FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2743196251> ?p ?o ?g. }

• W2743196251 endingPage "111" @default.
• W2743196251 startingPage "102" @default.
• W2743196251 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." @default.
• W2743196251 created "2017-08-17" @default.
• W2743196251 creator A5045935650 @default.
• W2743196251 date "2018-01-01" @default.
• W2743196251 modified "2023-10-02" @default.
• W2743196251 title "Predicting economic growth with stock networks" @default.
• W2743196251 cites W1046441252 @default.
• W2743196251 cites W1901616594 @default.
• W2743196251 cites W1965591532 @default.
• W2743196251 cites W1966402429 @default.
• W2743196251 cites W2000080801 @default.
• W2743196251 cites W2004824191 @default.
• W2743196251 cites W2013075675 @default.
• W2743196251 cites W2030210484 @default.
• W2743196251 cites W2031849821 @default.
• W2743196251 cites W2041933291 @default.
• W2743196251 cites W2047940964 @default.
• W2743196251 cites W2052110486 @default.
• W2743196251 cites W2053615983 @default.
• W2743196251 cites W2056944867 @default.
• W2743196251 cites W2060154096 @default.
• W2743196251 cites W2064951420 @default.
• W2743196251 cites W2094665138 @default.
• W2743196251 cites W2094688862 @default.
• W2743196251 cites W2096526352 @default.
• W2743196251 cites W2099815494 @default.
• W2743196251 cites W2103852640 @default.
• W2743196251 cites W2112615110 @default.
• W2743196251 cites W2117899923 @default.
• W2743196251 cites W2123199877 @default.
• W2743196251 cites W2129737628 @default.
• W2743196251 cites W2131681506 @default.
• W2743196251 cites W2138144286 @default.
• W2743196251 cites W2143426320 @default.
• W2743196251 cites W2158970114 @default.
• W2743196251 cites W2160268549 @default.
• W2743196251 cites W2167638534 @default.
• W2743196251 cites W2171707538 @default.
• W2743196251 cites W2505700388 @default.
• W2743196251 cites W2508447208 @default.
• W2743196251 cites W2527457764 @default.
• W2743196251 cites W2738003060 @default.
• W2743196251 cites W3093578674 @default.
• W2743196251 cites W3104358692 @default.
• W2743196251 cites W3121160289 @default.
• W2743196251 cites W3121792421 @default.
• W2743196251 cites W3126021322 @default.
• W2743196251 cites W3126128307 @default.
• W2743196251 cites W2318485631 @default.
• W2743196251 doi "https://doi.org/10.1016/j.physa.2017.07.022" @default.
• W2743196251 hasPublicationYear "2018" @default.
• W2743196251 type Work @default.
• W2743196251 sameAs 2743196251 @default.
• W2743196251 citedByCount "27" @default.
• W2743196251 countsByYear W27431962512018 @default.
• W2743196251 countsByYear W27431962512019 @default.
• W2743196251 countsByYear W27431962512020 @default.
• W2743196251 countsByYear W27431962512021 @default.
• W2743196251 countsByYear W27431962512022 @default.
• W2743196251 countsByYear W27431962512023 @default.
• W2743196251 crossrefType "journal-article" @default.
• W2743196251 hasAuthorship W2743196251A5045935650 @default.
• W2743196251 hasConcept C10138342 @default.
• W2743196251 hasConcept C106159729 @default.
• W2743196251 hasConcept C111919701 @default.
• W2743196251 hasConcept C127413603 @default.
• W2743196251 hasConcept C139719470 @default.
• W2743196251 hasConcept C144587487 @default.
• W2743196251 hasConcept C149782125 @default.
• W2743196251 hasConcept C154945302 @default.
• W2743196251 hasConcept C162324750 @default.
• W2743196251 hasConcept C163068380 @default.
• W2743196251 hasConcept C166957645 @default.
• W2743196251 hasConcept C19244329 @default.
• W2743196251 hasConcept C195742910 @default.
• W2743196251 hasConcept C199845137 @default.
• W2743196251 hasConcept C204036174 @default.
• W2743196251 hasConcept C205649164 @default.
• W2743196251 hasConcept C2776554220 @default.
• W2743196251 hasConcept C2776760741 @default.
• W2743196251 hasConcept C2778300220 @default.
• W2743196251 hasConcept C2779343474 @default.
• W2743196251 hasConcept C2780299701 @default.
• W2743196251 hasConcept C33724603 @default.
• W2743196251 hasConcept C41008148 @default.
• W2743196251 hasConcept C49937458 @default.
• W2743196251 hasConcept C50522688 @default.
• W2743196251 hasConcept C78519656 @default.
• W2743196251 hasConceptScore W2743196251C10138342 @default.
• W2743196251 hasConceptScore W2743196251C106159729 @default.
• W2743196251 hasConceptScore W2743196251C111919701 @default.
• W2743196251 hasConceptScore W2743196251C127413603 @default.
• W2743196251 hasConceptScore W2743196251C139719470 @default.
• W2743196251 hasConceptScore W2743196251C144587487 @default.
• W2743196251 hasConceptScore W2743196251C149782125 @default.
• W2743196251 hasConceptScore W2743196251C154945302 @default.
• W2743196251 hasConceptScore W2743196251C162324750 @default.
• W2743196251 hasConceptScore W2743196251C163068380 @default.

• next