To see the first four rounds of conference speakers we announced, check the Data Natives 2015 website!

Data Natives, a first-of-its-kind conference for the data-driven generation, is set to bring together the brightest minds in data science and related technologies for a 2-day event in Berlin November 19-20.

What’s on the agenda

Data Natives features a packed agenda designed for those looking to push the boundaries of their ideas and work in data science and beyond. With 40+ industry talks and unlimited networking opportunities, the conference aims to break down barriers in the industry and forge lasting relationships among its participants. Talks will focus on three buzzing areas of data science – Machine Learning, Internet of Things and FinTech.

The conference will also host a Startup Battle, showcasing some of the most exciting companies poised to do big things in big data. Young startups will have the chance to pitch to influencers and investors in the tech community and compete for the prize of Best Big Data Startup in 2015.

Confirmed Speakers

Louisa Heinrich, Founder at Superhuman

Louisa is all about making connections for humans – with other people, with content, with products, services, brands and businesses. She believes that enabling and empowering people to do the things they need and love to do is the only way to achieve sustainable success. Her company, Superhuman Limited, works with businesses and governments to design strategies, products, services and organisational structures that use digital technology to improve individual lives, make a positive contribution to society, and achieve commercial results. She has been working in the consumer-facing digital world more or less as long as there’s been such a thing. She’s held many titles over the past 20 years, including Design Director in the first dotcom boom, Executive for Future Platforms at the BBC, and Head of Strategy for international Service Design agency Fjord.

Suzy Moat, Assistant Professor of Behavioural Science at Warwick Business School

Suzy Moat is an Associate Professor of Behavioural Science at Warwick Business School, where she co-directs the Data Science Lab. Her research investigates whether data on our usage of the Internet, from sources such as Google, Wikipedia and Flickr, can help us measure and even predict human behaviour in the real world. Moat’s work touches on problems as diverse as anticipating stock market moves (with Preis, Curme, Stanley, et al.), estimating crowd sizes (with Botta and Preis) and evaluating whether the beauty of the environment we live in affects our health (with Seresinhe and Preis). The results of her research have been featured by television, radio and press worldwide, by outlets such as CNN, BBC, The Guardian, Wall Street Journal, New Scientist and Wired. Moat studied Computer Science at UCL, where she was awarded the Faculty of Engineering Medal, and received a PhD in Psychology from the University of Edinburgh. With her collaborator and Data Science Lab co-director Tobias Preis, she recently led an online course on using big data to measure and predict human behaviour which attracted over 15,000 learners. Suzy has also acted as an advisor to government and public bodies on the predictive capabilities of big data.

Matthias Korn, Technical Consultant at datavirtuality

Matthias Korn, Technical Consultant at datavirtuality. His talk, “Beyond the Data Lake” will talk about the shift in digital era, which harnesses large amounts of data to make astute business decisions and improve operations, which is now an imperative. While our ability to generate data still far outstrips our ability to analyze it, we are making strides. Exciting new approaches are merging big data solutions with traditional enterprise data strategies. Logical data warehouses, in which there is no single data repository, hold enormous promise. By offering an ecosystem of multiple best-fit repositories, technologies, and tools, business can effectively analyze data for powerful insight.

Duena Blomstrom, FinTech & Innovation Consultant

Duena is an independent Digital Banking consultant, FinTech specialist, an entrepreneur and an Angel Investor, a mentor for Startupbootcamp and Techstars, a blogger with cutting edge opinion style, a public speakers at industry events, the inventor of the EX concept and for the past 18 years has been in the the Telco and the Finance world on the strategy and consulting side, be it for sales or marketing. Most recently, Duena has been the Head of Sales and Marketing for Meniga from when it was a tiny Icelandic start-up to winning Finovate 3 times and becoming market leader in customer engagement in finance and have therefore “seen it all” in digital. With a background in Psychology as well as Business, Duena is on a crusade to teach the industry that Big-4-wooden-language will get us nowhere; she is passionate about getting FIs to think of the concept of “Emotional Banking” or how to stop thinking feature set and start thinking customer’s feelings; and she is interested in all things at the intersection of technology, innovation and behavioural science.

Interested in sponsoring opportunities? Please get in touch at info@datanatives.io

Are you game for the Big Data event of the year? Mark your calendars now, and snag your tickets here!

Weeks before the release of their paper, “Quantifying the semantics of search behavior before stock market moves”, Dataconomy met with Dr. Suzy Moat and Dr. Tobias Preis to discuss their research on predicting the stock market by analyzing Google and Wikipedia searches. The initial two studies — which asked the question “Is there a relationship between what people are looking for on Google and Wikipedia and subsequent stock market moves?” — was released in April 2013, and received considerable media attention. Now, the two researchers, along with H. Eugene Stanley and Chester Curme, have come out with a follow-up study that seeks to look at their original findings from a different angle — essentially, which particular topics (Politics, Food, Sports) might have a relationship to stock market moves?

The Initial Study: Google Trends, Wikipedia and Anticipating Stock Market Moves

The research from the two professors postulates that a historical analysis of key search terms on Google can predict how stock prices will rise and fall in the coming weeks.

After selecting 98 keywords, ranging from “debt” to “housing,” Dr. Preis and Dr. Moat analyzed whether changes in search volumes for a particular term bore any relationship to changes in stock prices in the subsequent weeks. By managing a theoretical Dow Jones industrial average index-weighted portfolio from 2004 to 2012, they found that changes in searches for terms that were financially related (based on their prevalence on the Financial Times over the eight-year span) — such as, “debt”, “NASDAQ”, “stocks” – displayed a significant relationship to subsequent stock market movements:

“We investigated whether there were more searches for a given term this week, compared to previous weeks. If we found an increase in search volume, we sold the market in the coming week.. And vice versa; if the number of people looking for a certain keyword went down, then we bought the market,” Dr. Preis told Dataconomy.

By employing this “Google Trends Strategy,” Dr. Moat and Dr. Preis saw a 326% investment return based on how often the term “debt” alone was searched — compared to the 6.2% return (after fees) hedge funds generated for their investors in 2012.

Similar results were found by analysing changes in interest in Wikipedia pages, for example pages relating to companies listed in the Dow Jones, or general economic concepts such as “capital”, “wealth”, and “macroeconomics”. Again, the financial relevance of these pages appeared important: data on how often people viewed pages relating to actors and filmmakers was found to be of no value in developing a trading strategy.

The Follow-Up Study: Quantifying Meaning with Wikipedia

To get a better understanding of a search term’s relevance for the stock market, the two researchers worked with Chester Curme, a Research Fellow in the Data Science Lab at Warwick Business School. Their aim was to determine which topics people searched for before stock market moves.

In essence, Curme used an algorithm known as Latent Dirichlet Allocation, or LDA, to parse Wikipedia and identify which words turn up in articles with other words. In so doing, he was able to define the meaning of a word based on its relationship to others and identify which significant “topics,” or groups of words, had a positive correlation with subsequent stock market moves. The researchers were now able to recognise which topics, beyond financial ones, might also perform similarly well with their initial strategy.

The findings showed that terms the algorithm identified as business-related performed well with the researchers’ strategy, as they had already established. What’s interesting, however, was that they also found that terms relating to politics, especially U.S. politics, also had a positive correlation to stock market moves. So it appears that it is not only an increase in searches for financial terms, but also political ones, that had a significant relationship to ensuing stock market activity.

As Dr. Moat describes, “By mining these datasets, we were able to identify a historic link between rises in searches for terms for both business and politics, and a subsequent fall in stock market prices. No other topic was linked to returns that were significantly higher than those generated by randomly buying and selling. The finding that political terms were of use in our trading strategies, as well as more obvious financial terms, provides evidence that valuable information may be contained in search engine data for keywords with less obvious semantic connections to events of interest. Our method provides a new approach for identifying such keywords.”

Implications?

What can we do with such predictions besides outperforming the stock market? Dr. Preis and Dr. Moat hope that their work will help us understand “how we can better forecast patterns in complex human behaviour, and perhaps better control certain aspects of this complexity” “We are not only interested in financial behaviour,” Dr. Preis reminded me throughout the interview.

As such, Dr. Moat and Dr. Preis’s work is more of a behavioral science experiment than a real life strategy for stock market investment, as they are both ready to point out: “We are not suggesting that the strategy we describe in these papers will continue to generate profit in the future.”

Instead, their research is more about using online data as an additional resource – alongside traditional surveys, censuses, opinion polls, etc. – to identify patterns and make predictions about everything from the stock market, to natural disaster prevention and even traffic flow.

“By our everyday use of Google, Twitter, Flickr and other online services, we are generating massive records of human behaviour,” explains Dr. Preis. “Now, more than ever, it is possible to analyse human collective experience with a computer. With these calculations, we can improve our understanding of the probability with which certain events may occur in the future.”

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(Image Credit: Jason Devaun)