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

  • -

    Chairperson

    Edward Fishwick, Managing Director, Global Co-Head, Risk & Quantitative Analysis at BlackRock

    Speakers:

    Edward Fishwick

  • -

    Evolving Social Networks: trajectories of communities

    Joao Gama, Professor, School of Economics, University of Porto

    In recent years we witnessed an impressive advance in the social networks field, which became a ”hot” topic and a focus of considerable attention. The development of methods that focus on the analysis and understanding of the evolution of data are gaining momentum. The need for describing and understanding the behavior of a given phenomenon over time led to the emergence of new frameworks and methods focused in temporal evolution of data and models. In this talk we discuss the research opportunities opened in analysing evolving data and present examples from mining the evolution of clusters and communities in social networks.

    Speakers:

    Joao Gama

  • -

    Mining News Topic Codes With Sentiment

    Ivailo Dimov, 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

  • -

    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

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    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

  • -

    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

  • -

    Enhanced Trading Strategy using Sentiment and Technical Indicators

    Gautam Mitra, CEO & Visiting Professor, OptiRisk & UCL, and Xiang Yu, Chief Business Development Officer, OptiRisk

    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

  • -

    Deep Learning for Sentiment Analysis

    Enza Messina, Professor in Operations Research, University of Milano-Bicocca

    We show how deep learning and ensemble methods can successfully address challenging problems arising in sentiment analysis such as irony detection or domain adaptation. In particular, we propose an unsupervised framework for domain-independent irony detection built upon an existing probabilistic topic model initially introduced for sentiment analysis purposes. Moreover, in order to improve its generalization abilities, we apply Word Embeddings to obtain domain-aware ironic orientation of words. The acquisition of cross-domain high level feature representations through word embeddings combined with the generalization capability of ensemble methods can also be used for addressing the problem of domain adaptation also in the scenario where the testing target domain is completely unlabelled.

    Speakers:

    Enza Messina

  • -

    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

  • -

    Why Algorithmic Trading in the Real World is so Different to Academic Experiments

    Humberto Brandão, Head of R&D Lab, Federal University of Alfenas

    It is not difficult to find academic papers showing how to make money easily using algorithmic trading, which includes graphs, statistical tests, etc. However, in real markets, the majority of them cannot be replicated. In this presentation, I will discuss some reasons for this problem and try to explain how to improve validation processes before applying an algotrader in real stock exchanges.

    Speakers:

    Humberto Brandão

  • -

    Different Components of Algorithmic Trading Systems - increasing profitability by optimising systems

    Rajib Borah, Co-founder & CEO, iRage

    By properly leveraging the power of technology, a trader can increase the profitability of an already profitable systematic trading strategy multi-fold. This talk will look at the evolution of algorithmic trading systems - and the efficiency introduced at each step. The talk will also try to introduce participants to the various technological complexities at exchanges - and opportunities that could exist because of the same. The aim will be to have an interactive discussion, and understand the functional implications (for quantitative traders) of technological complexities.

    Speakers:

    Rajib Borah

  • -

    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

  • -

    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

  • -

    Enhanced prediction of sovereign bond spreads through Macroeconomic News Sentiment

    Christina Erlwein-Sayer, 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

  • -

    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

  • -

    Hierarchical Natural Language Representation Using Deep Learning

    Nishant Chandra, 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

  • -

    AI-Machine Learning and Deep Learning in FinTech

    Sanjiv Das, Santa Clara University, USA

    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

  • -

    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

  • -

    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

  • -

    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

  • -

    Bringing numbers 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

  • -

    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

  • -

    Going Native with Japanese News Analysis

    Dan Joldzic, CFA, FRM, CEO of Alexandria Technology

    Speakers:

    Dan Joldzic

  • -

    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

  • -

    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

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

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

Enza Messina

University of Milano-Bicocca

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

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End User
27 – 28 June

  • Super Early Bird until 4 May - £275 + VAT
  • Early Bird until 25 May - £400 + VAT
  • Standard Price - £550 + VAT
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27 – 28 June

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Workshop Price per day

  • Super Early Bird until 4 May - £150 + VAT
  • Early Bird until 25 May - £225 + VAT
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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
Early Bird End Date