Background

AI, Machine Learning and Sentiment Analysis Applied to Finance London

Artificial Intelligence is deemed to be the main driver of the 4th Industrial Revolution. Investment in AI has grown at a phenomenal rate with companies investing $26-39bn in 2016. Adoption in 2017, however, remains low. As a result, this has spurred companies from every industry to seize the trend and innovate – from virtual assistants to cyber security to fraud detection and much more. The majority of C-level executives have identified and agree that AI will have an impact on their industry. However, only 20% of C-level executives admit they have already adopted AI technology in their businesses, according to research conducted by McKinsey. So, there is plenty of scope for change and improvement. The Finance industry is anticipated to lead the way in adoption of AI with a significant projected increase in spending over the next three years.

Until recently, practitioners have faithfully relied upon neo-classical models to measure performance, whether it’s in financial organisations or marketing corporations. AI is the new technology that offers an automated solution to these processes. It has the capability to replicate cognitive decisions made by humans and also remove behavioural bias adherent to humans.

Machine learning and sentiment analysis are specific techniques that are applied in AI. These techniques are maturing and rapidly proving their value within businesses. In order to process and understand the masses of data out there, machine learning and sentiment analysis have become essential methods that open the gateway to data analytics. To keep up with the ever-expanding datasets, it is only natural that the techniques and methods with which to analyse them must also improve and update.

This conference will help you to demystify the buzz around AI and differentiate the reality from the hype. Learn about how you can benefit from the unprecedented progress in AI technologies at this conference. Participants will be presented with real insights on how they can exploit these technological advances for themselves and their companies.

Topics Covered Include:

  • Fundamentals and applications of machine learning and deep learning
  • Pattern classifiers, Natural Language Processing (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
  • Behavioural and cognitive science
  • The future of AI and its impact on industries

Why participate?

  • Hear from leading subject experts from UK, US, Europe and India/Hong Kong
  • Programme includes the latest state-of-the-art research, practical applications and case studies
  • Expect technical and in-depth presentations and discussions; we like to stimulate your brain cells!
  • Excellent networking opportunities throughout the days with all participants, including presenters, investors and exhibitors.

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Programme

  • -

    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

  • -

    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 Hafez

  • -

    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

  • -

    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

  • -

    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

    Xiang Yu

  • -

    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

  • -

    Practical Aspects of Applying Deep Learning for Market Making

    Oded Luria, Data Scientist, Citi Technology Innovation Lab TLV

    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

  • -

    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

  • -

    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

  • -

    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

  • -

    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

  • -

    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

  • -

    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

Speakers

Ashok Banerjee

Professor, Finance & Control, Indian Institute of Management Calcutta

Sanjiv Das

Professor of Finance and Data Science, Santa Clara University

Giuliano De Rossi

Head of European Quantitative Research, Macquarie

Peter Hafez

Head of Data Science, RavenPack

 

Geoff Horrell

Director of Product Incubation at Thomson Reuters

Oded Luria

Data Scientist, Citi Technology Innovation Lab TLV

Enza Messina

Professor in Operations Research, University of Milano-Bicocca

Gautam Mitra

OptiRisk Systems

 

Richard Peterson

CEO, MarketPsych Data

Stephen Pulman

Professor of Computational Linguistics, Oxford University/ TheySay

Tilman Sayer

CIO, Advanced Logic Analytics

Arun Verma

Quantitative Researcher, Bloomberg LP

 

Guillaume Vidal

CEO, Walnut Algorithms

Xiang Yu

OptiRisk Systems

Previous 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

  • 09:55 -

    Introduction to Sponsors

  • 10:00 -

    Coffee

  • 10:30 -
    Sanjiv-Das

    Keynote: Text Mining and Networks for Systemic Risk Measurement

  • 11:15 -
    peter-hafiz

    Keynote: Exploiting Alternative Data in the Investment Process

  • 11:45 -
    Gautam-Mitra
    Xiang-Yu

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

  • 12:15 -

    Lunch

  • 13:15 -
    Arun-Verma

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

  • 13:45 -
    Oded-Luria

    Practical Aspects of Applying Deep Learning for Market Making 

  • 14:15 -
    Giuliano-De-Rossi

    How Machine Learning Can Help Stock Pickers

  • 14:45 -

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

  • 15:30 -

    Tea

  • 16:00 -
    Guillaume-Vidal

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

  • 16:30 -
    Ashok-Banerjee

    Predicting Corporate Default using Text of Corporate Filings

  • 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

  • 10:15 -
    Geoff-Horrell

    Knowledge Graphs and NLP for Asset Management

  • 10:45 -

    Coffee

  • 11:15 -
    Tilman Sayer

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

  • 11:45 -
    Anders-Bally

    Social Listening & Financial Crowd-Intelligence

  • 12:15 -
    Stephen-Pulman

    Keynote: Sentiment Analysis for Fun and Profit

  • 12:45 -

    Lunch

  • 13:45 -
    Richard-Peterson

    Approaches to Market Forecasting with Media Sentiment Data

  • 14:15 -

    Panel Session: Machine Learning and Data Science for Financial Analytics

  • 15:00 -
    Christina-Erlwein-Sayer

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

  • 15:30 -

    Tea

  • 16:00 -
    Holger-Knauer

    The Use of Big Data and Artificial Intelligence for Investment Managers

  • 16:30 -
    Grigorios

    Machine Learning for Tactical Asset Allocation Decisions

  • 17:00 -

    Close of Conference

Previous Speakers

Anders Bally

Sentifi

Ashok Banerjee

IIM Calcutta, India

Sanjiv Das

Santa Clara University, USA

Peter Hafez

RavenPack

Geoff Horrell

Thomson Reuters

Giuliano De Rossi

Macquarie

Christina Erlwein-Sayer

OptiRisk Systems

Holger Knauer

Catana Capital

Oded Luria

Citi Technology Innovation Lab TLV

Enza Messina

University of Milano-Bicocca, Italy

Gautam Mitra

OptiRisk Systems

Grigorios Papamanousakis

Aberdeen Asset Management

Richard Peterson

MarketPsych, USA

Stephen Pulman

Oxford University/ TheySay Analytics

Mandie Quartly

IBM

Tilman Sayer

Advanced Logic Analytics

Arun Verma

Bloomberg LP

Guillaume Vidal

CEO, Walnut Algorithms

Xiang Yu

OptiRisk Systems

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