50,383 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%
On using Twitter to monitor political sentiment and predict election results
The body of content available on Twitter undoubtedly contains a diverse range of political insight and commentary. But, to what extent is this representative of an
electorate? Can we model political sentiment effectively enough to capture the voting intentions of a nation during an election capaign? We use the recent Irish General
Election as a case study for investigating the potential to model political sentiment through mining of social media. Our approach combines sentiment analysis using
supervised learning and volume-based measures. We evaluate against the conventional election polls and the final election result. We find that social analytics using
both volume-based measures and sentiment analysis are predictive and wemake a number of observations related to the task of monitoring public sentiment during
an election campaign, including examining a variety of sample sizes, time periods as well as methods for qualitatively exploring the underlying content
From Social Data Mining to Forecasting Socio-Economic Crisis
Socio-economic data mining has a great potential in terms of gaining a better
understanding of problems that our economy and society are facing, such as
financial instability, shortages of resources, or conflicts. Without
large-scale data mining, progress in these areas seems hard or impossible.
Therefore, a suitable, distributed data mining infrastructure and research
centers should be built in Europe. It also appears appropriate to build a
network of Crisis Observatories. They can be imagined as laboratories devoted
to the gathering and processing of enormous volumes of data on both natural
systems such as the Earth and its ecosystem, as well as on human
techno-socio-economic systems, so as to gain early warnings of impending
events. Reality mining provides the chance to adapt more quickly and more
accurately to changing situations. Further opportunities arise by individually
customized services, which however should be provided in a privacy-respecting
way. This requires the development of novel ICT (such as a self- organizing
Web), but most likely new legal regulations and suitable institutions as well.
As long as such regulations are lacking on a world-wide scale, it is in the
public interest that scientists explore what can be done with the huge data
available. Big data do have the potential to change or even threaten democratic
societies. The same applies to sudden and large-scale failures of ICT systems.
Therefore, dealing with data must be done with a large degree of responsibility
and care. Self-interests of individuals, companies or institutions have limits,
where the public interest is affected, and public interest is not a sufficient
justification to violate human rights of individuals. Privacy is a high good,
as confidentiality is, and damaging it would have serious side effects for
society.Comment: 65 pages, 1 figure, Visioneer White Paper, see
http://www.visioneer.ethz.c
Inferring short-term volatility indicators from Bitcoin blockchain
In this paper, we study the possibility of inferring early warning indicators
(EWIs) for periods of extreme bitcoin price volatility using features obtained
from Bitcoin daily transaction graphs. We infer the low-dimensional
representations of transaction graphs in the time period from 2012 to 2017
using Bitcoin blockchain, and demonstrate how these representations can be used
to predict extreme price volatility events. Our EWI, which is obtained with a
non-negative decomposition, contains more predictive information than those
obtained with singular value decomposition or scalar value of the total Bitcoin
transaction volume
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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