BACKGROUND

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.

EVENT TIME

The event is mainly focused on participant from Europe and UK. So the Event time is set so that participants from UK or Europe may attend in the AM 08:30 GMT or 09:30 CET.

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 21 GARP CPD credit hours. If you are a Certified Financial Risk Manager (FRM®), or Energy Risk Professional (ERP®), please record this activity in your Credit Tracker.

Discounted attendance offer:
If you are a GARP Alumni, that is, an ERP or FRM certificate holder then avail yourself a 20% discount offer for the event registration fee. To know more about the offer contact us [email: info@unicom.uk; ankita.talit@unicom.uk]

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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…

November 2020 conference programme under development

ESG in Factors  - Read More
Katharina Schwaiger, Investment Researcher, BlackRock

Katharina Schwaiger, Investment Researcher, BlackRock

Environmental, Social, and Governance (ESG) signals are an important part of factor-based investing strategies as they can stem from the same economic rationales as general factor premiums. Because factors are broad and diversified, building portfolios by jointly optimizing factor exposures with ESG and carbon outcomes results in similar historical performance as benchmark factor portfolios which do not include those considerations. We show how sustainable signals, which often involve alternative data, can be integrated in the definitions of factors themselves: we offer two examples on green intangible value and corporate culture quality which enhance traditional financial value and quality factors, respectively.

Discovering Intelligence in Unstructured Content  - Read More
Dan Joldzic, CEO and Quantitative Researcher, Alexandria Technology

Dan Joldzic, CEO and Quantitative Researcher, Alexandria Technology

♦ Technology borrowed from the domain of DNA identification
♦ Design to identify cause and effect in large datasets
♦ Analysis of immense quantity of genomic information
♦ This AI&ML base technology applied to financial analytics

TBC  - Read More
DROR Y. KENETT, Economist, Financial Industry Regulatory Authority (FINRA)

Dror Y. Kenett, Economist, Financial Industry Regulatory Authority (FINRA)
TBC

TBC  - Read More
SAEED AMIN, Founder, Cuemacro

SAEED AMIN, Founder, Cuemacro
TBC

TBC  - Read More
ALEXANDER DENEV, Head of AI – Financial Services Advisory, Deloitte

ALEXANDER DENEV, Head of AI – Financial Services Advisory, Deloitte
TBC

TBC  - Read More
RAUL GLAVAN, Consultant Artificial Intelligence & Asset Management | Trader | Speaker | UBI Enthusiast

RAUL GLAVAN, Consultant Artificial Intelligence & Asset Management | Trader | Speaker | UBI Enthusiast
TBC

TBC  - Read More
DR. ARUN VERMA, Quantitative Research Solutions, Bloomberg

DR. ARUN VERMA, Quantitative Research Solutions, Bloomberg
TBC

TBC  - Read More
DR. RADU CIOBANU, VP Engineering, PredictNow.ai

DR. RADU CIOBANU, VP Engineering, PredictNow.ai
TBC

TBC  - Read More
DR. ERNIE CHAN, Founder, PredictNow.ai Inc

DR. ERNIE CHAN, Founder, PredictNow.ai Inc
TBC

SPEAKERS

Saeed Amin

Founder, Cuemacro

Dr. Ernie Chan

Founder, PredictNow.ai Inc

Dr. Radu Ciobanu

VP Engineering, PredictNow.ai

Alexander Denev

Head of AI – Financial Services Advisory, Deloitte

Raul Glavan

Consultant Artificial Intelligence & Asset Management | Trader | Speaker | UBI Enthusiast

DAN JOLDZIC

CEO and Quantitative Researcher, Alexandria Technology

Katharina Schwaiger

Investment Researcher, BlackRock

Dr. Arun Verma

Quantitative Research Solutions, Bloomberg

Dror Y. Kenett

Economist, Financial Industry Regulatory Authority (FINRA)

Previous programme June 2019

Day 1
Day 2

08:45 – MORNING CHAIR: GAUTAM MITRA, CEO, OPTIRISK SYSTEMS & VISITING PROFESSOR, UCL

09:00 – ADVANCES IN FACTOR INVESTINGRead More
KATHARINA SCHWAIGER, INVESTMENT RESEARCHER, BLACKROCK

Factor investing is an investment approach that involves targeting specific drivers of return across asset classes. There are two main types of factors: macroeconomic and style. Investing in factors can help improve portfolio outcomes, reduce volatility and enhance diversification. Factors has the transformative ability to change the way that we efficiently invest, deliberately manage risk and holistically build portfolios.

09:30 – THE KNOWING-DOING GAP IN BEHAVIORAL FINANCERead More
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?

10:00 – SOCIAL LISTENING AND FINANCIAL CROWD INTELLIGENCERead More
LUCAS BRUGGEMAN, PARTNER, SENTIFI

On a single day, humans across the globe produce 500 million tweets, 4 million blogs and 2 million online news.

That’s why in the age of big data, the real challenge is to make sense of it by filtering out the noise and finding relevant signals. In this session, we show you how we extract actionable insights and how these help you to stay ahead of the curve.

10:40 – INTRODUCTION OF SPONSORS

10:45 – COFFEE BREAK

11:15 – RAPID CONDITIONING OF RISK ESTIMATES USING QUANTIFIED NEWS FLOWRead More
CHRIS 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.

11:45 – ENHANCING PERFORMANCE OF MID TO LOW FREQUENCY TRADE PORTFOLIOSRead More
GAUTAM MITRA, CEO, OPTIRISK SYSTEMS & VISITING PROFESSOR, UCL

  • Filtering asset universe
  • RSI, NewsRSI (NRSI), DerivedRSI (DRSI)
  • Results NIFTY 50, S&P 500
  • Machine Learning (ML) to predict market movements (mini regimes)
  • Feature Modelling
  • Results NIFTY 50, S&P 500

12:15 – PANEL: ALTERNATIVE DATA

Moderator: Alexander Eisele, Analytics & Quant Modelling, UBS

Panelists:
Dan Joldzic, CEO, Alexandria
Lucas Bruggeman, Partner, Sentifi

12:45 – LUNCH

AFTERNOON CHAIR: RONALD HOCHREITER, ASSOCIATE PROFESSOR FOR FINANCE, WEBSTER VIENNA PRIVATE UNIVERSITY

13:45 – MACHINE LEARNING FOR VISUAL PORTFOLIO RISK ANALYSISRead More
CLAUS HUBER, PORTFOLIO MANAGER, DEKA INVESTMENT

A very valuable feature of the Self-Organising Map, a method of Machine Learning, is its visualisation capabilities. We show how the Self-Organising Map can be deployed to visualise the risk structure in a portfolio, in particular for assets for which no risk models exist. Some examples to this end are the visualisation of risk concentrations, identifying diversifiers and scenario analysis. Real-world applications are the selection of hedge fund managers or the analysis of a portfolio of Alternative Risk Premia.

14:15 – A DEEP LEARNING META-MODEL APPROACH TO COMPUTE OPTIMAL INVESTMENT STRATEGIESRead More
RONALD HOCHREITER, ASSOCIATE PROFESSOR FOR FINANCE, WEBSTER VIENNA PRIVATE UNIVERSITY

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.

14:45 – GOING NATIVE WITH JAPANESE NEWS ANALYSISRead More
DAN JOLDZIC, CEO, ALEXANDRIA

Local source, native publishers may offer an information advantage compared to publications in English. Translation services have typically been sub-optimal for character-based languages, but machine learning allows for classification in the native form, which can lead to significant alpha in forward periods.

15:15 – TEA BREAK

15:45 – ENHANCED PREDICTION OF SOVEREIGN BOND SPREADS THROUGH MACROECONOMIC NEWS SENTIMENTRead More
CHRISTINA ERLWEIN – SAYER, PROFESSOR OF STATISTICS AND FINANCIAL MATHEMATICS, HTW BERLIN & 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.

16:15 – NEWS SENTIMENT AND MULTI-ASSET INVESTINGRead More
ALEXANDER EISELE, ANALYTICS & QUANT MODELLING, UBS

Institutional multi-asset portfolios are often managed with significant constraints on turnover, tracking errors and the investable asset universe. Does news sentiment add any value to a portfolio when such constraints are taken into account? In this session we provide and discuss evidence suggesting that it does. Furthermore, we decompose news sentiment into different components to learn more about the drivers of its value-added.

16:45 – CORRELATION INFLUENCE NETWORKS FOR SENTIMENT ANALYSIS IN EUROPEAN SOVEREIGN BONDSRead More
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.

17:15 – DRINKS RECEPTION

MORNING CHAIR: ENZA MESSINA, PROFESSOR IN OPERATIONS RESEARCH, UNIVERSITY OF MILANO-BICOCCA

09:00 – THE RISE AND RISE OF UPIRead More
SANTANU PAUL, CEO, TALENTSPRINT

If India is rapidly emerging as a world power in FinTech, full credit must be given to the digital payments revolution unleashed by the Unified Payment Interface (UPI). Designed and launched by the National Payments Corporation of India (NPCI), the volume of UPI transactions has exploded in the last one year, surpassing all expectations. What is fuelling the UPI revolution? Why are Banks and FinTechs adopting UPI at an unprecedented rate? What do consumers like so much about it? This presentation will provide a bird’s eye view of the UPI growth machine.

09:30 – QUANTAMENTAL FACTOR INVESTING USING ALTERNATIVE DATA AND MACHINE LEARNINGRead More
ARUN VERMA, QUANTITATIVE RESEARCH SOLUTIONS, BLOOMBERG

To gain an edge in the markets quantitative hedge fund managers require automated processing to quickly extract actionable information from unstructured and increasingly non-traditional sources of data. The nature of these “alternative data” sources presents challenges that are comfortably addressed through machine learning techniques. We illustrate use of AI and ML techniques that help extract derived signals that have significant alpha or risk premium and lead to profitable trading strategies.

This session will cover the following topics:

  • The broad application of machine learning in finance
  • Extracting sentiment from textual data such as news stories and social media content using machine learning algorithms
  • Construction of scoring models and factors from complex data sets such as supply chain graph, options (implied volatility skew, term structure), Geolocational datasets and ESG (Environmental, Social and Governance)
  • Robust portfolio construction using multi-factor models by blending in factors derived from alternative data with the traditional factors such as fama-french five-factor model.

10:00 – THE APPLICATION OF DEEP LEARNING TO HIGH DIMENSIONAL MODELS IN FINANCERead More
PANOS PARPAS, SENIOR LECTURER, IMPERIAL COLLEGE

– 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.

10:30 – COFFEE BREAK

11:00 – MODELLING INTRADAY RISK AND FLOW CO-MOVEMENT TO IMPROVE TRADING PERFORMANCERead More
GIULIANO DE ROSSI, EXECUTIVE DIRECTOR, GOLDMAN SACHS & ANDREA PETRIDES, ASSOCIATE, GOLDMAN SACHS

Markets around the globe exhibit strong varying intraday characteristics. As a consequence, modelling the underlying intraday market dynamics is crucial in optimising trading execution. In this talk, we discuss the effect that modelling intraday flow co-movement and intraday risk have in creating optimal trade schedules, while also taking into consideration the individual stock’s market microstructure, providing useful insights. Our methodology relies on unsupervised learning techniques to identify the most important drivers of intraday market dynamics at stock level.

11:30 – FROM NOWCASTING TO NEWSCASTING – EXPLORING THE LINK BETWEEN NEWS SENTIMENT, THE ECONOMY AND ASSET PRICES FLUCTUATIONSRead More
MAURIZIO LUISI, INVESTMENT RESEARCH SOLUTIONS, AD-IRS

Established nowcasting techniques generally use as inputs two types of economic data: directly quantifiable variables, mostly referred to as hard data, and survey-based measures of the economy, or soft data. In this presentation, we increase the dimensionality of the nowcasting input data set by introducing a third type of data: machine-readable news analytics based on textual analysis. We propose to exploit the quantitative information conveyed by machine-readable news, transforming these analytics into proxies for macro-driven sentiment values. To retain tractability and facilitate the interpretation of this novel type of data, we aggregate the high–dimensionality of this information set into an established taxonomy of economically identifiable sentiment proxies; through this, we are able to map them into the same categories in which we group hard and soft data. We concentrate on U.S. economy as the focus of our analysis. The wider objective here is to extract and incorporate additional information relevant for tracking current and future economic conditions, but also and foremost to provide an empirical method to shed light on the interaction between macro news-flow sentiment, the real economy and asset prices fluctuations.

WOMEN IN FINTECH SESSION

12:00 – HOW AI, ML & TEXT ANALYSIS OF ALTERNATIVE DATA IS IMPACTING FINANCIAL AND RETAIL MARKETSRead More
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.

12:20 – SOCIAL TRADING – DEVELOPING SIGNALS FROM SOCIAL SENTIMENTRead More
DOROTHY RUDERMAN, HEAD OF DATA PARTNERSHIPS, STOCKTWITS

StockTwits is the largest independent social network setup for investors and traders to talk about investing. In addition to covering 8,300 stocks per year, the network also discusses 1,500+ alternative assets, including FX, futures, fixed income, privative companies, ETFs/indexes, and cryptoassets. With a dataset that stretches back to 2009, the network becomes a rich dataset for both quantitative investing as well as model development. In this talk, we will discuss the methodology behind developing an NLP-based social signal, as well as some of the academic studies run in parallel with this research. We will also discuss some of the ways in which it is being deployed in markets today.

12:40 – PANEL: WOMEN IN FINTECH

Moderator: Enza Messina, Professor in Operations Research, University of Milano-Bicocca

Panelists:
Dorothy Ruderman, Head of Data Partnerships, StockTwits
Katharina Schwaiger, Investment Researcher, BlackRock

Erica Stanford, CEO, Crypto Curry Club

Monica Summerville, Director of FinTech Research, Tabb Group

13:15 – LUNCH

AFTERNOON CHAIR: GAUTAM MITRA, CEO, OPTIRISK SYSTEMS

14:15 – KEYNOTE – DOES NEWS SENTIMENT ADD ALPHA?Read More
ERNIE CHAN, HEDGE FUND MANAGER, QTS CAPITAL

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.

14:45 – MACHINE LEARNING FOR FINANCIAL MARKETS : THREE REPRESENTATIVE EXAMPLESRead More
CHARLES – ALBERT LEHALLE, SENIOR RESEARCH ADVISOR, CAPITAL FUND MANAGEMENT & VISITING RESEARCHER, IMPERIAL COLLEGE

As a generic technology, machine learning has numerous secondary innovations in a lot of sectors. In this talk I will discuss the innovations emerging (or to appear) for financial markets. I will provide one example of online learning on trading flows (for real-time Dark Pools selection), another one of now casting (satellite images processing for crop quality prediction), and the last one will be on human-machine interface (decision support to monitor hundreds of algorithms). This last example is significant of one of the greatest challenge linked to the use of AI: when and how to give back the control to humans, so that then can really take an enlighten decision.

15:15 – PANEL: ADOPTION OF AI & ML IN FINANCE

Moderator: Monica Summerville, Director of FinTech Research, Tabb Group

Panelists:
Arun Verma, Quantitative Research Solutions, Bloomberg
Francesco Cricchio, Executive Chairman, Brain

15:45 – TEA BREAK

16:00 – MACHINE LEARNING METHODS APPLIED TO FINANCE: BRAIN’S PROPRIETARY APPROACHES FOR STOCK SELECTION, CLUSTERING OF FUNDS AND MARKET SCENARIOS.Read More
FRANCESCO CRICCHIO, CEO, BRAIN & MATTEO CAMPELLONE, EXECUTIVE CHAIRMAN, BRAIN

We present Brain proprietary solutions to some common financial problems using machine learning techniques.

  • The BSR signal is a daily stock ranking based on a supervised machine learning model that uses an ensemble of features related to market regimes, stock fundamentals, prices and volumes, calendar anomalies. The model can be customized with the specific investable universe, the rebalancing frequency and the investment style.
  • Clustering of Market Scenarios: we use unsupervised machine learning to identify which days in the past are similar to current day, according to variables corresponding to a certain topic, e.g. financial stress.
  • Clustering of Funds: Brain developed a platform for monitoring mutual funds. The platform uses unsupervised machine learning techniques to aggregate funds that show a similar behaviour according to a combination of various metrics. Among other things this approach enable the user to detect if a fund behaves too differently from other funds that are supposed to display similar characteristics.

16:30 – CURRENCY AND FOREX FORECASTING WITH MEDIA SENTIMENT DATARead More
ANTHONY LUCIANI, QUANTITATIVE RESEARCHER, MARKETPYSCH

In this talk Anthony Luciani 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.

17:00 – CHAIRMAN CLOSING REMARKS AND CLOSE

PREVIOUS SPEAKERS

Lucas Bruggeman

Partner, Sentifi

Matteo Campellone

Executive Chairman, Brain

Ernie Chan

Hedge Fund Manager, QTS Capital

Giuliano De Rossi

Executive Director, Goldman Sachs

Alexander Eisele

Analytics & Quant Modelling, UBS

Christina Erlwein-Sayer

Professor of Statistics and Financial Mathematics, HTW Berlin

Gregory Gadzinski

Senior Consultant, Panthera Solutions

Claus Huber

Portfolio Manager, Deka Investment

Ronald Hochreiter

Associate Professor for Finance, Webster Vienna Private University

Dan Joldzic

CEO, Alexandria

Chris Kantos

Senior Equity Risk Analyst, Northfield

Charles-Albert Lehalle

Senior Research Advisor, Capital Fund Management & Visiting Researcher, Imperial College

Maurizio Luisi

Investment Research Solutions, AD-IRS

Enza Messina

Professor in Operations Research, University of Milano-Bicocca

Gautam Mitra

CEO, OptiRisk Systems

Panos Parpas

Senior Lecturer, Imperial College

Santanu Paul

CEO, TalentSprint

Anthony Luciani

Quantitative Researcher, MarketPysch

Andreas Petrides

Associate, Goldman Sachs

Dorothy Ruderman

Head of Data Partnerships, StockTwits

Markus Schuller

Founder & Managing Partner, Panthera Solutions

Katharina Schwaiger

Investment Researcher, BlackRock

Peter Schwendner

Professor, ZHAW School of Management and Law

Tharsis Souza

VP of Product Development, Yewno

Erica Stanford

CEO, Crypto Curry Club

Monica Summerville

Director of FinTech Research, Tabb Group

Arun Verma

Quantitative Research Solutions, Bloomberg

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