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 http://www.cs.ucr.edu/~vagelis/publications/wsdm2012-microblog-financial.pdf.
The research by Hristidis and Ruiz was supported by the National Science Foundation.
Sean Nealon | EurekAlert!
Microtechnology industry is hiring – positive developments of past years continue
09.04.2018 | IVAM Fachverband für Mikrotechnik
RWI/ISL-Container Throughput Index with minor decline on a high overall level
20.03.2018 | RWI – Leibniz-Institut für Wirtschaftsforschung
Researchers led by Francesca Ferlaino from the University of Innsbruck and the Austrian Academy of Sciences report in Physical Review X on the observation of supersolid behavior in dipolar quantum gases of erbium and dysprosium. In the dysprosium gas these properties are unprecedentedly long-lived. This sets the stage for future investigations into the nature of this exotic phase of matter.
Supersolidity is a paradoxical state where the matter is both crystallized and superfluid. Predicted 50 years ago, such a counter-intuitive phase, featuring...
A stellar flare 10 times more powerful than anything seen on our sun has burst from an ultracool star almost the same size as Jupiter
A localization phenomenon boosts the accuracy of solving quantum many-body problems with quantum computers which are otherwise challenging for conventional computers. This brings such digital quantum simulation within reach on quantum devices available today.
Quantum computers promise to solve certain computational problems exponentially faster than any classical machine. “A particularly promising application is the...
The technology could revolutionize how information travels through data centers and artificial intelligence networks
Engineers at the University of California, Berkeley have built a new photonic switch that can control the direction of light passing through optical fibers...
Physicists observe how electron-hole pairs drift apart at ultrafast speed, but still remain strongly bound.
Modern electronics relies on ultrafast charge motion on ever shorter length scales. Physicists from Regensburg and Gothenburg have now succeeded in resolving a...
17.04.2019 | Event News
15.04.2019 | Event News
09.04.2019 | Event News
23.04.2019 | Information Technology
23.04.2019 | Earth Sciences
23.04.2019 | Life Sciences