Artificial Intelligence and Machine Learning (AI & ML) and Sentiment Analysis are said to “predict the future through analysing the past” – the Holy Grail of the finance sector. They can replicate cognitive decisions made by humans yet avoid the behavioural biases inherent in humans.

Processing news data and social media data and classifying (market) sentiment and how it impacts Financial Markets is a growing area of research. The field has recently progressed further with many new “alternative” data sources, such as email receipts, credit/debit card transactions, weather, geo-location, satellite data, Twitter, Micro-blogs and search engine results. AI & ML are gaining adoption in the financial services industry especially in the context of compliance, investment decisions and risk management.

This is a sophisticated conference that not only interrogates and explores the implications of AI & ML in the financial services industry but also goes on to identify the investment opportunities of sharing knowledge and exploiting IP in the finance domain.

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 credit hours. If you are a Certified ERP® or FRM®, please record this activity in your Credit Tracker.

  • Learn how you can benefit from the unprecedented progress in technological advances for yourself and your company
  • Find out about the impact of Quantum Computing and Alternative Data
  • Benefit from the experience of world class presenters from the UK, US, Europe and India/Hong Kong
  • Gain exclusive insights into pioneering projects in AI, Machine Learning & Sentiment Analysis in Finance
  • Programme includes the latest state-of-the-art research, practical applications and case studies
  • Enjoy 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.

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.


  • -

    AI-Machine Learning and Deep Learning in FinTech

    Sanjiv Das, Professor of Finance, 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


    Sanjiv Das

  • -

    A Deep Learning Meta-model Approach to Compute Optimal Investment Strategies

    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.


    Ronald Hochreiter

  • -

    How AI & ML and Text Analysis of Alternative Data is impacting Financial and Retail Markets

    Enza Messina, University of Milano-Bicocca

    We analyze how AI and Machine Learning and Sentiment Analysis of News and Micro-blogs are and impacting the two rapidly expanding markets, namely, Financial market and Retail market. We support our analysis by a few Use Cases for these markets.


    Enza Messina

  • -

    Enhanced Trading Strategy using Sentiment and Technical Indicators

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

    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.


    Gautam Mitra

    Xiang Yu

  • -


    Claus Huber, Founder & MD, Rodex Risk Advisers LLC


    Claus Huber

  • -

    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.


    Christopher Kantos

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    Social Listening & Financial Crowd-Intelligence

    Anders Bally, CEO & Founder, Sentifi

    In the early 90’s, the majority of financial market participants used news mainly from services like Bloomberg and Reuters to inform themselves. 20 years later, they still do. During the same period, our society went through a communication paradigm shift. Today more than 2 billion people walk around with mobile devices and communicate what they see and think on social media. These billions of voices, when structured, can generate insights which can help investors make better investment decisions. This presentation will touch on how Sentifi structures and delivers these insights, providing an information advantage for media platforms globally.


    Anders Bally

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    Application of Generative Adversarial Networks (GANs) in Algorithmic Trading

    Mohammad Yousuf Hussain, Senior Tech and Innovation Specialist, Jasmine22

    As digital innovation and cognitive solutions gain more traction, there is a need to create greater awareness and familiarity with the latest technology trends amongst ourselves. Generative Adversarial Networks (GANs) seems to be advancing well through their hype cycle and are entering the phase of widespread deployment.

    In this session, the presenter will provide an overview of the GANs framework and highlight their explain ability through the concepts of game theory, enabling the discussion to move towards the application of GANs in algorithmic trading. The main use case would be about independent behaviour modelling of the market participants, construction of objective functions and suitable optimisation techniques.


    Mohammad Yousuf Hussain

  • -

    The application of deep learning to high dimensional models in finance

    Panos Parpas, Senior Lecturer, Dept. of Computing, Imperial College London

    – Reformulate deep learning as an optimisation problem
    – Discuss the importance of stability for robust solutions.
    – Illustrate the use of deep learning to solve high dimensional (more than 100 dimensions) nonlinear parabolic PDEs (Black&Scholes, Hamilton-Jacobi Belman)
    – Provide code and some examples for participants to experiment with.


    Panos Parpas


Anders Bally

CEO & Founder, Sentifi

Utpal Chakraborty

Head of AI, Yes Bank

Sanjiv Das

Professor of Finance, Santa Clara University, USA

Ronald Hochreiter

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

Claus Huber

Founder & MD, Rodex Risk Advisers LLC

Mohammad Yousuf Hussain

Senior Tech and Innovation Specialist, Jasmine22

Christopher Kantos

Senior Equity Risk Analyst, Northfield

Enza Messina

University of Milano-Bicocca

Gautam Mitra

CEO, OptiRisk System & Visiting Professor, UCL

Panos Parpas

Senior Lecturer, Dept. of Computing, Imperial College London

Xiang Yu

Chief Business Development Officer, OptiRisk Systems


Sentifi Gold Sponsor

Knowledge Partner

Supporting Bodies

Media Partners

Previous Programme

  • 08:00 -

    Registration and Coffee

  • 08:45 -

    Introduction and Welcome – Professor Gautam Mitra, OptiRisk Systems/UCL (Programme Chair) Introduction to sponsors

  • 09:00 -

    Applying Quantum Computing to the Finance Industry

  • 09:30 -

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

  • 10:00 -

    Robo X - The AI Based Investment Manager

  • 10:30 -


  • 11:00 -

    News Sentiment – a new yield curve factor

  • 11:20 -

    Enhanced prediction of sovereign bond spreads through Macroeconomic News Sentiment

  • 11:40 -

    Correlation Influence Networks for Sentiment Analysis in European Sovereign Bonds

  • 12:00 -

    Panel session 1: Macro News Analysis brings new perspectives on Bond Portfolios

  • 12:45 -


  • 13:45 -

    Machine Learning for Hedge Fund Selection

  • 14:05 -

    Understanding the complex network of information to find an information edge

  • 14:25 -

    Extracting tradable signals from traditional & alternative data using Machine learning

  • 14:45 -

    Enhanced Trading Strategy using Sentiment and Technical Indicators

  • 15:15 -

    Sentiment scoring of global stocks based on machine learning approaches combined with Natural Language Processing techniques.

  • 15:35 -

    Panel Session 2: AI, Alternative Data and Equity Trading

  • 16:00 -


  • 16:30 -

    Enhancing Cryptocurrency Forecasting by using Deep Learning Sentiment Analysis

  • 17:00 -

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

  • 17:30 -

    Close of conference; Drinks Reception

Previous Speakers

Anders Bally


Ashish Kishore Bindal

CTO, Deeption SA

Rajib Ranjan Borah

Co-founder & CEO, iRage

Humberto Brandão

Head of R&D Lab, Federal University of Alfenas

Matteo Campellone

Co-founder and Executive Chairman of Brain

Francesco Cricchio

Co-founder and CEO of Brain

Christina Erlwein-Sayer

Senior Quantitative Analyst, OptiRisk Systems

Ronald Hochreiter

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

Claus Huber

Rodex Risk Advisers

Gautam Mitra

CEO, OptiRisk & Visiting Professor, UCL

Kamel Nebhi


Peter Schwendner

Professor, Zurich University of Applied Sciences

Tobias Setz

CTO, OpenMetrics

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

Stefan Woerner

Global Leader – Quantum Finance & Optimization, Quantum Technologies Group, IBM Research – Zurich

Xiang Yu

Chief Business Development Officer, OptiRisk


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