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.

  • 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 and Deep Learning.

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…


  • -

    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

  • -

    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.


    Gautam Mitra

    Xiang Yu

  • -

    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.


    Humberto Brandão

  • -

    Enhanced prediction of sovereign bond spreads through Macroeconomic News Sentiment

    Christina Erlwein-Sayer,Senior Quantitative Analyst & 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.


    Christina Erlwein-Sayer

  • -

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

    Richard Peterson, Managing Director, MarketPsych

    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.


    Richard Peterson

Previous Programme

  • 08:00 -

    Registration and Coffee

  • 08:45 -

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

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


  • 09:00 -

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

  • 09:30 -

    News Sentiment Everywhere!

  • 10:15 -

    Hierarchical Natural Language Representation Using Deep Learning

  • 10:45 -

    Introduction to Sponsors

  • 10:50 -


  • 11:15 -

    Enhanced Trading Strategy using Sentiment and Technical Indicators

  • 11:45 -

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

  • 12:15 -

    Social Trading – Developing Signals from Social Sentiment

  • 12:45 -



  • 13:45 -

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

  • 14:15 -

    Bringing Data to Life at the Bank of England

  • 14:45 -

    Panel Session: Alternative Data

  • 15:30 -


  • 16:00 -

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

  • 16:30 -

    Including News Data in Forecasting the Macroeconomic Performance

  • 17:00 -

    Asset Classification Based on Machine Learning Techniques

  • 17:30 -

    Drinks Reception and Networking

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

  • 08:55 -

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

  • 09:00 -

    AI-Machine Learning and Deep Learning in FinTech

  • 09:30 -

    Enhanced prediction of sovereign bond spreads through Macroeconomic News Sentiment

  • 10:00 -

    Mining News Topic Codes With Sentiment

  • 10:30 -


  • 11:00 -

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

  • 11:30 -

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

  • 12:00 -

    The State of The Art in New Sentiment Visualization

  • 12:30 -



  • 13:30 -

    Panel Session: Does AI Beat Classical Models?

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

  • 14:15 -

    How AI Can Predict Crypto Assets by Using Sentiment

  • 14:45 -

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

  • 15:15 -


  • 15:45 -

    Rapid Conditioning of Risk Estimates Using Quantified News Flows

  • 16:15 -

    Going Native with Japanese News Analysis

  • 16:45 -

    Machine Learning for Hedge Fund Selection

  • 17:15 -

    Close of Conference


Humberto Brandão

Head of R&D Lab, Federal University of Alfenas

Utpal Chakraborty

Head of Artificial Intelligence, Yes Bank

Christina Erlwein-Sayer

Senior Quantitative Analyst & Researcher, OptiRisk Systems

Gautam Mitra

CEO & Visiting Professor, OptiRisk & UCL

Richard Peterson

Managing Director, MarketPsych

Xiang Yu

Chief Business Development Officer, OptiRisk

Previous Speakers

Anders Bally


Rajib Borah


Humberto Brandão

Federal University of Alfenas

Matteo Campellone



Douglas Castilho

University of São Paolo

Nishant Chandra

AIG Science

Francesco Cricchio


Pierce Crosby



Sanjiv Das

Santa Clara University, USA

Ivailo Dimov


Christina Erlwein-Sayer

OptiRisk Systems

Edward Fishwick



Joao Gama

University of Porto

Peter Hafez


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


Jakub Kolodziej


James Luke



Asger Lunde

Aarhus University

Gautam Mitra

OptiRisk & UCL

Jordan Mizrahi


Lyndsey Pereira-Brereton

Bank of England


Richard Peterson

MarketPsych Data

Guillaume Vidal

CEO, Walnut Algorithms

Xiang Yu


Andreas Zagos

Intracom GmbH

Media Partners



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