Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Increasing Function01:18

Increasing Function

An increasing function exhibits a rise in output values as input values increase. This behavior is depicted graphically as a curve or line that slopes upward from left to right. Such a function satisfies the condition that if x1 < x2, then f(x1) < f(x2), indicating that the function values grow with increasing inputs. This concept is fundamental in understanding growth trends across various domains, such as population dynamics, financial investments, or resource consumption.The average...
Quantitative Analysis01:12

Quantitative Analysis

Quantitative analysis is a technique for measuring the amount of specific constituents in a sample. When the sample's composition is unknown, qualitative analysis is performed first to identify its components, which ensures that the correct substances are measured during the quantitative phase.
In quantitative analysis, two key measurements are made: the sample quantity and a property proportional to the amount of the analyte (the substance being analyzed). This forms the basis of the method...
Time-Series Graph00:54

Time-Series Graph

A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
First Derivative Test: Problem Solving01:25

First Derivative Test: Problem Solving

Imagine an asset price that crashes to a low point, rebounds sharply as bargain-hunters step in, and then gradually declines. Such behavior can be modeled with a smooth function whose turning points represent locally overvalued and undervalued regions. A convenient example that captures rebound followed by decay is:The high and low points of this curve are identified using the first derivative test, which determines where the function changes from increasing to decreasing or vice versa. To...
Econometric Views (EViews)01:29

Econometric Views (EViews)

Econometric Views, often stylized as EViews, is a package that merges statistical analysis with econometric studies. It is designed to provide tools for time series analysis, forecasting, and econometric model simulation. The software originated from MicroTSP software and has evolved significantly since its inception in 1981. The history of EViews is marked by a continuous effort to enhance its computational speed and user interface. It was initially developed for large computing systems but...
Central Tendency: Analysis01:10

Central Tendency: Analysis

Measures of central tendency are tools used in biostatistics to identify the average or center of a dataset. They offer a single representative value for understanding and summarizing data distribution.
The mean is one such measure, calculated by totaling all values in a dataset and dividing by the number of values. For instance, the mean blood pressure reading (120, 130, 140, 150) would be 135. However, the mean can be affected by extreme values or outliers.
The median, another measure,...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Coordination of network heterogeneity and individual preferences promotes collective fairness.

Patterns (New York, N.Y.)·2025
Same author

A general urban spreading pattern of COVID-19 and its underlying mechanism.

npj urban sustainability·2023
Same author

Opinion dynamics in financial markets via random networks.

Proceedings of the National Academy of Sciences of the United States of America·2022
Same author

Most influential countries in the international medical device trade: Network-based analysis.

Physica A·2022
Same author

Faster indicators of chikungunya incidence using Google searches.

PLoS neglected tropical diseases·2022
Same author

A New Look at Calendar Anomalies: Multifractality and Day-of-the-Week Effect.

Entropy (Basel, Switzerland)·2022

Related Experiment Videos

Quantifying trading behavior in financial markets using Google Trends.

Tobias Preis1, Helen Susannah Moat, H Eugene Stanley

  • 1Warwick Business School, University of Warwick, Scarman Road, Coventry, CV4 7AL, UK. Tobias.Preis@wbs.ac.uk

Scientific Reports
|April 27, 2013
PubMed
Summary
This summary is machine-generated.

Analyzing Google search trends can reveal early warning signs of stock market movements. This study shows how internet data offers new insights into collective human behavior during financial crises.

Related Experiment Videos

Area of Science:

  • Behavioral economics
  • Computational social science
  • Financial market analysis

Background:

  • Financial market crises have significant global impact.
  • Trading decisions reveal complex human behaviors contributing to market instability.
  • Traditional market data offers limited insight into collective participant behavior.

Purpose of the Study:

  • To explore novel data sources for understanding market participant behavior.
  • To identify potential early warning signs of stock market fluctuations.
  • To investigate the utility of internet-derived behavioral data in financial analysis.

Main Methods:

  • Analysis of Google search query volumes for financial terms.
  • Correlation of search trend data with stock market movements.
  • Identification of patterns preceding significant market shifts.

Main Results:

  • Observed patterns in search query volumes correlate with stock market activity.
  • Specific search trends may function as predictive indicators for market movements.
  • Internet-based behavioral data provides a unique lens on collective market psychology.

Conclusions:

  • Massive online behavioral data offers a new perspective on financial markets.
  • Google search trends can serve as valuable early warning signals for market volatility.
  • Integrating diverse behavioral datasets enhances understanding of collective human actions in finance.