of the input for the full range of models available from Northfield. The line of research that led to this innovation stretches back to 1997, and includes five published papers by Northfield staff [diBartolomeo and Warrick (2005), diBartolomeo, Mitra, Mitra (2009), diBartolomeo (2011,2013,2016)]. Beyond the obvious improvement in risk estimation, the method has important implications for alpha generation by both quant and traditional for active managers.

    Speakers:

    Christopher Kantos

  • 16:15 -

    Going Native with Japanese News Analysis

    Dan Joldzic, CFA, FRM, CEO of Alexandria Technology

    Local source, native publishers may offer an information advantage compared to publications in English. Translation services have typically been sub-optimal for character-based languages, but machine learning allows for classification in the native form, which can lead to significant alpha in forward periods.

    Speakers:

    Dan Joldzic

  • 16:45 -

    Machine Learning for Hedge Fund Selection

    Claus Huber, Founder and Managing Director, Rodex Risk Advisers

    This article describes the application of Kohonen’s Self-Organising Maps (SOM), a method of Machine Learning, to the problem of selecting hedge funds to achieve stable portfolio performance. SOM can help to identify similarities in return structures of hedge fund managers and hence to avoid concentrations in a portfolio. The core question is if SOM can add any value for manager selection. 2 novel yet simple methods to select hedge funds based on the specific properties of SOM are proposed that both target to identify unique investment strategies. To evaluate their performance relative to other, simpler benchmark methods of portfolio selection, a simulation study finds both SOM-based methods proposed enhance risk/return profiles and drawdown patterns.

    Speakers:

    Claus Huber

  • 17:15 -

    Close of Conference

Speakers

Anders Bally

Sentifi

Rajib Borah

iRage

Humberto Brandão

Federal University of Alfenas

Matteo Campellone

Brain

Douglas Castilho

University of São Paolo

Nishant Chandra

AIG Science

Francesco Cricchio

Brain

Pierce Crosby

StockTwits

Sanjiv Das

Santa Clara University, USA

Ivailo Dimov

Bloomberg

Christina Erlwein-Sayer

OptiRisk Systems

Edward Fishwick

BlackRock

Joao Gama

University of Porto

Peter Hafez

RavenPack

Ronald Hochreiter

WU Vienna University of Economics and Business & Academy of Data Science in Finance

Claus Huber

Rodex Risk Advisers

Dan Joldzic

Alexandria Technology

Christopher Kantos

Northfield

Jakub Kolodziej

Macquarie

James Luke

IBM

Asger Lunde

Aarhus University

Gautam Mitra

OptiRisk & UCL

Jordan Mizrahi

FIRST TO INVEST

Lyndsey Pereira-Brereton

Bank of England

Richard Peterson

MarketPsych Data

Guillaume Vidal

CEO, Walnut Algorithms

Xiang Yu

OptiRisk

Andreas Zagos

Intracom GmbH

Platinum Sponsor

Bronze Sponsors

 

Knowledge Partners

Supporting Bodies

Media Partners

 

 

 

Tickets

4 people attend for the price of 3

  • Use the coupon code "UNI443" when booking.
  • Buy Ticket

End User
27 – 28 June

  • Super Early Bird until 4 May - £275 + VAT
  • Early Bird until 25 May - £400 + VAT
  • Standard Price - £550 + VAT
  • Buy Ticket

Vendors
27 – 28 June

  • Super Early Bird until 4 May - £500 + VAT
  • Early Bird until 25 May - £600 + VAT
  • Standard Price - £750 + VAT
  • Buy Ticket

Workshop Price per day

  • Super Early Bird until 4 May - £150 + VAT
  • Early Bird until 25 May - £225 + VAT
  • Standard Price - £300 + VAT
  • Buy Ticket

For combined discounted price

  • For combined discounted price of the conference “AI, Machine Learning and Sentiment Analysis Applied to Finance” and the workshop / workshops, please contact aqeela@unicom.co.uk or anirban@unicom.co.uk

Venue

  • Rooms on Regent’s Park
    27 Sussex Place
    Regent’s Park
    London NW1 4RG
The Event will Start In