Difference between revisions of "What Can Wikipedia and Google Tell Us About Stock Prices Under Diferent Market Regimes"
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| + | | title = What Can Wikipedia and Google Tell Us About Stock Prices Under Diferent Market Regimes | ||
| + | | date = 2015 | ||
| + | | authors = [[Boris Cergol]]<br />[[Matjaž Omladič]] | ||
| + | | doi = 10.26493/1855-3974.561.37f | ||
| + | | link = https://amc-journal.eu/index.php/amc/article/download/561/836 | ||
| + | }} | ||
'''What Can Wikipedia and Google Tell Us About Stock Prices Under Diferent Market Regimes''' - scientific work related to [[Wikipedia quality]] published in 2015, written by [[Boris Cergol]] and [[Matjaž Omladič]]. | '''What Can Wikipedia and Google Tell Us About Stock Prices Under Diferent Market Regimes''' - scientific work related to [[Wikipedia quality]] published in 2015, written by [[Boris Cergol]] and [[Matjaž Omladič]]. | ||
== Overview == | == Overview == | ||
In less than five years a surprisingly high level of attention has built up in the possible connection between internet search data and stock prices. It is the main aim of this paper to point out how this connection may depend heavily on different regimes of the market, i.e. the bear market vs. the bull market. Authors consider three types of internet search data (relative [[Google]] search frequencies of company tickers, relative Google search frequencies of company names and page visits of [[Wikipedia]] articles about individual companies) and a substantial sample of companies which are members of the S&P 500 index. Authors discover two inverse patterns in stock prices: in the bear market what authors propose to term a "merry frown" and in bull market a "sour smile", both clearly seen especially for the Wikipedia data. Authors propose market neutral strategies that exploit these new patterns and yield up to 17% in average annual return during sample period from 2008 to 2013. | In less than five years a surprisingly high level of attention has built up in the possible connection between internet search data and stock prices. It is the main aim of this paper to point out how this connection may depend heavily on different regimes of the market, i.e. the bear market vs. the bull market. Authors consider three types of internet search data (relative [[Google]] search frequencies of company tickers, relative Google search frequencies of company names and page visits of [[Wikipedia]] articles about individual companies) and a substantial sample of companies which are members of the S&P 500 index. Authors discover two inverse patterns in stock prices: in the bear market what authors propose to term a "merry frown" and in bull market a "sour smile", both clearly seen especially for the Wikipedia data. Authors propose market neutral strategies that exploit these new patterns and yield up to 17% in average annual return during sample period from 2008 to 2013. | ||
Revision as of 09:14, 14 August 2020
Authors | Boris Cergol Matjaž Omladič |
|---|---|
Publication date | 2015 |
DOI | 10.26493/1855-3974.561.37f |
Links | Original |
What Can Wikipedia and Google Tell Us About Stock Prices Under Diferent Market Regimes - scientific work related to Wikipedia quality published in 2015, written by Boris Cergol and Matjaž Omladič.
Overview
In less than five years a surprisingly high level of attention has built up in the possible connection between internet search data and stock prices. It is the main aim of this paper to point out how this connection may depend heavily on different regimes of the market, i.e. the bear market vs. the bull market. Authors consider three types of internet search data (relative Google search frequencies of company tickers, relative Google search frequencies of company names and page visits of Wikipedia articles about individual companies) and a substantial sample of companies which are members of the S&P 500 index. Authors discover two inverse patterns in stock prices: in the bear market what authors propose to term a "merry frown" and in bull market a "sour smile", both clearly seen especially for the Wikipedia data. Authors propose market neutral strategies that exploit these new patterns and yield up to 17% in average annual return during sample period from 2008 to 2013.
