Forum for Science, Industry and Business

Sponsored by:     3M 
Search our Site:


Using Twitter to predict financial markets

UC Riverside professor and collaborators build model using Twitter data that outperforms other investment strategies

A University of California, Riverside professor and several other researchers have developed a model that uses data from Twitter to help predict the traded volume and value of a stock the following day.

A trading strategy based on the model created by Vagelis Hristidis, an associate professor at the Bourns College of Engineering, one of his graduate students and three researchers at Yahoo! in Spain, outperformed other baseline strategies by between 1.4 percent and nearly 11 percent and also did better than the Dow Jones Industrial Average during a four-month simulation.

"These findings have the potential to have a big impact on market investors," said Hristidis, who specializes in data mining research, which focuses on discovering patterns in large data sets. "With so much data available from social media, many investors are looking to sort it out and profit from it."

Hristidis and his co-authors, Eduardo J. Ruiz, one of his graduate students, and Carlos Castillo, Aristides Gionis and Alejandro Jaimes, all of whom work for Yahoo! Research Barcelona, presented the findings last month at the Fifth ACM International Conference on Web Search & Data Mining in Seattle.

Hristidis and his co-authors set out to study how activity in Twitter is correlated to stock prices and traded volume. While past research has looked the sentiment, positive or negative, of tweets to predict stock price, little research has focused on the volume of tweets and the ways that tweets are linked to other tweets, topics or users. Further, past work has mostly studied the overall stock market indexes, and not individual stocks.

They obtained the daily closing price and the number of trades from Yahoo! Finance for 150 randomly selected companies in the S&P 500 Index for the first half of 2010.

Then, they developed filters to select only relevant tweets for those companies during that time period. For example, if they were looking at Apple, they needed to exclude tweets that focused on the fruit.

They expected to find the number of trades was correlated with the number of tweets. Surprisingly, the number of trades is slightly more correlated with the number of what they call "connected components." That is the number of posts about distinct topics related to one company. For example, using Apple again, there might be separate networks of posts regarding Apple's new CEO, a new product it released and its latest earnings report.

They also found stock price is slightly correlated with the number of connected components.

For the study, the researchers simulated a series of investments between March 1, 2010 and June 30, 2010 and analyzed performance using several investment strategies. During that time frame, the Dow Jones Industrial Average fell 4.2 percent.

In two variants of an autoregression model, that is buying every day stocks based on the assumption that the stock price is a function of the prices of the stock in the last few days, losses were 8.9 percent and 13.1 percent.

In the random model, in which as random set of stocks is bought every, sold at the end of the day and repeated the next day, the average loss was 5.5 percent.

In the fixed model, which involves buying a set of stocks that have best combination of market cap, company size and total debt and keeping them for the entire simulation, the average loss was 3.8 percent.

The model the researchers developed using Twitter data lost on average 2.4 percent.

Hristidis notes several potential weaknesses in the study.

First, the trading strategy worked in a period when the Dow Jones dropped, but it may not produce the same results when the Dow Jones is rising. There is also sensitivity related to the duration of the trading. For example, it took 30 days in the simulation to start outperforming the Dow Jones.

The published paper that outlines the findings can be found at

The research by Hristidis and Ruiz was supported by the National Science Foundation.

Sean Nealon | EurekAlert!
Further information:

Further reports about: Apple iPhone Riverside Tweets Twitter Twitter data stock price

More articles from Business and Finance:

nachricht Blockchain Set to Transform the Financial Services Market
28.09.2016 | HHL Leipzig Graduate School of Management

nachricht Paper or plastic?
08.07.2016 | University of Toronto

All articles from Business and Finance >>>

The most recent press releases about innovation >>>

Die letzten 5 Focus-News des innovations-reports im Überblick:

Im Focus: New 3-D wiring technique brings scalable quantum computers closer to reality

Researchers from the Institute for Quantum Computing (IQC) at the University of Waterloo led the development of a new extensible wiring technique capable of controlling superconducting quantum bits, representing a significant step towards to the realization of a scalable quantum computer.

"The quantum socket is a wiring method that uses three-dimensional wires based on spring-loaded pins to address individual qubits," said Jeremy Béjanin, a PhD...

Im Focus: Scientists develop a semiconductor nanocomposite material that moves in response to light

In a paper in Scientific Reports, a research team at Worcester Polytechnic Institute describes a novel light-activated phenomenon that could become the basis for applications as diverse as microscopic robotic grippers and more efficient solar cells.

A research team at Worcester Polytechnic Institute (WPI) has developed a revolutionary, light-activated semiconductor nanocomposite material that can be used...

Im Focus: Diamonds aren't forever: Sandia, Harvard team create first quantum computer bridge

By forcefully embedding two silicon atoms in a diamond matrix, Sandia researchers have demonstrated for the first time on a single chip all the components needed to create a quantum bridge to link quantum computers together.

"People have already built small quantum computers," says Sandia researcher Ryan Camacho. "Maybe the first useful one won't be a single giant quantum computer...

Im Focus: New Products - Highlights of COMPAMED 2016

COMPAMED has become the leading international marketplace for suppliers of medical manufacturing. The trade fair, which takes place every November and is co-located to MEDICA in Dusseldorf, has been steadily growing over the past years and shows that medical technology remains a rapidly growing market.

In 2016, the joint pavilion by the IVAM Microtechnology Network, the Product Market “High-tech for Medical Devices”, will be located in Hall 8a again and will...

Im Focus: Ultra-thin ferroelectric material for next-generation electronics

'Ferroelectric' materials can switch between different states of electrical polarization in response to an external electric field. This flexibility means they show promise for many applications, for example in electronic devices and computer memory. Current ferroelectric materials are highly valued for their thermal and chemical stability and rapid electro-mechanical responses, but creating a material that is scalable down to the tiny sizes needed for technologies like silicon-based semiconductors (Si-based CMOS) has proven challenging.

Now, Hiroshi Funakubo and co-workers at the Tokyo Institute of Technology, in collaboration with researchers across Japan, have conducted experiments to...

All Focus news of the innovation-report >>>



Event News

#IC2S2: When Social Science meets Computer Science - GESIS will host the IC2S2 conference 2017

14.10.2016 | Event News

Agricultural Trade Developments and Potentials in Central Asia and the South Caucasus

14.10.2016 | Event News

World Health Summit – Day Three: A Call to Action

12.10.2016 | Event News

Latest News

Resolving the mystery of preeclampsia

21.10.2016 | Health and Medicine

Stanford researchers create new special-purpose computer that may someday save us billions

21.10.2016 | Information Technology

From ancient fossils to future cars

21.10.2016 | Materials Sciences

More VideoLinks >>>