20 - 21 August, 2014
JW Marriott, Hong Kong, SAR
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  • DAY ONEDAY TWOMasterclass B

    Big Data Masterclass: Led By Tobia Preis and Helen Susannah Moat

    Masterclass A - 29th August

    About the Masterclass Day:


    For our masterclasses we bring you three of the world's top experts in the field of low latency, Haim Bodek, Tobias Preis and Dr Susannah Moat. Tobias's and Sussannah's masterclass gives you an insight into big data and how you can apply big data techniques in your organisation whilst Haim Bodek's masterclass will provide you with critical information and practical advice when designing low latency architectures. The masterclasses are seperately bookable, although the content of each one complements the other.

    Aim of Masterclass A - Using Big Data techniques to reveal patterns from vast amounts of data to predict trading behaviour:
    Technology is becoming ever more deeply interwoven into the fabric of society, transforming financial trading. The digital traces left behind by our interactions with technology form extensive behavioural data sets, offering unprecedented potential for a better understanding of collective decision making in financial markets.

    This masterclass outlines key principles and concepts in big data analytics in the financial trading context as well as related areas. It covers a range of examples of intelligence based on big data, including prediction of crime, measurement of responses to natural disasters, prediction of elections and disease outbreaks, as well as anticipation of economic and financial instability.

    The module aims to encourage attendees to see how digital traces of human activity can be used to anticipate financial events and provides awareness and understanding of how big data can provide crucial new insights into collective human behaviour. The module illustrates how to build trading algorithms based on big data signals.

    This masterclass, led by two of the world’s leading experts in big data analytics, will pass on financial trading focused knowledge of mining, processing, analysing, and visualising large financial and complementary data sets.


    08.30 Registration

    09:00 - Session 1 : Mining Big Data
    Big data and data mining of publicly available information including online activity and news

    09:45 - Session 2: Visualising Big Data
    Visualising big data sets and their relationship to price movements

    10.30 - Morning Coffee

    11:00 - Session 3: Analysing Big Data
    Analysing large data sets to nowcast or even forecast events such as financial market moves

    11:45 - Session 4: Building Predictive Systems Using Big Data
    Bringing the skills of data science together to build big data driven trading algorithms

    12.30 Q&A and Close

     Big Data Masterclass



     About Your Masterclass Leaders
    Tobias Preis

    Tobias Preis is an Associate Professor of Behavioural Science and Finance at Warwick Business School. His recent research has aimed to carry out large scale experiments on complex social and economic systems by exploiting the volumes of data being generated by our interactions with technology.

    In 2010, Preis headed a research team which provided evidence that search engine query data and stock market fluctuations are correlated. In 2012, Preis and his colleagues Helen Susannah Moat, H. Eugene Stanley and Steven R. Bishop used Google Trends data to demonstrate that Internet users from countries with a higher per capita GDP are more likely to search for information about the future than information about the past.

    He is founder and CEO of Artemis Capital Asset Management GmbH, a company which develops methods and tools for predictive analytics. He was awarded a Ph.D. in physics from the Johannes Gutenberg University of Mainz in Germany.


    Susannah Moat

    Helen Susannah Moat is a Senior Research Fellow in Behavioural Science at Warwick Business School, and also holds an honorary position in the Department of Physics, Boston University.

    Moat’s research focuses on how information flows between people and affects their behaviour, exploiting online data to anticipate real world actions. Her expertise covers a range of areas needed to make full use of big data, including computer science, linguistics, behavioural science and statistics for large data sets.


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