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 14 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.

Programme

  • -

    Keynote Presentation: Does News Sentiment Add Alpha?

    Ernie Chan, Hedge Fund Manager, QTS Capital Management, LLC

    We will present results of the Kaggle Two Sigma News Analytics Competition. This competition provides both prices and Thomson-Reuters news sentiment data as input to predict the future returns of US stocks. We want to answer questions such as whether machine learning techniques truly outperform simple factor models, and whether news sentiment truly adds value. Our presentation will not only include our own research, but also highlights other high-performing competitors research as well.

    Speakers:

    Ernie Chan

  • -

    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

  • -

    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

  • -

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

    Enza Messina, Professor in Operations Research, 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.

    Speakers:

    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.

    Speakers:

    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.

    Speakers:

    Humberto Brandão

  • -

    TBD

    Claus Huber, Founder & MD, Rodex Risk Advisers LLC

    Speakers:

    Claus Huber

  • -

    How Machine Learning Can Help Stock Pickers

    Giuliano De Rossi, Head of Quantitative Strategy, Macquarie

    We consider a new approach to analyse the vast amount of information available about the portfolio positions of institutional investors over time. Our goal is to use machine learning to analyse the stock picks of active equity funds. Recommender systems have been employed for a wide range of applications: Suggesting books, hotels, scientific papers and even new social connections. Here we aim to identify stocks that are likely to be bought by a given portfolio manager based on his or her own existing stock picks and the recent trading activity of other investors. By aggregating the results we seek to build a new signal related to the institutional demand for a given stock analysed by Koijen and Yogo (2016).

    Speakers:

    Giuliano De Rossi

  • -

    TBD

    Katharina Schwaiger, Director, also an investment researcher within the Factor Based Strategies Group, BlackRock

    Speakers:

    Katharina Schwaiger

  • -

    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.

    Speakers:

    Mohammad Yousuf Hussain

  • -

    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.

    Speakers:

    Peter Schwendner

  • -

    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

  • -

    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.

    Speakers:

    Anders Bally

  • -

    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.

    Speakers:

    Panos Parpas

  • -

    News Sentiment – a new yield curve factor

    Alexander Eisele, UBS AM Investment Solutions, Analytics & Quant Modelling (AQM)

    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:

    Alexander Eisele

Speakers

Anders Bally

CEO & Founder, Sentifi

Humberto Brandão

Head of R&D Lab, Federal University of Alfenas

Utpal Chakraborty

Head of AI, Yes Bank

Ernie Chan

Hedge Fund Manager, QTS Capital Management, LLC

Giuliano De Rossi

Head of Quantitative Strategy, Macquarie

Alexander Eisele

UBS AM Investment Solutions, Analytics & Quant Modelling (AQM)

Claus Huber

Founder & MD, Rodex Risk Advisers LLC

Ronald Hochreiter

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

Mohammad Yousuf Hussain

Senior Tech and Innovation Specialist, Jasmine22

Christopher Kantos

Senior Equity Risk Analyst, Northfield

Enza Messina

Professor in Operations Research, University of Milano-Bicocca

Gautam Mitra

CEO, OptiRisk System & Visiting Professor, UCL

Panos Parpas

Senior Lecturer, Dept. of Computing, Imperial College London

Katharina Schwaiger

Director, also an investment researcher within the Factor Based Strategies Group, BlackRock

Peter Schwendner

Professor, ZHAW School of Management and Law

Arun Verma

Quantitative Research Solutions, Bloomberg

Xiang Yu

Chief Business Development Officer

Sponsors

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)

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

  • JOINT COMMON SESSION WITH AI & ML APPLIED TO INDUSTRY & COMMERCE -

  • 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 -

    Coffee

  • 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 -

    Lunch

  • JOINT COMMON SESSION WITH AI & ML APPLIED TO INDUSTRY & COMMERCE -

  • 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 -

    Tea

  • 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 -

    Coffee

  • 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 -

    Lunch

  • JOINT COMMON SESSION WITH AI & ML APPLIED TO INDUSTRY & COMMERCE -

  • 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 -

    Tea

  • 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

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

Pierce Crosby

StockTwits

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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