Last Thursday, we hosted a href="http://marketing.streambase.com/acton/form/1262/0026:d-0001/0/index.htm"

target="_self">webinar - "Trading on Macroeconomic

News" with href="http://www.ntkn.com/?option=com_content&task=view&id=42&Itemid=54">Need

to Know News (NTKN), a subsidiary of Deutsche Bourse

Group Company. With pre-built connectivity to NTKN's AlphaFlash,

we demonstrated how a trading firm could easily incorporate

low-latency news algo data feeds into their trading systems. In

the 50-minute webinar, we built a basic algo that will trade

EUR/USD based on the data of non-farm payroll employment

report.

Trading on unstructured or non-traditional

data is a topic that is gaining more and more attention. Adam

Honoré (

target="_self">@adhonore) at Aite Group

reported that 35% of quant firms are now exploring

machine-readable news feeds in some way, compared to only 2% in

2008. The development of electronic trading and explosion in data

volumes over the past decade play key roles in this phenomenon.

Yet, most market participants are still trying to figure out

effective and efficient ways of transforming unstructured data

into increased profits or prevented

losses.

One fund,

href="http://www.pri.org/stories/business/hedge-fund-uses-twitter-to-profitably-predict-market-swings5894.html">Derwent

Capital has reported success already,

announcing a 2% profit from its $40 million dollar fund, with

investment strategies based on Twitter mood - derived from a

href="http://streambase.typepad.com/streambase_stream_process/#http://economix.blogs.nytimes.com/2010/10/19/using-twitter-to-predict-stock-market-swings/?partner=rss&emc=rss">paper

published by Johan Bollen and Huina Mao at Indiana University,

Bloomington, and Xiao-Jun Zeng at the University of Manchester.

In 2009, Zhi Da and Pengjie Gao at Notre Dame and Joseph

Engelberg of UNC also proposed href="http://www.smartmoney.com/invest/stocks/which-stocks-will-rise-ask-google-1297982065387/">correlation

between Google

Search Volume Index (SIV) and stock

prices.

Here at

StreamBase, we were one of the first financial technology firms

to offer clients a way to connect to unstructured social media

content, such as href="http://www.streambase.com/9bd00b4c-a8b1-4260-9751-65d5abad13c5/press-release-detail.htm">Twitter.

Media interest in this area has been high, with coverage from

href="http://video.cnbc.com/gallery/?video=1178221977">CNBC,

the href="http://www.ft.com/intl/cms/s/0/ff39ba8a-a2ef-11de-ba74-00144feabdc0.html?nclick_check=1#axzz1YOsCdp2I">FT,

the WSJ, and href="http://www.usatoday.com/money/perfi/stocks/2011-05-03-wall-street-traders-mine-tweets_n.htm">USA

Today; though enabling customers to trading on social

media, is really only a small percentage of what we do. The

scalability and flexibility of StreamBase CEP makes it a great

platform href="http://streambase.typepad.com/streambase_stream_process/2009/06/on-ibm-unstructured-data-and-cep.html">to

handle unstructured data in

real-time.

Leading economic indicators, such as GDP,

consumer confidence index, interest rates and unemployment

reports are commonly used for signal generation but a firm needs

to href="http://streambase.typepad.com/streambase_stream_process/2009/06/finding-alpha-be-fast-be-smart-or-be-dirty.html">make

extra efforts to really gain a competitive edge with unstructured

data. Simply categorizing news into positive, neutral

or negative might not be sufficient enough for effective

algorithmic trading. In 2010 we demonstrated how StreamBase can

be used with a href="http://www.streambase.com/webinar-trading-on-the-news-reg.htm">LingPipe,

a natural language processing tool kit that can understand the

implications of a news story vs. market

expectation.

Although

it is rare to see signal generation or alpha seeking strategies

solely based on news events, some sophisticated firms incorporate

unstructured data and sentiment analysis to develop algos that

react to market volatility after a particular news item

breaks.

Processing of unstructured data has

relevance not only for alpha-generating strategies but also for

risk managers and regulators who could use machine-readable news

and sentiment analysis to simulate a firms' exposure or market

fairness under extreme events, for example, href="http://online.wsj.com/article/SB10001424053111904106704576581133063805062.html?mod=WSJ_hp_LEFTTopStories">the

recent downgrade of government credit

ratings.

Trading on unstructured data will continue

to be a popular topic. If you have any new ideas for using other

non-traditional data for trading, please let us know. Below are

some additional links which might prove

interesting.

/>

Further

reading:

href="http://economix.blogs.nytimes.com/2010/10/19/using-twitter-to-predict-stock-market-swings/?partner=rss&emc=rss">Using

Twitter to Predict Stock Market Swings, New York

Times

href="http://www.technologyreview.com/blog/arxiv/25900/?p1=Blogs">Twitter

Mood Predicts The Stock Market, Technology

Review

href="http://www.bloomberg.com/news/2010-12-22/hedge-fund-will-track-twitter-to-predict-stockmarket-movements.html">Hedge

Fund Will Track Twitter to Predict Stock Moves,

Bloomberg

href="http://www.smartmoney.com/invest/stocks/which-stocks-will-rise-ask-google-1297982065387/">Which

Stocks Will Rise? Ask Google,

SmartMoney

href="http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1364209">In

Search of Attention, Zhi Da, Joseph Engelberg and

Pengjie Gao

href="http://www.nytimes.com/2010/12/23/business/23trading.html?scp=1&sq=adam%20honore&st=cse">Computers

That Trade on the News, The New York

Times

href="http://streambase.typepad.com/streambase_stream_process/2009/06/on-ibm-unstructured-data-and-cep.html">On

IBM, Unstructured Data, and CEP, StreamBase Event

Processing Blog

href="http://ftalphaville.ft.com/blog/2009/06/24/58736/twitter-for-those-dirty-alpha-seeking-strategies/">Twitter

for those dirty, alpha-seeking strategies, FT

Alphaville