6,441 research outputs found

    Econometrics meets sentiment : an overview of methodology and applications

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    The advent of massive amounts of textual, audio, and visual data has spurred the development of econometric methodology to transform qualitative sentiment data into quantitative sentiment variables, and to use those variables in an econometric analysis of the relationships between sentiment and other variables. We survey this emerging research field and refer to it as sentometrics, which is a portmanteau of sentiment and econometrics. We provide a synthesis of the relevant methodological approaches, illustrate with empirical results, and discuss useful software

    Predictive Analytics on Emotional Data Mined from Digital Social Networks with a Focus on Financial Markets

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    This dissertation is a cumulative dissertation and is comprised of five articles. User-Generated Content (UGC) comprises a substantial part of communication via social media. In this dissertation, UGC that carries and facilitates the exchange of emotions is referred to as “emotional data.” People “produce” emotional data, that is, they express their emotions via tweets, forum posts, blogs, and so on, or they “consume” it by being influenced by expressed sentiments, feelings, opinions, and the like. Decisions often depend on shared emotions and data – which again lead to new data because decisions may change behaviors or results. “Emotional Data Intelligence” ultimately seeks an answer to the question of how all the different emotions expressed in public online sources influence decision-making processes. The overarching research topic of this dissertation follows the question whether network structures and emotional sentiment data extracted from digital social networks contain predictive information or they are just noise. Underlying data was collected from different social media sources, such as Twitter, blogs, message boards, or online news and social networking sites, such as Xing. By means of methodologies of social network analysis (SNA), sentiment analysis, and predictive analysis the individual contributions of this dissertation study whether sentiment data from social media or online social networking structures can predict real-world behaviors. The focus lies on the analysis of emotional data and network structures and its predictive power for financial markets. With the formal construction of the data analyses methodologies introduced in the individual contributions this dissertation contributes to the theories of social network analysis, sentiment analysis, and predictive analytics

    Quantifying the diversity of news around stock market moves

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    The dynamics of news are such that some days are dominated by a single story while others see news outlets reporting on a range of different events. While these largescale features of news are familiar to many, they are often ignored in settings where they may be important in understanding complex decision-making processes, such as in financial markets. In this paper, we use a topic-modeling approach to quantify the changing attentions of a major news outlet, the Financial Times, to issues of interest. Our analysis reveals that the diversity of financial news, as quantified by our method, can improve forecasts of trading volume. We also find evidence which suggests that, while attention in financial news tends to be concentrated on a smaller number of topics following stock market falls, there is a "healthy diversity" of news following upward market movements. We conclude that the diversity of financial news can be a useful forecasting tool, offering early warning signals of increased activity in financial markets

    Excitable human dynamics driven by extrinsic events in massive communities

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    Using empirical data from a social media site (Twitter) and on trading volumes of financial securities, we analyze the correlated human activity in massive social organizations. The activity, typically excited by real-world events and measured by the occurrence rate of international brand names and trading volumes, is characterized by intermittent fluctuations with bursts of high activity separated by quiescent periods. These fluctuations are broadly distributed with an inverse cubic tail and have long-range temporal correlations with a 1/f1/f power spectrum. We describe the activity by a stochastic point process and derive the distribution of activity levels from the corresponding stochastic differential equation. The distribution and the corresponding power spectrum are fully consistent with the empirical observations.Comment: 9 pages, 3 figure

    Does web anticipate stocks? Analysis for a subset of systemically important banks

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    Is web buzz able to lead stock behavior for a set of systemically important banks? Are stock movements sensitive to the geo-tagging of the web buzz? Between Dec. 2013 and April 2014, we scrape about 4000 world media websites retrieving all public information related to 10 systemically important banks. We process web news with a sentiment analysis algorithm in order to detect article mood. We show that web buzz does not seem to lead stock behavior as Granger test fails to support an average association that goes one-way from web to stocks. We nevertheless find a statistically sound anticipation capacity for single banks with gains ranging from 4 to 12%. Hierarchical clustering and Principal Component Analysis suggest that Euro area level decisions/facts do in fact drive stock behaviour, while web news about single banks only episodically make a difference in stock movements. Our analysis confirms that the location of the web source matters. The use of sources with international echo eliminates some of the noise introduced by irrelevant texts at the country level and improves the predictive power of the model up to 27.5%.JRC.G.1-Financial and Economic Analysi
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