%0 Journal Article %1 heiberger_predicting_2018 %A Heiberger, Raphael H. %D 2018 %J Physica A: Statistical Mechanics and its Applications %K Bayes Economic Econophysics, Machine Naïve Stock classifier, growth, learning, networks %P 102--111 %R 10.1016/j.physa.2017.07.022 %T Predicting economic growth with stock networks %U http://www.sciencedirect.com/science/article/pii/S0378437117307100 %V 489 %X Networks derived from stock prices are often used to model developments on financial markets and are tightly intertwined with crises. Yet, the influence of changing market topologies on the broader economy (i.e. GDP) is unclear. In this paper, we propose a Bayesian approach that utilizes individual-level network measures of companies as lagged probabilistic features to predict national economic growth. We use a comprehensive data set consisting of Standard and Poor’s 500 corporations from January 1988 until October 2016. The final model forecasts correctly all major recession and prosperity phases of the U.S. economy up to one year ahead. By employing different network measures on the level of corporations, we can also identify which companies’ stocks possess a key role in a changing economic environment and may be used as indication of critical (and prosperous) developments. More generally, the proposed approach allows to predict probabilities for different overall states of social entities by using local network positions and could be applied on various phenomena.

@article{heiberger_predicting_2018, abstract = {Networks derived from stock prices are often used to model developments on financial markets and are tightly intertwined with crises. Yet, the influence of changing market topologies on the broader economy (i.e. GDP) is unclear. In this paper, we propose a Bayesian approach that utilizes individual-level network measures of companies as lagged probabilistic features to predict national economic growth. We use a comprehensive data set consisting of Standard and Poor’s 500 corporations from January 1988 until October 2016. The final model forecasts correctly all major recession and prosperity phases of the U.S. economy up to one year ahead. By employing different network measures on the level of corporations, we can also identify which companies’ stocks possess a key role in a changing economic environment and may be used as indication of critical (and prosperous) developments. More generally, the proposed approach allows to predict probabilities for different overall states of social entities by using local network positions and could be applied on various phenomena.}, added-at = {2020-05-04T15:23:37.000+0200}, author = {Heiberger, Raphael H.}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/255f17f2e962d4dc3b589738eaca05476/raphei}, doi = {10.1016/j.physa.2017.07.022}, file = {Heiberger - 2018 - Predicting economic growth with stock networks.pdf:C\:\\Users\\ac135138\\Zotero\\storage\\CZJFW65A\\Heiberger - 2018 - Predicting economic growth with stock networks.pdf:application/pdf;ScienceDirect Snapshot:C\:\\Users\\ac135138\\Zotero\\storage\\22DU9NNX\\S0378437117307100.html:text/html}, interhash = {91a929c6ff6f05b61f41f2cc5645c06b}, intrahash = {55f17f2e962d4dc3b589738eaca05476}, issn = {0378-4371}, journal = {Physica A: Statistical Mechanics and its Applications}, keywords = {Bayes Economic Econophysics, Machine Naïve Stock classifier, growth, learning, networks}, month = jan, pages = {102--111}, timestamp = {2020-05-04T13:23:37.000+0200}, title = {Predicting economic growth with stock networks}, url = {http://www.sciencedirect.com/science/article/pii/S0378437117307100}, urldate = {2018-01-16}, volume = 489, year = 2018 }