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.

Read more…


  • -
    Ronald Hochreiter

    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

    Ronald Hochreiter

  • -
    Gautam Mitra
    Xiang Yu

    Enhanced Trading Strategy using Sentiment and Technical Indicators

    Gautam Mitra, CEO, OptiRisk System & 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.

    Gautam Mitra

    Gautam Mitra

    Xiang Yu

    Xiang Yu

  • -
    Utpal Chakraborty

    Curating the Type of Data for Machine Learning Viability

    Utpal Chakraborty, Head of Artificial Intelligence, Yes Bank

    ♦ Importance of data management and data processing: Impacting the variables
    ♦ Lack of data or missing data for machine learning models: Sourcing internally or externally
    ♦ Identifying where financial institutions have the data sets to conduct deep learning
    ♦ Practicalities of using machine learning to create the data required: Quality consistency issues and more

    Utpal Chakraborty

    Utpal Chakraborty

  • -


    Christopher Kantos, Senior Equity Risk Analyst, Northfield


    Christopher Kantos

  • -

    Extracting Embedded Alpha in Social & News Data Using Statistical Arbitrage Techniques

    Arun Verma, Quantitative Research Solutions, Bloomberg LP

    ♦ 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


    Arun Verma

  • -

    Correlation Influence Networks for Sentiment Analysis in European Sovereign Bonds

    Peter Schwender, Professor, ZHAW School of Management and Law

    European sovereign bonds are especially sensitive to the political news flow. Consistent to the current sentiment, market makers adjust factor models in their quotation systems to be prepared for short-term market reactions in the most liquid instruments. We present a correlation influence network case study to make the signs of these factor betas transparent using intraday data analysis. This shows the sentiment of the most active market participants.


    Peter Schwender


Christopher Kantos

Senior Equity Risk Analyst, Northfield

Utpal Chakraborty

Utpal Chakraborty

Head of Artificial Intelligence, Yes Bank

Ronald Hochreiter

Ronald Hochreiter

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

Gautam Mitra

Gautam Mitra

CEO, OptiRisk System & Visiting Professor, UCL

Peter Schwender

Professor, ZHAW School of Management and Law

Arun Verma

Quantitative Research Solutions, Bloomberg LP

Xiang Yu

Xiang Yu

Chief Business Development Officer, OptiRisk

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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