H
Hauke Fuehres
Researcher at Massachusetts Institute of Technology
Publications - 11
Citations - 730
Hauke Fuehres is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Social network analysis & Stock market. The author has an hindex of 5, co-authored 11 publications receiving 683 citations. Previous affiliations of Hauke Fuehres include University of Bamberg.
Papers
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Journal ArticleDOI
Predicting Stock Market Indicators Through Twitter “I hope it is not as bad as I fear”
TL;DR: This paper found that emotional tweet percentage significantly negatively correlated with Dow Jones, NASDAQ and S&P 500, but displayed a significant positive correlation to VIX, and that just checking on twitter for emotional outbursts of any kind gives a predictor of how the stock market will be doing the next day.
Book ChapterDOI
Predicting Asset Value through Twitter Buzz
TL;DR: In this article, the authors tried to predict financial market movement such as gold price, crude oil price, currency exchange rates and stock market indicators by analyzing Twitter posts and found that these variables are correlated to and even predictive of the financial market movements.
Journal ArticleDOI
Towards “Honest Signals” of Creativity – Identifying Personality Characteristics Through Microscopic Social Network Analysis
Peter A. Gloor,Kai Fischbach,Hauke Fuehres,Casper Lassenius,Tuomas Niinimaki,Daniel Olguin Olguin,Sandy Pentland,Arttu Piri,Johannes Putzke +8 more
TL;DR: In this article, the authors combine insights from analyzing communication in an E-mail student network of a distributed course with measurements of interaction by sociometric badges for 23 programmers in Northern Europe.
Journal ArticleDOI
Choosing the right friends – predicting success of startup entrepreneurs and innovators through their online social network structure
TL;DR: In this paper, the authors compared the success of startup entrepreneurs and innovators with their social networking behavior and found that the more central actors are in different types of networks, the more successful they are, proximity to key people also correlates with success.
Book ChapterDOI
Adding Taxonomies Obtained by Content Clustering to Semantic Social Network Analysis
TL;DR: A novel method to analyze the content of communication in social networks and extracts a taxonomy of concepts based on terms extracted from the communication’s content to provide insights not possible through conventional social network analysis.