Background

AI, Machine Learning and Sentiment Analysis Applied to Finance, London, 14 – 15 July 2016

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. This 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.

This conference builds on the findings of the six previous highly-regarded conferences on this topic. It highlights the recent developments in the application of AI and machine learning to trading strategies including automatic and algorithmic trading, quantitative fund management.

Topics to be 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 Retail and Investment Banking

Programme

  • -
    Elijah-DePalma

    Using AI to Integrate Behavioral Insights into Investment Strategies

    Elijah DePalma, Thomson Reuters

    Machine learning algorithms analyze multilingual professional news and unstructured social media to provide meaningful investment signals for both fundamental and quantitative strategies. Furthermore, research applications of News & Social Media Analytics are expanding beyond equities to encompass Global Macro, Systemic Risk, Supply-Chain Economics and FX Markets. We demonstrate the importance of integrating behavioral insights into long-term investment decisions, and we discuss cutting-edge AI applications for short-term trading strategies.

    Speakers:
    Elijah-DePalma

    Elijah DePalma

  • -
    Enza-Messina

    Text and Network Analysis for Sentiment Mining

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

    In this talk we show how social relationships can be managed to improve user-level sentiment analysis of microblogs, overcoming the limitation of the state-of-the-art methods that generally consider posts as independent data. Early approaches consist in exploiting friendship relations, but since two friends could have different opinions about the same topic, it could however be inappropriate to measure sentiment similarity. We show how combining post contents and approval relations may lead to significant improvements in the polarity classification of the sentiment both at post and at user level.

    Speakers:
    Enza-Messina

    Enza Messina

  • -
    Merve-Alanyali

    From news to protests: Measuring human behaviour around the world

    Merve Alanyali, Data Science Lab, Warwick Business School, University of Warwick

    A mammoth amount of data is being generated by our daily interactions with technological devices and online services. Compared to existing approaches, these new forms of data offer faster and cheaper measurements of human behaviour at a global scale. Examples I will showcase range from using online newspapers to quantify the relationship between financial news and the stock market, to analysing photographs shared on Flickr to track protest outbreaks around the world.

    Speakers:
    Merve-Alanyali

    Merve Alanyali

  • -
    peter-hafiz

    Exploiting Alternative Data in the Investment Process

    Peter Hafez,Chief Data Scientist,RavenPack

    The emergence of big data in finance has shifted the alpha focus away from being faster to being smarter than the competition. Access to alternative data sources is considered a key input to such process. Peter Hafez, Chief Data Scientist, will provide an overview of RavenPack’s Big Data Analytics and the future development of the RavenPack product suite. In addition, he will present on his latest work on thematic alpha streams as well as providing an overview of general use cases of RavenPack data across various trading and investment applications.

    Speakers:
    peter-hafiz

    Peter Hafez

  • -
    tilmal-sayer1
    Xiang-Yu

    Beating Markowitz with SSD and Sentiment

    Tilman Sayer,OptiRisk,Xiang Yu,OptiRisk Systems

    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:
    tilmal-sayer1

    Tilman Sayer

    Xiang-Yu

    Xiang Yu

  • -
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    Social listening & financial crowd-intelligence (To be confirmed)

    Anders Bally,Sentifi

    Nearly all online “news” sources, which are the traditional sources we know in the likes of Bloomberg and Reuters, are a fraction of the content that is available on the World Wide Web. The remaining content comes from new media sources including Twitter, YouTube, and Facebook generated by individuals who talk about events as they happen. These millions of voices, when structured, can generate insights which can help investors make investment decisions. This presentation will touch on how Sentifi structures and delivers these insights, providing an information advantage for media platforms globally.

    Speakers:
    dummyimage

    Anders Bally

  • -
    dummyimage

    An Era of Emerging Data: APIs, Unstructured Text, and Crowd Trading (To be confirmed)

    Pierce Crosby, StockTwits

    The digitizing of disparate data has led to a new wave of “emerging data” in the industry, applicable specifically to existing risk and trading models. Translating these data into repeatable results is the new frontier for many asset managers and hedge funds. The discussion will draw specifically from examples of earnings crowdsourcing, satellite imagery forecasting, and social data as it applies to investor sentiment and volatility modeling. We will also cover the diversity of methods used to deliver these data sources and what is considered the new standard for asset managers when it comes to the consumption of data.

    Speakers:
    dummyimage

    Pierce Crosby

  • -
    Enza-Messina

    Text and network analysis for sentiment mining (To be confirmed)

    Enza Messina, University of Milano-Bicocca

    In this talk we show how social relationships can be managed to improve user-level sentiment analysis of microblogs, overcoming the limitation of the state-of-the-art methods that generally consider posts as independent data. We show how combining post contents and network structure information may lead to significant improvements in the polarity classification of the sentiment both at post and at user level.

    Speakers:
    Enza-Messina

    Enza Messina

Speakers

Xiang-Yu

Xiang Yu

OptiRisk Systems

tilmal-sayer1

Tilman Sayer

OptiRisk

peter-hafiz

Peter Hafez

RavenPack

Merve-Alanyali

Merve Alanyali

University of Warwick

James-Cantarella

James Cantarella

Thomson Reuters

derossie

Giuliano De Rossi

Macquarie Research

Elijah-DePalma

Elijah DePalma

Thomson Reuters

Enza-Messina

Enza Messina

University of Milano-Bicocca

Gautam-Mitra

Gautam Mitra

OptiRisk Systems

Tobias-Preis

Tobias Preis

Warwick Business School

dummyimage

Anders Bally

Sentifi

dummyimage

Pierce Crosby

StockTwits

Platinum Sponsors

Gold Sponsor

ravenpack

Bronze Sponsor

marketpsych

Media Partners

automated1
Infosecurity-Magazine

Professional Society Partners

garp
prmia
cqflogo

Knowledge Partner

CAIA-logo

Tickets

Delegates

125 GBP
  • Super Early Bird (For One Day) until 3 June - £125 + VAT
  • Super Early Bird (For Two Days) until 3 June - £195 + VAT
  • Early Bird (For One Day) until 1 July - £195 + VAT
  • Early Bird (For Two Days) until 1 July - £250 + VAT
  • Standard Rate (For One Day) - £275 + VAT
  • Standard Rate (For Two Days) - £375 + VAT
  • Buy Ticket

Vendors

550 GBP
  • Super Early Bird until 3 June - £550
  • Early Bird until 1 July - £650
  • Standard Rate - £750
  • Buy Ticket