4,006 research outputs found
Twitter mood predicts the stock market
Behavioral economics tells us that emotions can profoundly affect individual
behavior and decision-making. Does this also apply to societies at large, i.e.,
can societies experience mood states that affect their collective decision
making? By extension is the public mood correlated or even predictive of
economic indicators? Here we investigate whether measurements of collective
mood states derived from large-scale Twitter feeds are correlated to the value
of the Dow Jones Industrial Average (DJIA) over time. We analyze the text
content of daily Twitter feeds by two mood tracking tools, namely OpinionFinder
that measures positive vs. negative mood and Google-Profile of Mood States
(GPOMS) that measures mood in terms of 6 dimensions (Calm, Alert, Sure, Vital,
Kind, and Happy). We cross-validate the resulting mood time series by comparing
their ability to detect the public's response to the presidential election and
Thanksgiving day in 2008. A Granger causality analysis and a Self-Organizing
Fuzzy Neural Network are then used to investigate the hypothesis that public
mood states, as measured by the OpinionFinder and GPOMS mood time series, are
predictive of changes in DJIA closing values. Our results indicate that the
accuracy of DJIA predictions can be significantly improved by the inclusion of
specific public mood dimensions but not others. We find an accuracy of 87.6% in
predicting the daily up and down changes in the closing values of the DJIA and
a reduction of the Mean Average Percentage Error by more than 6%
Applied machine learning using twitter sentiment and time series data for stock market forecasting
Master's Project (M.S.) University of Alaska Fairbanks, 2020This paper presents an approach to determine stock prices using Twitter sentiment. Due to the highly stochastic nature of the stock market, it is difficult to determine a model that accurately predicts prices. In Twitter Mood Predicts the Stock Market by Bollen, capturing tweets and classifying each tweetās mood was useful in predicting the Dow Industrial Jones Average (DJIA). Accurately predicting a movement quantitatively is profitable. We present a method that captures sentiment from Twitter with mentions of specific companies to predict their price for the following day
Sentiment Analysis of Twitter Data for Predicting Stock Market Movements
Predicting stock market movements is a well-known problem of interest.
Now-a-days social media is perfectly representing the public sentiment and
opinion about current events. Especially, twitter has attracted a lot of
attention from researchers for studying the public sentiments. Stock market
prediction on the basis of public sentiments expressed on twitter has been an
intriguing field of research. Previous studies have concluded that the
aggregate public mood collected from twitter may well be correlated with Dow
Jones Industrial Average Index (DJIA). The thesis of this work is to observe
how well the changes in stock prices of a company, the rises and falls, are
correlated with the public opinions being expressed in tweets about that
company. Understanding author's opinion from a piece of text is the objective
of sentiment analysis. The present paper have employed two different textual
representations, Word2vec and N-gram, for analyzing the public sentiments in
tweets. In this paper, we have applied sentiment analysis and supervised
machine learning principles to the tweets extracted from twitter and analyze
the correlation between stock market movements of a company and sentiments in
tweets. In an elaborate way, positive news and tweets in social media about a
company would definitely encourage people to invest in the stocks of that
company and as a result the stock price of that company would increase. At the
end of the paper, it is shown that a strong correlation exists between the rise
and falls in stock prices with the public sentiments in tweets.Comment: 6 pages 4 figures Conference Pape
The Effects of Twitter Sentiment on Stock Price Returns
Social media are increasingly reflecting and influencing behavior of other
complex systems. In this paper we investigate the relations between a well-know
micro-blogging platform Twitter and financial markets. In particular, we
consider, in a period of 15 months, the Twitter volume and sentiment about the
30 stock companies that form the Dow Jones Industrial Average (DJIA) index. We
find a relatively low Pearson correlation and Granger causality between the
corresponding time series over the entire time period. However, we find a
significant dependence between the Twitter sentiment and abnormal returns
during the peaks of Twitter volume. This is valid not only for the expected
Twitter volume peaks (e.g., quarterly announcements), but also for peaks
corresponding to less obvious events. We formalize the procedure by adapting
the well-known "event study" from economics and finance to the analysis of
Twitter data. The procedure allows to automatically identify events as Twitter
volume peaks, to compute the prevailing sentiment (positive or negative)
expressed in tweets at these peaks, and finally to apply the "event study"
methodology to relate them to stock returns. We show that sentiment polarity of
Twitter peaks implies the direction of cumulative abnormal returns. The amount
of cumulative abnormal returns is relatively low (about 1-2%), but the
dependence is statistically significant for several days after the events
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