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Financial Evolution AI Machine Learning Sentiment Analysis London Gold Sponsor Other Sponsors Knowledge Partner Supporting Bodies Media Partners www.unicom.co.uk 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. üLearn how you can benefit from the unprecedented progress in technological advances for yourself and your company üFind out about the impact of Quantum Computing and Alternative Data üBenefit from the experience of world class presenters from the UK, US, Europe and India/Hong Kong üGain exclusive insights into pioneering projects in AI, Machine Learning & Sentiment Analysis in Finance üProgramme includes the latest state-of-the-art research, practical applications and case studies üEnjoy excellent networking opportunities throughout the days with all participants, including presenters, investors and exhibitors. 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. www.unicom.co.uk Programme 25 June 08:45 - Morning Chair: Gautam Mitra, CEO, OptiRisk Systems & Visiting Professor, UCL 09:00 - Advances in Factor Investing 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 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. üWhy more talk than walk? üWhat are the benefits of applying Behavioral Finance insights? üHow to overcome the knowing-doing gap? 10:00 - Social Listening and Financial Crowd Intelligence 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 Flow 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 Portfolios Gautam Mitra, CEO, OptiRisk Systems & Visiting Professor, UCL üFiltering asset universe üRSI, NewsRSI (NRSI), DerivedRSI (DRSI) üResults NIFTY 50, S&PaLash 500 üMachine Learning (ML) to predict market movements (mini regimes) üFeature Modelling üResults NIFTY 50, S&P 500 www.unicom.co.uk Programme 12:15 - Panel: Alternative Data Moderator:Alexander Eisele, Analytics & Quant Modelling, UBS Panellists: 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 Analysis 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 Strategies 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 Analysis 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 Sentiment 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 investing 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. www.unicom.co.uk Programme 16:45 - 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
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