It is widely recognized that worldwide financial market can affect several aspects of our lives, from prices of primary goods to Geopolitics.
A business is usually fractioned into small ownerships represented by shares which can be publicly sold to raise additional capital. Stock markets are loose and complex networks of economic transactions created for the trading of company stock and derivatives. We can comfortably argue that nowadays global economic growth or crashes originate in stock markets.
Although anyone can personally experience in everyday life the consequences of worldwide economy changes, the dynamic behind such processes may generally require a specific knowledge in order to be fully understood.
Can the trading market patterns be represented in a perceptually weighted experience?
Below you can find an audio visual real time recording of an earlier output of the system, however it is highly recommended to watch the video in full screen High Definition here.
Invisible Suns (Marco Donnarumma, 2010) is an autonomous system that perform a permanent analysis of historical stock prices of a variable selection of major corporations, compresses in few minutes over 8 years of economic transactions and eventually produces a generative and self-organizing audiovisual datascape every 24 hours.
The work does not focus on traditional visualization of data, but rather aims at exploring how this data – and their implied meaning – can be perceptually, emotionally experienced.
Everyday since the 1st August 2010 the system retrieves from the Internet up to date stock prices of selected companies and adds new values to its set of databases. The oldest figures date back to January 2002 while the newest are being collected today.
At the moment the system is analyzing historical stock prices of six companies which boast the highest market capital in defense and oil industry: BAE Systems, Lockheed Martin Corporation, Exxon Mobil Corporation, Royal Dutch Shell, Chevron Corporation and General Dynamics Corporation.
Data are processed in real time to generate a panoramic synaesthetic scape which demonstrates an auditive and visual sensation of expansions and falls of companies shares as well as the overall movement of the trading market.
The system also operates a cross-comparison of datasets in order to identify peaks and lows in the overall trading activity and outline them utilizing sound spatialization and lights movements in the 3D environment.
Duration of the work, intended as audio visual output, is constantly growing: as figures increase every day, the time length of the piece increments too.
Share prices are retrieved from a publicly available on-line service at Google Finance and the total amount of data examined so far is around 9.000 per company for a total amount of around 54.000 data.
Data retrieval, analysis and processing is implemented using free software.
Sound is generated by mean of wavetable synthesis matrix modulation. Stock prices are utilized to produce several single-cycle waveforms and to determine the modulation rate of the resulting wavetables. Eventually the volume of trading activity is employed to control additional post-processing such as filtering and spatialization.
nyse, london stock exchange, shares, shareholders, liquidity, economic growth, financial crisis, trading; wavetable synthesis, matrix modulation, multiple single cycle waveforms, periodic reproduction, evolving synth pads; perceptual, sensorial, synaesthetic; data visualization, autonomous, database, bash, script, pure data.
- Linux Audio Conference, CCRMA, Stanford University, CA, USA, April 2012
- Screengrab New Media Art Award, eMerge Gallery, Townsville, Australia, October 2010