A Global Mood Ring for Financial Markets

By Nicole Hemsoth

May 11, 2011

What if you could instantly scan all individual posts on Twitter for one day, cull those snippets together into a cogent whole, and use that information to paint a picture of the global mood?

If that idea alone isn’t enough, imagine making the snap decision to rush out and buy stocks because within three to four days stocks will rise due to the positive “vibe” in the air as foretold by the collective Twitter chatter. Conversely, if the world is having what amounts to a bad hair day, you accordingly sell your stock holdings, knowing that within three to four days, that dour zeitgeist will portend a drop in the Dow.

Does this sound to you like yet another flimsy system to sell traders on the idea that this might be the next big secret? Does it, like all other stock-related get-rich-quick schemes (and let’s face it, just because it comes out of academia doesn’t mask an unmistakble sense of self-interest) seem too good to be true?

For years scientists and speculators have tried to pin down the mysterious changing tides of the stock market. Proof? Search for “stock predictions” to find millions of options from informed analysis and offers of psychic or spiritual guidance. So far nothing has hit home for long enough to be a tried and true standard for evaluating buy or trade decisions.

That “too good to be true” paradigm for market predictions might be upended, however, thanks to our endless tweets and social media updates that indicate our mood, both through words and emoticons–not to mention an expensive array of compute-end tools to tackle massive unstructured data sets in a flash.

The provocative predictive analytics study in question proved a direct correlation between overall stock market performance and the general mood of many thousands of people as gauged from their brief posts on Twitter.  While the model for buying and selling described above only works around 86% of the time, this news caught the attention of traders and computer scientists with equal force.

As a recent article noted, “Online surveillance of social networking sites is emerging as a must-have tool for hedge funds, big banks, high-frequency traders and black box investment firms that run money via computer programs.” The author goes on to note that your feelings and general mood, captured and combined with the rest of the Tweeting, Facebooking world, could become the core of decision-making processes at major financial institutions.

Dr. Johan Bollen teaches informatics at Indiana University and is the lead behind the Twitter mood informatics project. He noted in a recent interview that this marks the beginning of a new era of mood collection to measure stock performance, noting that it is indeed like science fiction that we can now have “a large-scale emotional thermometer for society as a whole.”

In an interview this week Bollen told us that while he can’t share specific details about storage, application layers and the like since his team is in the process of further developing and licensing the research, the processing of tweets is happening in “real time” although it all depends on how one defines “real time.”

In Bollen’s words:

There is clearly a lower bound of the temporal granularity at which you can compute these signals. This limit is largely shaped by processing speeds and the amount of data to process. The amount of Twitter and social media data keeps growing very fast, however at some point you could expect all 7 billion people on earth to have a Twitter or Facebook account and since no one can tweet faster than their thumbs will move across a smart phone screen.

We may find some upper limit on the amount of social media data that can feasible be generated by humanity. From that you can work back to determine the temporal granularity at which you can operate the existing computational limits at a particular point in time.”

In Bollen’s experiences with the computational angle to arrival at global sentiment, he says that he remains optimistic that his team will be able to generate these signals at very small temporal granularity. He noted that as of now, they are “easily processing daily feeds meaning we have a daily signal which is suitable given that our research has shown that this signal is predictive of real-world changes 3 to 4 days in advance. From a preliminary analysis it seems we can take this down to hourly or even half-hourly signals without too much trouble.”

Bollen claims that while all of his work is quite CPU-intensive, much of it can be parallelized because they are analyze each tweet in isolation. With this in mind, however, he claims that they do run into some pretty hard computational limits with their social network analysis as some of the existing algorithms simply cannot run over social networks of such size.

As another element in the computational depth involved in such an undertaking, Bollen told us:

In terms of data intensiveness you do need large-scale data to counter-act noise and other distortions, but there is definitely a law of diminishing returns. Many of these data sets follow very skewed distributions. In general terms you will have very few people making very large and significant contributions, and very many making small and insignificant contributions. By capturing the right subset you can therefore arrive at a much smaller data set that still provides 90% of your signal, and thereby greatly boost your ability to perform your analysis at very short time intervals.

Outside of CPU and signals, there are other challenges that might stand in the way as this idea potentially takes off for financial companies.

This data, which comes in from across an array of global social networks creates a massive pool of unstructured data, could prove a stumbling block for the widespread viability of this kind of real-time data analysis.

Xenomorph is a data analytics and management firm with roots in financial services. According to its CEO, Brian Sentence, “Real time social media feeds give some insight into the human behavior that really drives the markets. However, in addition to the challenge of processing such a large amount of data, correct understanding of the data is the biggest challenge. For instance, seeing “Hathaway” mentioned on Twitter might mean some news on ‘Berkshire Hathaway’ or the actress “Anne Hathaway.”

However, this type of analytics goes far beyond general semantics and natural language processing—it looks at more discreet indicators of mood, including emoticons and other less word-bound cues.

Henry Newman, CEO and CTO of Instrumental, Inc., which is a consultancy firm for users and manufacturers of HPC, also weighed in on the data-level challenges of such analytics. He noted, “There are a number of challenges in this areas including the capture and indexing of the data and of course the development of algorithms to correlate the trends to specific market changes.  Additional challenges include the long term storage and the analysis model. I am aware of some sites MapReduce to be able to search this type of data but these are still problems.”

Instrumental Inc.’s Henry Newman also made a good point about the use of predictive analytics for financial markets in his speculation about the real value of this kind of technology. As he said, “I am not a sociologist and have not looked to see if this type of analysis will provide trending information for trading. What I am sure of is that if it does work just like every other method used it will not work all of the time and could cause large market swings just like other methods we have seen over the last few decades.”

The takeaway here if you’re not a stock market player: Be careful what you tweet, the world’s economy might just depend on it…well, at least 86% of the time. Allow me to do my part for the vitality of world economies and end this piece with a 🙂

Subscribe to HPCwire's Weekly Update!

Be the most informed person in the room! Stay ahead of the tech trends with industy updates delivered to you every week!

And So It Begins…Again – The FY19 Exascale Budget Rollout (and things look good)

February 23, 2018

On February 12, 2018, the Trump administration submitted its Fiscal Year 2019 (FY-19) budget to Congress. The good news for the U.S. exascale program is that the numbers look very good and the support appears to be stron Read more…

By Alex R. Larzelere

Lenovo Unveils Warm Water Cooled ThinkSystem SD650 in Rampup to LRZ Install

February 22, 2018

This week Lenovo took the wraps off the ThinkSystem SD650 high-density server with third-generation direct water cooling technology developed in tandem with partner Leibniz Supercomputing Center (LRZ) in Germany. The ser Read more…

By Tiffany Trader

Start-up Aims AI at Automated Tuning of Complex Systems

February 22, 2018

Today’s bigger, more complex, connected and intelligent systems have an exponentially higher number of connections, dependencies, interfaces, protocols and processing architectures that, if not optimized, will hamstrin Read more…

By Doug Black

HPE Extreme Performance Solutions

Experience Memory & Storage Solutions that will Transform Your Data Performance

High performance computing (HPC) has revolutionized the way we harness insight, leading to a dramatic increase in both the size and complexity of HPC systems. Read more…

Do Cryptocurrencies Have a Part to Play in HPC?

February 22, 2018

It’s easy to be distracted by news from the US, China, and now the EU on the state of various exascale projects, but behind the vinyl-wrapped cabinets and well-groomed sales execs are an army of Excel-wielding PMO and Read more…

By Chris Downing

Lenovo Unveils Warm Water Cooled ThinkSystem SD650 in Rampup to LRZ Install

February 22, 2018

This week Lenovo took the wraps off the ThinkSystem SD650 high-density server with third-generation direct water cooling technology developed in tandem with par Read more…

By Tiffany Trader

Start-up Aims AI at Automated Tuning of Complex Systems

February 22, 2018

Today’s bigger, more complex, connected and intelligent systems have an exponentially higher number of connections, dependencies, interfaces, protocols and pr Read more…

By Doug Black

HOKUSAI’s BigWaterfall Cluster Extends RIKEN’s Supercomputing Performance

February 21, 2018

RIKEN, Japan’s largest comprehensive research institution, recently expanded the capacity and capabilities of its HOKUSAI supercomputer, a key resource manage Read more…

By Ken Strandberg

Neural Networking Shows Promise in Earthquake Monitoring

February 21, 2018

A team of Harvard University and MIT researchers report their new neural networking method for monitoring earthquakes is more accurate and orders of magnitude faster than traditional approaches. Read more…

By John Russell

HPE Wins $57 Million DoD Supercomputing Contract

February 20, 2018

Hewlett Packard Enterprise (HPE) today revealed details of its massive $57 million HPC contract with the U.S. Department of Defense (DoD). The deal calls for HP Read more…

By Tiffany Trader

Fluid HPC: How Extreme-Scale Computing Should Respond to Meltdown and Spectre

February 15, 2018

The Meltdown and Spectre vulnerabilities are proving difficult to fix, and initial experiments suggest security patches will cause significant performance penal Read more…

By Pete Beckman

Brookhaven Ramps Up Computing for National Security Effort

February 14, 2018

Last week, Dan Coats, the director of Director of National Intelligence for the U.S., warned the Senate Intelligence Committee that Russia was likely to meddle in the 2018 mid-term U.S. elections, much as it stands accused of doing in the 2016 Presidential election. Read more…

By John Russell

AI Cloud Competition Heats Up: Google’s TPUs, Amazon Building AI Chip

February 12, 2018

Competition in the white hot AI (and public cloud) market pits Google against Amazon this week, with Google offering AI hardware on its cloud platform intended Read more…

By Doug Black

Inventor Claims to Have Solved Floating Point Error Problem

January 17, 2018

"The decades-old floating point error problem has been solved," proclaims a press release from inventor Alan Jorgensen. The computer scientist has filed for and Read more…

By Tiffany Trader

Japan Unveils Quantum Neural Network

November 22, 2017

The U.S. and China are leading the race toward productive quantum computing, but it's early enough that ultimate leadership is still something of an open questi Read more…

By Tiffany Trader

AMD Showcases Growing Portfolio of EPYC and Radeon-based Systems at SC17

November 13, 2017

AMD’s charge back into HPC and the datacenter is on full display at SC17. Having launched the EPYC processor line in June along with its MI25 GPU the focus he Read more…

By John Russell

Researchers Measure Impact of ‘Meltdown’ and ‘Spectre’ Patches on HPC Workloads

January 17, 2018

Computer scientists from the Center for Computational Research, State University of New York (SUNY), University at Buffalo have examined the effect of Meltdown Read more…

By Tiffany Trader

IBM Begins Power9 Rollout with Backing from DOE, Google

December 6, 2017

After over a year of buildup, IBM is unveiling its first Power9 system based on the same architecture as the Department of Energy CORAL supercomputers, Summit a Read more…

By Tiffany Trader

Nvidia Responds to Google TPU Benchmarking

April 10, 2017

Nvidia highlights strengths of its newest GPU silicon in response to Google's report on the performance and energy advantages of its custom tensor processor. Read more…

By Tiffany Trader

Fast Forward: Five HPC Predictions for 2018

December 21, 2017

What’s on your list of high (and low) lights for 2017? Volta 100’s arrival on the heels of the P100? Appearance, albeit late in the year, of IBM’s Power9? Read more…

By John Russell

Russian Nuclear Engineers Caught Cryptomining on Lab Supercomputer

February 12, 2018

Nuclear scientists working at the All-Russian Research Institute of Experimental Physics (RFNC-VNIIEF) have been arrested for using lab supercomputing resources to mine crypto-currency, according to a report in Russia’s Interfax News Agency. Read more…

By Tiffany Trader

Leading Solution Providers

Chip Flaws ‘Meltdown’ and ‘Spectre’ Loom Large

January 4, 2018

The HPC and wider tech community have been abuzz this week over the discovery of critical design flaws that impact virtually all contemporary microprocessors. T Read more…

By Tiffany Trader

Perspective: What Really Happened at SC17?

November 22, 2017

SC is over. Now comes the myriad of follow-ups. Inboxes are filled with templated emails from vendors and other exhibitors hoping to win a place in the post-SC thinking of booth visitors. Attendees of tutorials, workshops and other technical sessions will be inundated with requests for feedback. Read more…

By Andrew Jones

How Meltdown and Spectre Patches Will Affect HPC Workloads

January 10, 2018

There have been claims that the fixes for the Meltdown and Spectre security vulnerabilities, named the KPTI (aka KAISER) patches, are going to affect applicatio Read more…

By Rosemary Francis

GlobalFoundries, Ayar Labs Team Up to Commercialize Optical I/O

December 4, 2017

GlobalFoundries (GF) and Ayar Labs, a startup focused on using light, instead of electricity, to transfer data between chips, today announced they've entered in Read more…

By Tiffany Trader

Tensors Come of Age: Why the AI Revolution Will Help HPC

November 13, 2017

Thirty years ago, parallel computing was coming of age. A bitter battle began between stalwart vector computing supporters and advocates of various approaches to parallel computing. IBM skeptic Alan Karp, reacting to announcements of nCUBE’s 1024-microprocessor system and Thinking Machines’ 65,536-element array, made a public $100 wager that no one could get a parallel speedup of over 200 on real HPC workloads. Read more…

By John Gustafson & Lenore Mullin

Flipping the Flops and Reading the Top500 Tea Leaves

November 13, 2017

The 50th edition of the Top500 list, the biannual publication of the world’s fastest supercomputers based on public Linpack benchmarking results, was released Read more…

By Tiffany Trader

V100 Good but not Great on Select Deep Learning Aps, Says Xcelerit

November 27, 2017

Wringing optimum performance from hardware to accelerate deep learning applications is a challenge that often depends on the specific application in use. A benc Read more…

By John Russell

SC17: Singularity Preps Version 3.0, Nears 1M Containers Served Daily

November 1, 2017

Just a few months ago about half a million jobs were being run daily using Singularity containers, the LBNL-founded container platform intended for HPC. That wa Read more…

By John Russell

  • arrow
  • Click Here for More Headlines
  • arrow
Share This