Background

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.

Subscribe for updates

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…

Programme

  • -
    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.

    Speakers:
    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.

    Speakers:
    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

    Speakers:
    Utpal Chakraborty

    Utpal Chakraborty

  • -

    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

  • -

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

    Arun Verma, Quantitative Research Solutions, Bloomberg

    ♦ 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

  • -

    Correlation Influence Networks for Sentiment Analysis in European Sovereign Bonds

    Peter Schwendner, 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.

    Speakers:

    Peter Schwendner

  • -

    The Knowing-Doing Gap in Behavioral Finance

    Markus Schuller, Founder & Managing Partner, Panthera Solutions

    Investment management, is it discretionary or systematic, can benefit from insights gained in behavioral finance. Markus will highlight why professional investors tend to talk more about behavioral finance in investment management than actually make use of its practical takeaways in favor of more rational decision making.

    • Why more talk than walk?
    • What are the benefits of applying Behavioral Finance insights?
    • How to overcome the knowing-doing gap?

    Speakers:

    Markus Schuller

Speakers

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

Christopher Kantos

Senior Equity Risk Analyst, Northfield

Gautam Mitra

Gautam Mitra

CEO, OptiRisk System & Visiting Professor, UCL

Markus Schuller

Founder & Managing Partner, Panthera Solutions

Peter Schwendner

Professor, ZHAW School of Management and Law

Arun Verma

Quantitative Research Solutions, Bloomberg

Xiang Yu

Xiang Yu

Chief Business Development Officer, OptiRisk

Sponsor

Knowledge Partner

Supporting Bodies

Media Partners

 

 

Tickets

4 people attend for the price of 3

  • Use the coupon code "UNI443" when booking.
  • Buy Ticket

End user

  • Very Super Early Bird until 21 Dec - € 175
  • Super Early Bird until 5 Apr - € 275
  • Early Bird until 15 Apr - € 375
  • Standard Price - € 475
  • Buy Ticket

Vendor / Consultant

  • Very Super Early Bird until 21 Dec - € 375
  • Super Early Bird until 5 Apr - € 475
  • Early Bird until 15 Apr - € 575
  • Standard Price - € 675
  • Buy Ticket

Combined workshop discounted price

Contact Us

The Event Will Start In