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Related Concept Videos

Correlation01:09

Correlation

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In statistics, two variables are said to be correlated if the values of one variable are associated with the other variable. Depending on the relationship between two variables, correlation can be of three types– positive correlation, negative correlation, and zero correlation.
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Correlation and Causation01:27

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Statistical tests can calculate whether there is a relationship, or correlation, between independent and dependent variables. An indirect relationship of the variables signifies a correlation, while a direct relationship shows causation. If it is determined that no connection exists between the variables, then the correlation is a coincidence.
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Cause and Effect01:53

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While variables are sometimes correlated because one does cause the other, it could also be that some other factor, a confounding variable, is actually causing the systematic movement in our variables of interest. For instance, as sales in ice cream increase, so does the overall rate of crime. Is it possible that indulging in your favorite flavor of ice cream could send you on a crime spree? Or, after committing crime do you think you might decide to treat yourself to a cone?
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An outlier is an observation of data that does not fit the rest of the data. It is sometimes called an extreme value. When you graph an outlier, it will appear not to fit the pattern of the graph. Some outliers are due to mistakes (for example, writing down 50 instead of 500), while others may indicate that something unusual is happening. Outliers are present far from the least squares line in the vertical direction. They have large "errors," where the "error" or residual is the...
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In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
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Regression Toward the Mean01:52

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Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
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Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method
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Information Theoretic Causality Detection between Financial and Sentiment Data.

Roberta Scaramozzino1, Paola Cerchiello1, Tomaso Aste2,3,4

  • 1Department of Economics and Management, University of Pavia, Via San Felice 7, 27100 Pavia, Italy.

Entropy (Basel, Switzerland)
|June 2, 2021
PubMed
Summary
This summary is machine-generated.

Stock market prices and social media sentiment exhibit a causal relationship, with information flowing in both directions. The technology sector shows the strongest link between sentiment and stock price dynamics.

Keywords:
causalityfinancial newsinformation theorytextual analysistime seriestransfer entropy

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Area of Science:

  • Computational finance
  • Information theory
  • Social media analytics

Background:

  • Understanding the interplay between public sentiment and financial markets is crucial.
  • Traditional analyses often struggle to quantify causal relationships between sentiment and stock prices.

Purpose of the Study:

  • To quantify the causal relationship between social media sentiment and stock market prices.
  • To identify the directionality and strength of information flow between sentiment and prices.
  • To investigate sector-specific differences in this relationship.

Main Methods:

  • Utilized transfer entropy, an information-theoretic measure, to analyze causality.
  • Analyzed daily stock prices and social media sentiment for top 50 S&P 500 companies (Nov 2018 - Nov 2020).
  • Incorporated news data mentioning these companies into the analysis.

Main Results:

  • Identified a significant causal flux of information linking companies.
  • Found the strongest causal links predominantly between stock prices and between sentiments.
  • Observed significant bidirectional causality between sentiment and prices.
  • Detected the strongest sentiment-price causal signal within the technology sector.

Conclusions:

  • Social media sentiment and stock prices are causally interconnected, not merely correlated.
  • Information flows dynamically between sentiment and market prices.
  • The technology sector exhibits a particularly strong sensitivity to sentiment-driven market dynamics.