Background

AI, Machine Learning and Sentiment Analysis Applied to Finance, London, 28 – 29 June 2017, Millennium Hotel London Mayfair 

sentiment-logo

Artificial Intelligence, Machine Learning and Sentiment Analysis are changing the way in which numerous client services are offered. In particular, Financial Organisations are creating and leveraging such innovation in the domain of wealth management. This trend is now being taken on board by multiple innovators: academia, start-ups, technology companies and financial market participants.

AI and Machine Learning have emerged as a central aspect of analytics which is applied to multiple domains. AI and Machine Learning, Pattern classifiers and natural language processing (NLP) underpin Sentiment Analysis (SA); SA is a technology that makes rapid assessment of the sentiments expressed in news releases as well as other media sources such as Twitter and blogs.

The conference addresses and explains how to extract sentiment from these multiple sources of information and showcases the advances that have taken place in the field of financial innovation. The focus is on the latest research and includes presentations by leading subject experts from all over the world, as well as a number of applications/case studies.

Topics Covered Include:

  • Pattern classifiers, NLP and AI applied to data, text, and multi-media
  • Sentiment scores combined with neo-classical models of finance
  • Financial analytics underpinned by qualitative and quantitative methods
  • Predictive and normative analysis applied to finance
  • Applications in Financial Markets

This program qualifies for 14 GARP CPD credit hours. If you are a certified Financial Risk Manager (FRM®), please record this activity in your Credit Tracker.

Programme

  • 08:30 -

    Registration and Coffee

  • 09:00 -

    Introduction and Welcome– Professor Gautam Mitra, OptiRisk Systems/UCL (Programme Chair)

  • Morning Session Chairperson: Ashok Banerjee, Indian Institute of Management Calcutta -

  • JOINT PLENARY SESSION -

  • 09:15 -
    Mandie-Quartly

    PLENARY KEYNOTE: Data Science and Machine Learning Applied to Business Analytics: Financial and Retail Markets Use Cases

    Mandie Quartly, Worldwide Lead, Machine Learning and High Performance Analytics software, IBM

    There’s a lot of talk about using Artificial Intelligence (AI) to gain business insights, but there are a number of key ingredients required to actually make it happen in a timely fashion. A vital element is the technology which underpins AI and machine learning applications. Come and hear more about "making it happen" for your organisation; learn how to take advantage of rapidly evolving and innovating technologies. The emphasis is very much on real world use cases encompassing retail and financial markets.

    Speakers:
    Mandie-Quartly

    Mandie Quartly

  • 09:55 -

    Introduction to Sponsors

  • 10:00 -

    Coffee

  • 10:30 -
    Sanjiv-Das

    Keynote: Text Mining and Networks for Systemic Risk Measurement

    Sanjiv Das, Professor of Finance and Data Science, Santa Clara University

    Information is extracted from big textual data to create a single risk score for the financial system. To do this network analysis is overlaid with mathematical theory to create a systemic risk monitoring dashboard. I will discuss implementations in the US and India. Extensions to stochastic networks will also be presented. 

    Speakers:
    Sanjiv-Das

    Sanjiv Das

  • 11:15 -
    peter-hafiz

    Keynote: Exploiting Alternative Data in the Investment Process

    Peter Hafez, Head of Data Science, RavenPack

    The emergence of big data in finance has shifted the alpha focus away from being faster to being smarter and more efficient than the competition. Access to alternative data sources is considered a key input to such a process. During his talk, Peter Hafez will provide an overview of the changing investment landscape and provide the “winning formula” for successful quant investing. He’ll discuss issues related to unstructured content, crowd-sources alpha, and proprietary vs. public content.

    Speakers:
    peter-hafiz

    Peter Hafez

  • 11:45 -
    Gautam-Mitra
    Xiang-Yu

    Daily Trade Signals Using Sentiment Analysis and Stochastic Dominance for Downside Risk Control

    Xiang Yu, Chief Business Development Officer, and Gautam Mitra, CEO/Director, OptiRisk Systems/UCL

    We have created an innovative and dynamic trading strategy for equities, with a particular focus on controlling downside risk. The mathematical concept behind the approach is called stochastic dominance, where investment decisions are based on distributions rather than moments. A major contribution of news sentiment is in the prediction of future distributions. Regression analysis on news sentiment and regime switching models are employed to digest market moods and account for changing market situations.

    Speakers:
    Gautam-Mitra

    Gautam Mitra

    Xiang-Yu

    Xiang Yu

  • 12:15 -

    Lunch

  • 13:15 -
    Arun-Verma

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

    Arun Verma, Quantitative Researcher, Bloomberg LP

    ♦ 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

    Arun Verma

  • 13:45 -
    Oded-Luria

    Practical Aspects of Applying Deep Learning for Market Making 

    Oded Luria, CITI

    Deep Learning has been shown to outperform traditional methods in many learning tasks such as image and voice recognition, but its role in processing financial datasets is yet to be fully discovered. In this talk, Oded shares practical insights about applying Deep Learning for different aspects of market making and discusses some of the unique challenges and tradeoffs of this field.

    Speakers:
    Oded-Luria

    Oded Luria

  • 14:15 -
    Giuliano-De-Rossi

    How Machine Learning Can Help Stock Pickers

    Giuliano De Rossi, Head of European Quantitative Research, 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

    Giuliano De Rossi

  • 14:45 -

    Panel Session: News, Social Media and Sentiment for Financial Analytics

    Moderator: Gautam Mitra; Panellists: Ashok Banerjee, Simon Bigg, Sanjiv Das, Giuliano de Rossi, Peter Hafez

  • 15:30 -

    Tea

  • 16:00 -
    Guillaume-Vidal

    The Application of AI to Quantitative Systematic Strategies, Opportunities and Risks

    Guillaume Vidal, CEO, Walnut Algorithms

    ♦ How machine learning fits into systematic strategies
    ♦ What are the pros and cons of using machine learning in quant finance
    ♦ Building an infrastructure that enables machine learning research within the company
    ♦ Debunking the myth of superintelligence in finance

    Speakers:
    Guillaume-Vidal

    Guillaume Vidal

  • 16:30 -
    Ashok-Banerjee

    Predicting Corporate Default using Text of Corporate Filings

    Ashok Banerjee, Professor, Finance & Control, Indian Institute of Management Calcutta

    Banks and financial institutions in emerging markets are saddled with a huge proportion of bad loans. Banking regulations require lenders to provide for troubled debt which adversely affects the profitability of banks. The capital market also reacts negatively to such write-offs of big ticket debts. Banks are, therefore, putting significant resources into developing early warning signals to arrest eventual default. The financial institutions use a wide range of default prediction models to estimate the loan loss. These models use data from financial statements and the market. The present study shows that such models fail to provide effective early warning signals. We use annual reports of companies to develop a default model which is predictive and hence has the capability of providing early warning signals. Using information from Directors’ Reports, Audit Reports and notes to accounts, our model successfully discriminates the ‘good’ firms from the ‘bad’ ones.

    Speakers:
    Ashok-Banerjee

    Ashok Banerjee

  • 17:00 -

    Chairman’s Closing Remarks

  • 17:15 -

    Drinks Reception and Networking

  • 09:30 -

    Welcome and Introduction to Day 2– Professor Gautam Mitra, OptiRisk Systems/UCL

  • 09:45 -
    Enza-Messina

    Deep Learning for Sentiment Analysis

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

    We show how deep learning and ensemble methods can successfully address challenging problems arising in sentiment analysis such as irony detection or domain adaptation. In particular, we propose an unsupervised framework for domain-independent irony detection built upon an existing probabilistic topic model initially introduced for sentiment analysis purposes. Moreover, in order to improve its generalization abilities, we apply Word Embeddings to obtain domain-aware ironic orientation of words. The acquisition of cross-domain high level feature representations through word embeddings combined with the generalization capability of ensemble methods can also be used for addressing the problem of domain adaptation also in the scenario where the testing target domain is completely unlabeled.

    Speakers:
    Enza-Messina

    Enza Messina

  • 10:15 -
    Geoff-Horrell

    Knowledge Graphs and NLP for Asset Management

    Geoff Horrell, Director of Product Incubation at Thomson Reuters

    How can asset managers deliver insight using NLP and Knowledge Graphs? Data Lakes, Data Science, AI, Graphs, Machine Learning are all getting massive attention. How should asset managers approach this area to ensure that business value isn’t lost in the mix of the technology hype?

    Speakers:
    Geoff-Horrell

    Geoff Horrell

  • 10:45 -

    Coffee

  • 11:15 -
    Tilman Sayer

    Putting Big Data, Advanced Analytics and Break-Through Trading Strategies To Work in the Financial Markets

    Tilman Sayer, CIO, Advanced Logic Analytics

    The classic Markowitz model considers the standard characteristics: return and volatility. Second order stochastic dominance (SSD) in contrast encompasses the whole distribution of asset returns.

    The true magic of SSD lies in its choice of portfolio based on the minimisation of downside tail risk. Using this modelling paradigm we have developed an innovative and dynamic trading product for equities. News sentiment is integrated into the system to digest market moods and enhance prediction. Regime switching algorithms are used to detect market shifts. We provide insight into these novel techniques and supply performance results.

    Speakers:
    Tilman Sayer

    Tilman Sayer

  • 11:45 -
    Anders-Bally

    Social Listening & Financial Crowd-Intelligence

    Anders Bally, CEO and 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

    Anders Bally

  • 12:15 -
    Stephen-Pulman

    Keynote: Sentiment Analysis for Fun and Profit

    Stephen Pulman, Professor of Computational Linguistics, Oxford University/ TheySay

    A non-technical overview of our work over the last ten years in sentiment analysis and related techniques. I'll also describe various practical applications of these technologies, some successful, some less so, in a variety of different areas: sports gambling, politics, conversational agents, health care monitoring, and financial market prediction.

    Speakers:
    Stephen-Pulman

    Stephen Pulman

  • 12:45 -

    Lunch

  • 13:45 -
    Richard-Peterson

    Approaches to Market Forecasting with Media Sentiment Data

    Richard Peterson, CEO, MarketPsych Data

    Dr. Peterson will describe the unique characteristics of media sentiment data and approaches to financial price prediction with this data. The basics of media sentiment data, various modeling approaches, and their results (including live trading results) will be described in this talk. Viewers will gain an understanding of real-world modeling tips and techniques when dealing with noisy and inconsistent data such as media sentiment streams.

    Speakers:
    Richard-Peterson

    Richard Peterson

  • 14:15 -

    Panel Session: Machine Learning and Data Science for Financial Analytics

    Moderator: Richard Peterson; Panellists: Anders Bally, Geoff Horrell, Holger Knauer, Enza Messina, Grigorios Papamanousakis, Arun Verma

  • 15:00 -
    Christina-Erlwein-Sayer

    SENRISK – Sentiment of News and Market Analysis of Sovereign and Corporate Bonds for Credit Risk Assessment

    Christina Erlwein, Senior Quantitative Analyst and Researcher, OptiRisk Systems

    We report the outlook of a credit risk assessment tool, in which sentiments of news and social media are included in the risk assessment of corporate and sovereign bonds. Our tool investigates all current country and company information and market sentiment as well as historical time series to enable a quantitative as well as qualitative analysis of bonds’ inherent risk.

    Speakers:
    Christina-Erlwein-Sayer

    Christina Erlwein-Sayer

  • 15:30 -

    Tea

  • 16:00 -
    Holger-Knauer

    The Use of Big Data and Artificial Intelligence for Investment Managers

    Holger Knauer, CEO/CRO, Catana Capital

    ♦ How to use Big Data and A.I. to build predictive models and better analyse structured and unstructured data
    ♦ How to interpret big data to view a real-time gauge of financial markets
    ♦ Assessing how far big data’s capabilities will impact, reshape and benefit world economies

    Speakers:
    Holger-Knauer

    Holger Knauer

  • 16:30 -
    Grigorios

    Machine Learning for Tactical Asset Allocation Decisions

    Grigorios Papamanousakis, Deputy Head, Systematic Asset Solutions, Aberdeen Asset Management

    On this presentation we describe how we use Machine learning for forecasting the relative performance of various asset classes (rates, credit, equities, commodities, etc.) from an asset management perspective. How we define the question to the machine based on different client risk profiles, performance targets and machine learning algorithms. We finally emphasize on the market factors selection, data cleansing, signal processing and high performance computing.

    Speakers:
    Grigorios

    Grigorios Papamanousakis

  • 17:00 -

    Close of Conference

Speakers

Anders-Bally

Anders Bally

Sentifi

Ashok-Banerjee

Ashok Banerjee

IIM Calcutta, India

Sanjiv-Das

Sanjiv Das

Santa Clara University, USA

peter-hafiz

Peter Hafez

RavenPack

Geoff-Horrell

Geoff Horrell

Thomson Reuters

Giuliano-De-Rossi

Giuliano De Rossi

Macquarie

Christina-Erlwein-Sayer

Christina Erlwein-Sayer

OptiRisk Systems

Holger-Knauer

Holger Knauer

Catana Capital

Oded-Luria

Oded Luria

Citi Technology Innovation Lab TLV

Enza-Messina

Enza Messina

University of Milano-Bicocca, Italy

Gautam-Mitra

Gautam Mitra

OptiRisk Systems

Grigorios

Grigorios Papamanousakis

Aberdeen Asset Management

Richard-Peterson

Richard Peterson

MarketPsych, USA

Stephen-Pulman

Stephen Pulman

Oxford University/ TheySay Analytics

Mandie-Quartly

Mandie Quartly

IBM

Tilman Sayer

Tilman Sayer

Advanced Logic Analytics

Arun-Verma

Arun Verma

Bloomberg LP

Guillaume-Vidal

Guillaume Vidal

CEO, Walnut Algorithms

Xiang-Yu

Xiang Yu

OptiRisk Systems

Platinum Sponsor

Bronze Sponsor

FTRI

Other Sponsors

sentifi
MarketPsych
ala

Media Partners

enterprise-management-logo
automated-trader
mindcmrce
datafloq

 

finmaps
Quirks
MoneyScience
alpha-journal

 

Financial-IT
bt
datanami
7wData

Professional Society Partners

cqflogo
garp
SPSlogo

Knowledge Partners

optirisk
ucl
CAIA-logo
walnut

Tickets

4 people attend for the price of 3

  • Use the coupon code "SA443" when booking.

End User

225 GBP
  • Super Early Bird until 28 April - £225
  • Early Bird until 19 May - £350
  • Standard Rate - £500
  • Buy Ticket

Vendors and Consultants

595 GBP
  • Super Early Bird until 28 April - £595
  • Early Bird until 19 May - £695
  • Standard Rate - £795
  • Buy Ticket

For combined price of the conference attendance and the workshop/workshops, please email to info@unicom.co.uk

Event Venue

  • Millennium Hotel London Mayfair
    44 Grosvenor Square
    London W1K 2HP
  • +44 (0) 1895 256 484
  • info@unicom.co.uk.
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