Featured Research

from universities, journals, and other organizations

Using Twitter to predict financial markets

Date:
March 26, 2012
Source:
University of California - Riverside
Summary:
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 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.


Story Source:

The above story is based on materials provided by University of California - Riverside. Note: Materials may be edited for content and length.


Cite This Page:

University of California - Riverside. "Using Twitter to predict financial markets." ScienceDaily. ScienceDaily, 26 March 2012. <www.sciencedaily.com/releases/2012/03/120326113321.htm>.
University of California - Riverside. (2012, March 26). Using Twitter to predict financial markets. ScienceDaily. Retrieved October 23, 2014 from www.sciencedaily.com/releases/2012/03/120326113321.htm
University of California - Riverside. "Using Twitter to predict financial markets." ScienceDaily. www.sciencedaily.com/releases/2012/03/120326113321.htm (accessed October 23, 2014).

Share This



More Computers & Math News

Thursday, October 23, 2014

Featured Research

from universities, journals, and other organizations


Featured Videos

from AP, Reuters, AFP, and other news services

Chameleon Camouflage to Give Tanks Cloaking Capabilities

Chameleon Camouflage to Give Tanks Cloaking Capabilities

Reuters - Innovations Video Online (Oct. 22, 2014) — Inspired by the way a chameleon changes its colour to disguise itself; scientists in Poland want to replace traditional camouflage paint with thousands of electrochromic plates that will continuously change colour to blend with its surroundings. The first PL-01 concept tank prototype will be tested within a few years, with scientists predicting that a similar technology could even be woven into the fabric of a soldiers' clothing making them virtually invisible to the naked eye. Matthew Stock reports. Video provided by Reuters
Powered by NewsLook.com
Internet of Things Aims to Smarten Your Life

Internet of Things Aims to Smarten Your Life

AP (Oct. 22, 2014) — As more and more Bluetooth-enabled devices are reaching consumers, developers are busy connecting them together as part of the Internet of Things. (Oct. 22) Video provided by AP
Powered by NewsLook.com
Free Math App Is A Teacher's Worst Nightmare

Free Math App Is A Teacher's Worst Nightmare

Newsy (Oct. 22, 2014) — New photo-recognition software from MicroBlink, called PhotoMath, solves linear equations and simple math problems with step-by-step results. Video provided by Newsy
Powered by NewsLook.com
Rate Hike Worries Down on Inflation Data

Rate Hike Worries Down on Inflation Data

Reuters - Business Video Online (Oct. 22, 2014) — Inflation remains well under control according to the latest consumer price index, giving the Federal Reserve more room to keep interest rates low for awhile. Bobbi Rebell reports. Video provided by Reuters
Powered by NewsLook.com

Search ScienceDaily

Number of stories in archives: 140,361

Find with keyword(s):
 
Enter a keyword or phrase to search ScienceDaily for related topics and research stories.

Save/Print:
Share:  

Breaking News:

Strange & Offbeat Stories

 

Space & Time

Matter & Energy

Computers & Math

In Other News

... from NewsDaily.com

Science News

Health News

Environment News

Technology News



Save/Print:
Share:  

Free Subscriptions


Get the latest science news with ScienceDaily's free email newsletters, updated daily and weekly. Or view hourly updated newsfeeds in your RSS reader:

Get Social & Mobile


Keep up to date with the latest news from ScienceDaily via social networks and mobile apps:

Have Feedback?


Tell us what you think of ScienceDaily -- we welcome both positive and negative comments. Have any problems using the site? Questions?
Mobile iPhone Android Web
Follow Facebook Twitter Google+
Subscribe RSS Feeds Email Newsletters
Latest Headlines Health & Medicine Mind & Brain Space & Time Matter & Energy Computers & Math Plants & Animals Earth & Climate Fossils & Ruins