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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 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, /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) . 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, 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.

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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 , 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 , 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 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 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 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.

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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 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

26 June

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

09:00 - The Rise and Rise of UPI 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 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 Learning 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: üThe broad application of machine learning in finance üExtracting sentiment from textual data such as news stories and social media content using machine learning algorithms üConstruction 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) üRobust 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 finance Panos Parpas, Senior Lecturer, Imperial College üReformulate deep learning as an optimisation problem üDiscuss the importance of stability for robust solutions. üIllustrate the use of deep learning to solve high dimensional (more than 100 dimensions) nonlinear parabolic PDEs (Black&Scholes, Hamilton- Jacobi Belman) üProvide code and some examples for participants to experiment with.

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10:30 - Coffee Break

11:00 - Modelling Intraday Risk and Flow Co-movement to Improve Trading Performance 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 ’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 fluctuations 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 Chair: Enza Messina, Professor in Operations Research, University of Milano-Bicocca

12:00 - How AI, ML & Text Analysis of Alternative Data is Impacting Financial and 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.

12:20 - Social Trading - Developing Signals from Social Sentiment 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, , 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

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Afternoon Chair: Gautam Mitra, CEO, OptiRisk Systems

14:15 - Keynote - Does News Sentiment add Alpha? 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 examples 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 Matteo Campellone, 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. Francesco Cricchio, CEO, Brain & Matteo Campellone, Executive Chairman, Brain We present Brain proprietary solutions to some common financial problems using machine learning techniques. ü 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. üClustering 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. üClustering 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 - Blowing Bubbles: Quantifying how News, Social Media and Contagion Effect Drive Speculative Manias 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

www.unicom.co.uk Speakers’ Profiles Lucas Bruggeman Lucas Bruggeman is an , partner and head of sales and marketing at Sentifi in Zurich. Lucas has more than 20 years of experience in investment and private banking. He worked in Equity Derivatives at Royal of Scotland and ABN AMRO Bank and served as Vice-President of the Swiss Structured Products Association. Prior to joining Sentifi in 2012, he was part of the executive management board at Liechtensteinische Landesbank AG. Originally from the Netherlands, he is living in Zurich with his wife and two sons for 13 years.

Matteo Campellone Matteo is co-founder and Executive Chairman of Brain, a company focused on the development of algorithms for trading strategies and investment decisions. He holds a Ph.D. in Physics and a Master in Business Administration. Matteo’s past activities included Financial Modelling for financial institutions and Corporate Risk and Value Based Management for industrial companies. As a Theoretical Physicist he worked in the field of statistical mechanics of complex systems and of non-linear stochastic equations.

Ernie Chan Ernie Chan is the Managing Member of QTS Capital Management, LLC., a commodity pool operator and trading advisor. Ernie has worked for various investment banks (Morgan Stanley, , Maple) and hedge funds (Mapleridge, Millennium Partners, MANE) since 1997. He received his Ph.D. in physics from Cornell University and was a member of IBM’s Human Language Technologies group before joining the financial industry. He is the author of “Quantitative Trading: How to Build Your Own Business”, “Algorithmic Trading: Winning Strategies and Their Rationale”, and “Machine Trading: Deploying Computer Algorithms to Conquer the Markets”. Find out more about Ernie at www.epchan.com.

Francesco Cricchio Francesco is the co-founder and CEO of Brain, a company focused on the development of algorithms for trading strategies and investment decisions. He obtained his Ph.D. in Computational Methods applied to Quantum Physics from Uppsala University in 2010. He focused his career in solving complex computational problems in different sectors using a wide range of techniques, from density functional theory in solid state physics to the application of machine learning in different industrial sectors.

Giuliano De Rossi Giuliano is an Executive Director in the Quantitative Execution Services team at Goldman Sachs. Prior to this, he worked at Macquarie and PIMCO. He also spent six years in the Quant research team at UBS. Giuliano has a PhD in economics from Cambridge University, and worked for three years as a college lecturer in economics at Cambridge before joining the finance industry on a full-time basis. Giuliano’s Masters degree is from the LSE and his first degree is from Bocconi University in Milan. He has worked on a wide range of topics, including pairs trading, low volatility, the tracking error of global ETFs, cross asset strategies, downside risk and applications of machine learning to finance. His academic research has been published in the Journal of Econometrics and the Journal of Empirical Finance.

Alexander Eisele Alexander Eisele works on analytics, quantitative portfolio construction, and multi-asset trading strategies at UBS AM Investment Solutions. Prior to that, he was a quantitative researcher at UBS AM Products focusing on the analysis, the improvement, and the design of investment products. Before joining UBS in 2015, Alexander was a researcher at the Swiss Finance Institute where he also received a Ph.D. in Finance with a focus on empirical asset pricing. Alexander’s work deals with sentiment-based trading strategies as well as with agency problems in the asset management industry, low volatility strategies, institutional trading behaviour and merger arbitrage strategies. His academic research has been published in the Journal of Financial Economics.

Christina Erlwein-Sayer Christina Erlwein-Sayer is Professor of Statistics and Financial Mathematics at Hochschule für Technik und Wirtschaft (HTW) Berlin. Previous to this, she worked at OptiRisk Systems as a quantitative analyst and senior researcher working on the topic of financial analytics and modelling for portfolio construction and credit risk assessment. Christina completed her PhD in Mathematics at Brunel University, London in 2008. She then worked as a researcher and consultant in the Financial Mathematics Department at Fraunhofer ITWM, Kaiserslautern, Germany. Christina has extensive experience in research and has worked on several R&D projects, the most recent of which was a multi-million pound project funded by EU. She has also led training workshops on the topics of financial modelling, scenario generation and regime detection.

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Gregory Gadzinski Dr. Gregory Gadzinski is Senior Consultant at Panthera Solutions and also a full-time professor of Finance and Economics at the International University of Monaco, teaching a wide range of courses in the DBA, MBA and MFIN programs. He was previously an Assistant Professor of Economics at the Chair for International Economics in Cologne, Germany. Dr. Gadzinski was also a full-time researcher at the Hedge Fund Research Institute in Monaco. His consultancy experience includes mandates at ALPSTAR Management, a multi-strategy hedge fund and at the European , DG Research, Frankfurt, Germany. Dr. Gadzinski has a PhD from the Universite´ de la Me´diterrane´e, France, a postgraduate degree in Mathematical Economics and Econometrics and a “Magiste`reInge´nieurEconomiste” from the University Aix-Marseille II. He has published several scientific articles in prestigious journals such as the Journal of Asset Management, the Journal of Hedge Funds and Derivatives, and the Journal of Investing.

Claus Huber Claus Huber is a Portfolio Manager at Deka Investment LLC, Frankfurt, where he helps to develop new quantitative investment products. As the founder of Rodex Risk Advisers LLC, based in Altendorf (Switzerland), he advised clients on risk management and quantitative investment solutions, for example, a Machine Learning approach to select hedge fund managers. He has extensive experience as entrepreneur, risk manager, credit strategist, hedge fund analyst and government bond and worked for hedge funds, banks and companies.

Ronald Hochreiter Ronald Hochreiter is Principal Investigator of the project ReKlaSat 3D (Reconstruction and Classification of Satellite Images) using contemporary Deep Learning technologies at the WU Vienna University of Economics and Business as well as President and CEO of the Academy of Data Science in Finance (dsf.academy). He is actively teaching Quantitative Finance, Machine Learning and AI at different WU Executive Academy MBA programs as well as various Bachelor and Master programs at the WU Vienna University of Economics and Business, Austria and the University of Bergamo, Italy.

Dan Joldzic Dan Joldzic, CFA, FRM is CEO of Alexandria Technology, Inc, which develops artificial intelligence to analyze financial news. Prior to joining Alexandria, Dan served dual roles as an equity portfolio manager and quantitative research analyst at Alliance Bernstein where he performed factor research to enhance the performance of equity portfolios.

Chris Kantos Chris Kantos is senior equity risk analyst at Northfield. Joining the firm in 2007, Chris now has responsibility for the analytical estimation and data production of all equity risk and transaction cost models of the firm. He is active in numerous investment industry associations including the Chicago Quantitative Alliance, the International Association for Quantitative Finance, and Boston QWAFAFEW. Chris has done public presentations in seven countries including the London Quant Group, the CQA, the IAQF and Northfield events. Mr. Kantos is a magna cum laude graduate of Tufts University in Computer Engineering.

Charles-Albert Lehalle Currently Senior Research Advisor at Capital Fund Management (CFM, Paris) and visiting researcher at Imperial College (London), Charles-Albert Lehalle is a leading expert in market microstructure and optimal trading. Formerly Global Head of Quantitative Research at Crédit Agricole Cheuvreux, and Head of Quantitative Research on Market Microstructure in the Equity Brokerage and Department of Crédit Agricole Corporate Investment Bank, he studied intensively the market microstructure evolution since the financial crisis and regulatory changes in Europe and in the US. He provided research and expertise on this topic to investors and intermediaries, and is often heard by regulators and policy-makers like the European Commission, the French Senate, the UK Foresight Committee, etc. He chairs the Index Advisory Group of Euronext, is a member of the Scientific Committee of the French regulator (AMF), and has been part of the Consultative Workgroup on Financial Innovation of the European Authority (ESMA). Moreover, Charles-Albert received the 2016 Best Paper Award in Finance from Europlace Institute for Finance (EIF) and published more than fifty academic papers and book chapters. He co-authored the book “Market Microstructure in Practice” (World Scientific Publisher, 2nd ed 2018), analyzing the main features of modern markets. With a PhD in machine learning, Charles-Albert is chairing the “Finance and Insurance Reloaded” transverse research program of the Louis Bachelier Institute, this program explores the influence of new technologies (from blockchain to artificial intelligence) on our industries.

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Maurizio Luisi Dr Maurizio Luisi is a quantitative strategist at AD-IRS in London. Previously he worked as Senior Portfolio Manager at Unigestion, focusing on research, development and trading of quantitative investment strategies. Prior to this he worked at Bloomberg, where he was responsible for research and development of the quantitative platform. Beforehand, he worked for six years at RBS, focusing on quantitative and proprietary trading within the Delta One desk. His career started off in the area of quantitative macro research, working for Bank of International Settlements and Goldman Sachs. Maurizio held teaching and research positions at different academic institutions including the Amsterdam Business School, Duisenberg School of Finance and University of Lugano. His research has appeared in leading academic journals, including the Journal of Financial Economics. He holds a Ph.D. in Finance from the Swiss Finance Institute and a master’s degree in Finance from the University of Warwick’s Business School.

Enza Messina Enza Messina is a Professor in Operations Research at the Department of Informatics Systems and Communications, University of Milano-Bicocca, Italy, where she leads the research Laboratory MIND (Models in Decision Making and Data Analysis). She holds a PhD in Computational Mathematics and Operations Research from the University of Milano. Her research activity is mainly focused on decision models under uncertainty and more recently on statistical relational models for data analysis and knowledge extraction. In particular, she developed relational classification and clustering models that find applications in different domains such as systems biology, e-justice, text mining and social network analysis. She is a co-founder of Sharper Analytics, a spin-off from the University of Milano Bicocca.

Gautam Mitra Gautam Mitra is the founder and the MD of OptiRisk Systems. He is an internationally renowned research scientist in the field of Operational Research in general and computational optimisation and modelling in particular. He has developed a world class research group in his area of specialisation with researchers from Europe, UK, USA and India. He has published five books and over hundred and fifty research articles. He is an alumni of UCL and currently a Visiting Professor of UCL. In 2004 he was awarded the title of ‘distinguished professor’ by Brunel University in recognition of his contributions in the domain of computational optimisation, risk analytics and modelling. In OptiRisk Systems he directs research and actively pursues the development of the company as a leader in the domain of financial analytics. Professor Mitra is also the founder and chairman of the sister company UNICOM seminars. OptiRisk systems and UNICOM Seminars also have subsidiaries in India. In India and Southeast Asia both the companies are going through a period of organic growth.

Panos Parpas Dr Parpas is a Senior Lecturer in the Computational Optimisation Group of the Department of Computing at Imperial College London. Before joining Imperial College he was a research fellow at MIT (2009-2011). Before that he was a quantitative associate at Credit-Suisse (2007-2009). He completed his PhD in computational optimization at Imperial College in 2006. He is interested in the development and analysis of algorithms for large scale optimisation problems and exploiting the structure of large scale models arising in applications such as machine learning and finance.

Santanu Paul Santanu Paul is an entrepreneur, technocrat and opinion writer. He is the co-founder and chief executive of TalentSprint, a digital upskilling and bootcamp platform for professionals in search of deep and disruptive skills. The platform aims to equip and empower one million professionals and knowledge workers in the fields of Information Technology, Banking, Financial Services and Education by 2020. Santanu previously worked for Virtusa Corporation as SVP for Global Delivery Operations and Head of Indian Operations, and was part of the global leadership team when the company went public on NASDAQ in 2007. Prior to that he was CTO at Viveca and Openpages, both based in Boston. Santanu received his B.Tech from IIT Madras and his Ph.D. from the University of Michigan, Ann Arbor, both in Computer Science. He is an author or co-author of twenty international technology papers and two United States patents.

Anthony Luciani Anthony Luciani is a Senior Quantitative Analyst at MarketPsych. He is working on simplified sentiment and “Superforecasters” models. He developed sentiment-based financial models, previously for Optirisk. He has a Master’s Degree in Financial Mathematics from the University of Leicester.

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Andreas Petrides Andreas works as a Quantitative Researcher at Goldman Sachs Quantitative Execution Services, with an emphasis in machine learning techniques for execution algorithms. Andreas has a PhD in Information Engineering from the University of Cambridge, focusing on the interface of stochastic control theory and Bayesian machine learning. Andreas’ teaching experience included several engineering undergraduate courses, including Inference and Machine Learning, Linear Algebra, Probability, Control and Signal Processing. During his PhD, he has also worked at Informetis Europe as a Machine Learning Algorithm Engineer, developing efficient Bayesian inference techniques for smart electricity meter applications. Andreas also holds a BA and an MEng degree in Electrical and Information Sciences from Trinity College, University of Cambridge, during which he received the G-Research and The Technology Partnership (TTP) awards. His Master’s thesis was in collaboration with British Cycling, developing a racing cyclist fitness predictor.

Dorothy Ruderman Dorothy Ruderman is the Head of Data Partnerships at StockTwits, where she leads all initiatives around StockTwits’ enterprise data access. She oversees the development of partnerships with asset managers, trading products, analytics and signal companies, as well as over 50 research universities, helping to leverage StockTwits data within the investment decision-making process. Previously she led partnerships at inSided and Kustomer focusing on B2B and B2C partnerships of intelligent community vendors. With over 5 years in social media and social data products, Dorothy has an integral understanding of the social data landscape as well as the products and providers in the space. She is based out of the New York City headquarters, and holds a degree in economics from Lehigh University.

Markus Schuller Markus Schuller is the founder and managing partner of Panthera Solutions. As Investment Decision Architects ™, Panthera optimizes the choice architecture of professional investors through applied behavioral finance methods. Empowering the decision makers towards comparative advantages in capital markets remains the ultimate goal. As adjunct professor, Markus teaches courses like “Adaptive Risk Management”, “Investment Banking” and “Asset Allocation for Practitioners” at renowned Master in Finance programs of the EDHEC Business School and the International University of Monaco. Markus publishes in academic top journals (i.e. Journal of Portfolio Management, 2018), writes articles for professional journals (i.e. CFA Institute, OECD Insights, etc.) and holds keynotes at international investment conferences. As an investment banker, adjunct professor and author, Markus looks back at 18 rewarding years of trading, structuring, and managing standard and products. Prior to founding Panthera Solutions, he worked in executive roles for a long/short equity hedge fund for which he developed the trading algorithm. Markus started his career working as equity trader, derivatives trader and macro analyst for different banks.

Katharina Schwaiger She joined from the European EII (ETF and Index Investments) Product Innovation group, where she was responsible for developing rules-based passive strategies across asset classes for iShares, index mutual funds and segregated mandates. Dr. Schwaiger’s service with the firm dates back to 2013 when she joined as a member of the Risk & Quantitative Analysis (RQA) group. At RQA she was responsible for the risk management and quantitative analysis of Fundamental Equity portfolios in EMEA.

Prior to joining BlackRock in 2013, she has worked as a Financial Engineer in the City of London, as a Quantitative Researcher at a London-based hedge fund and as a lecturer in Operational Research at the London School of Economics. She earned a BSc degree in Financial Mathematics in 2005, and a PhD degree in Mathematics/Operational Research from Brunel University in 2009. She is also the editor of the Asset and Liability Management Handbook (Palgrave, 2011).

Peter Schwendner Peter Schwendner is a Professor at Zurich University of Applied Sciences. His research interests are financial markets, asset management and network analytics. He has 15 years’ work experience in the financial industry as a head of quantitative research at Sal. Oppenheim and as a partner at Fortinbras Asset Management. His specific expertise are correlation analytics and liquid multi- asset products run by systematic models.

Tharsis Souza Tharsis Souza is Vice President of Product Development at Yewno, a Silicon Valley-based start-up that leverages a proprietary Knowledge Graph to offer a variety of AI-augmented products and solutions including index strategies, alternative data feeds and an AI platform for financial services professionals. Tharsis is a product leader with 10 years of experience in technology, quantitative finance, and data science. Tharsis built teams and delivered new products across companies in Latin America, Europe, and the US. Prior to Yewno, Tharsis worked with Product Development and Quantitative Finance in the banking and stock exchange industries, consulted for Hedge Funds and developed research at the Financial Computing & Analytics research group at University College London (UK). Tharsis is an author of scientific publications in financial computing and analytics and is an ad-hoc reviewer of scientific journals such as Science Advances (AAAS) and The Journal of Network Theory in Finance.

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Erica Stanford Erica graduated from the University of Edinburgh with an MA in Economics and has a wealth of experience working in business, marketing and sales strategy. Erica has been in the Blockchain space for a number of years specializing in researching trends and identifying new investment opportunities across the blockchain and emerging tech industries. Erica is currently working on several blockchain platforms and projects – focused on micropayments, digital rights management, donation tracking and especially focused on proving and providing sustainability within supply chain. Erica is the founder of the Crypto Curry Club- a highly sought- after series of fun networking lunches and educational events for leaders in blockchain, crypto and emerging tech www.cryptocurryclub.com and a co-founder of SWcircle, a boutique consulting firm focused on Emerging Technologies.

Monica Summerville Monica Summerville is Director, FinTech Research, and Head of TABB UK. She is an accomplished financial services industry executive with more than 20 years of experience in senior positions on both the buy and sell side, including working for leading consulting firms and as a U.S. retail broker. Her past positions include vice president of front office and market data technology for North America at ABN Amro; senior consultant in the financial services division at PwC Consulting; publisher and executive editor at RiskWaters Group; director at Jordan & Jordan; broker services at J&W Seligman Investment Management.

Arun Verma Dr. Arun Verma joined the Bloomberg Quantitative Research group in 2003. Prior to that, he earned his Ph.D from Cornell University in the areas of computer science & applied mathematics. At Bloomberg, Mr. Verma’s work initially focused on Stochastic Volatility Models for Derivatives & Exotics pricing and hedging. More recently, he has enjoyed working at the intersection of diverse areas such as data science (for structured & unstructured data), innovative quantitative & machine learning methods and finally interactive visualizations to help reveal embedded signals in financial data.

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4 people attend for the price of 3

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Very Super Early Bird until 29 March £195 + VAT

Super Early Bird until 15 April £300 + VAT End user Early Bird until 10 May £400 + VAT

Standard Rate £550 +VAT

Very Super Early Bird until 29 March £400 + VAT

Super Early Bird until 15 April £500 + VAT Vendor / Consultant Early Bird until 10 May £600 + VAT

Standard Rate £750 +VAT

Combined discounted price

For a combined discounted price with the conference “Financial Evolution: AI, Machine Learning and Sentiment Analysis”, please contact [email protected] or [email protected]

Contact

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