Counting Google searches predicts market movements

  title={Counting Google searches predicts market movements},
  author={Philip Ball},
  • P. Ball
  • Published 2013
  • Computer Science
  • Nature
Traders reveal their mood — but no easy path to riches — in the search terms they use. 

Topics from this paper

Paper Mentions

Trading Network Predicts Stock Price
Experimental results on 51 stocks in two Chinese Stock Exchanges demonstrate the accuracy of stock price prediction is significantly improved by the inclusion of trading relationship indices. Expand
Market Confidence Predicts Stock Price: Beyond Supply and Demand
Light is shed on using cross-day trading behavior to characterize market confidence and to predict stock price by incorporating the index of market confidence into a neural network based on time series of stock price. Expand
Using Social Media to Predict the Future: A Systematic Literature Review
It is found that SM forecasting is limited by data biases, noisy data, lack of generalizable results, a lack of domain-specific theory, and underlying complexity in many prediction tasks, but recurring findings and promising results continue to galvanize researchers and demand continued investigation. Expand
The Technology and Economic Determinants of Cryptocurrency Exchange Rates: The Case of Bitcoin
Cryptocurrencies, such as Bitcoin, have ignited intense discussions. Despite receiving extensive public attention, theoretical understanding is limited regarding the value of blockchain-basedExpand
The technology and economic determinants of cryptocurrency exchange rates: The case of Bitcoin
  • Xin Li, C. Wang
  • Economics, Computer Science
  • Decis. Support Syst.
  • 2017
A theory-driven empirical study of the Bitcoin exchange rate (against USD) determination, taking into consideration both technology and economic factors, finds the impact of computational capacities on Bitcoin is decreasing as technology progresses. Expand
Global mapping of artificial intelligence in Google and Google Scholar
This study explores the human mind through Google query data, linking popular regions, countries, and related topics to the concept of AI, and aims to predict the impact of AI on society, as interpreted through the lenses of well-established theories. Expand
A cloud-based dashboard for time series analysis on hot topics from social media
  • Ehsan Akbari Dahouei
  • Computer Science
  • 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS)
  • 2017
In this research, the distributions of keywords (hashtags) from hot topics are analyzed for finding patterns using a dashboard that can perform trend analysis similar to Google Trends. Expand
Global Stock Selection with Hidden Markov Model
Hidden Markov model (HMM) is a powerful machine-learning method for data regime detection, especially time series data. In this paper, we establish a multi-step procedure for using HMM to selectExpand
Big Data : Features, Architecture, Research and Applications
In this article, we present a description of the dimensions of Big Data, for the study of the application of this notion in several fields of application. We focus on the volume, variety andExpand
Investigations on big data features research challenges and applications
Evaluating the different dimensions of Big Data in various fields of applications with the Volume, Variety and frequency of generations of huge data focuses on the areas where large volume of data is being used for the growth and progress of the organizations. Expand


Quantifying Trading Behavior in Financial Markets Using Google Trends
By analyzing changes in Google query volumes for search terms related to finance, this work finds patterns that may be interpreted as “early warning signs” of stock market moves. Expand
Complex dynamics of our economic life on different scales: insights from search engine query data
It is found clear evidence that weekly transaction volumes of S&P 500 companies are correlated with weekly search volume of corresponding company names, and a recently introduced method for quantifying complex correlations in time series finds a clear tendency that search volume time series and transactionvolume time series show recurring patterns. Expand
Loss Aversion in Riskless Choice: A Reference-Dependent Model
Much experimental evidence indicates that choice depends on the status quo or reference level: changes of reference point often lead to reversals of preference. We present a reference-dependentExpand
Detecting influenza epidemics using search engine query data
A method of analysing large numbers of Google search queries to track influenza-like illness in a population and accurately estimate the current level of weekly influenza activity in each region of the United States with a reporting lag of about one day is presented. Expand