Background

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 7 GARP CPD credit hours. If you are a Certified Financial Risk Manager (FRM®), please record this activity in your Credit Tracker.

Topics to be 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, India and 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.

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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 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.

Code of Conduct & Diversity

UNICOM’s Code of Conduct & Views on Diversity

We at UNICOM strive to be a leading provider of knowledge to the business community and to engage the global business community as a specialised provider of knowledge. We strive to do this maintaining a culture of co-operation, commitment and trust. We want every UNICOM conference and training day to be a safe and productive environment for everyone – a place to share research and innovation and to build professional networks. To that end, we will enforce a code of conduct throughout all our events. We expect cooperation from all participants to help ensure a safe environment for everybody.

Our approach is that our events are dedicated to providing a harassment-free experience for everyone, regardless of gender, gender identity and expression, age, sexual orientation, disability, physical appearance, body size, race, ethnicity or religion. We do not tolerate intimidation, stalking, harassing photography or recording, sustained disruption of sessions or events, and unwelcome physical contact or sexual attention. We do not tolerate harassment of conference participants in any form. Sexual language and imagery is not appropriate for any conference venue, including talks, workshops, Twitter and other online media. Event participants violating these rules may be sanctioned or expelled from the event without a refund at the discretion of the conference organisers. Please bring your concerns to the immediate attention of the event staff.

Diversity: In our endeavour to be the provider of knowledge to the business community, we understand that this depends on hearing from and listening to a variety of perspectives that come from people of all races, ethnicities, genders, ages, abilities, religions, sexual orientation, and military service. We welcome diverse speakers for all our events, we do not always fully achieve this goal, but it is an ongoing process.

Programme

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    Chairperson

    Gautam Mitra, CEO, OptiRisk & Visiting Professor, UCL

    Speakers:

    Gautam Mitra

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    Extracting tradable signals from traditional & alternative data using Machine learning

    Arun Verma, Ph.D, Senior Quantitative Researcher & Quant Solutions Team Lead, Bloomberg L.P.

    ♦ Extracting actionable information in the high volume, time-sensitive environment of news and social media stories
    ♦ Using machine learning to address the unstructured nature of textual information
    ♦ Techniques for identifying relevant news stories and tweets for individual stock tickers and assigning them sentiment scores
    ♦ Demonstrating that using sentiment scores in your trading strategy ultimately helps in achieving higher risk-adjusted returns

    Speakers:

    Arun Verma

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    News Sentiment – a new yield curve factor

    Matthias Uhl, Executive Director in Analytics & Quantitative Modelling at UBS Asset Management

    We show that sentiment from news articles can explain and predict movements in the term structure of U.S. government bonds. This effect is stronger at the short end of the curve, coinciding with greater volatility and investors’ need to continually reassess the Fed’s reaction function. Facing such uncertainty, market participants rely on news and sentiment as a central element in their decision-making process. Considering this dependence, we propose a new yield curve factor-news sentiment-that is distinct from the 3 established yield curve factors (level, slope, and curvature) as well as from fundamental macroeconomic variables.

    Speakers:

    Matthias Uhl

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    Enhanced Trading Strategy using Sentiment and Technical Indicators

    Gautam Mitra, CEO, OptiRisk & Visiting Professor, 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

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

    Ronald Hochreiter, 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

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    Enhanced prediction of sovereign bond spreads through Macroeconomic News Sentiment

    Christina Erlwein-Sayer, Senior Quantitative Analyst, 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

  • -

    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

Speakers

Rajib Ranjan Borah

Co-founder & CEO, iRage

Humberto Brandão

Head of R&D Lab, Federal University of Alfenas

Christina Erlwein-Sayer

Senior Quantitative Analyst, OptiRisk Systems

Ronald Hochreiter

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

Gautam Mitra

CEO, OptiRisk & Visiting Professor, UCL

Arun Verma

Ph.D, Senior Quantitative Researcher & Quant Solutions Team Lead, Bloomberg L.P.

Matthias Uhl

Executive Director in Analytics & Quantitative Modelling at UBS Asset Management

Xiang Yu

Chief Business Development Officer, OptiRisk

Knowledge Partner

Supporting Bodies

Previous Programme

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    Chairperson

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    Evolving Social Networks: trajectories of communities

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    Mining News Topic Codes With Sentiment

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    News Sentiment Everywhere!

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

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

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    Enhanced Trading Strategy using Sentiment and Technical Indicators

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    Asset Classification Based on Machine Learning Techniques

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

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

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

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

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    Enhanced prediction of sovereign bond spreads through Macroeconomic News Sentiment

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

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    Hierarchical Natural Language Representation Using Deep Learning

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    AI-Machine Learning and Deep Learning in FinTech

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

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    Rapid Conditioning of Risk estimates Using Quantified News Flows

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

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    Bringing Data to Life at the Bank of England

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    Including news data in forecasting the macroeconomic performance

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    Going Native with Japanese News Analysis

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    The State of The Art in New Sentiment Visualization

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    Machine Learning for Hedge Fund Selection

Previous 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

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

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