Hedge Fund Returns and Total Factor Productivity
DC Field | Value | Language |
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dc.contributor.author | 최수정 | - |
dc.date.accessioned | 2024-05-22T02:00:23Z | - |
dc.date.available | 2024-05-22T02:00:23Z | - |
dc.date.issued | 2024-04 | - |
dc.identifier.issn | 2005-8187 | - |
dc.identifier.issn | 2713-5543 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/ssu/handle/2018.sw.ssu/49588 | - |
dc.description.abstract | This study explores whether hedge funds’ investment behavior can predict variations in productivity levels using a structural vector autoregressive model (SVAR) and a vector error correction model (VECM). As informed traders in the stock market with superior skills, hedge funds may quickly capture news shocks regarding future production growth in advance. With quarterly series of TFP provided by John Fernald (2014) and hedge fund index (HFI) returns obtained from the Credit Suisse/Tremont database, I find a contemporaneous correlation coefficient of 0.9791 between two endogenous variables over the sample period from 1Q:1994 to 2Q:2023, indicating a high degree of similarity in their movements. A Granger Causality Test rejects the hypothesis, “ DeltaLn(HFI) does not Granger Cause DeltaTFP”, suggesting that the information inferred from the hedge fund index are valuable in predicting future economic productivity. Finally, the forecast error variance decompositions using the VECM model indicate that over 65% of the variation in even after 20 quarters can be attributed to a shock to the DeltaLn(HFI). | - |
dc.format.extent | 23 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | 한국증권학회 | - |
dc.title | Hedge Fund Returns and Total Factor Productivity | - |
dc.title.alternative | Hedge Fund Returns and Total Factor Productivity | - |
dc.type | Article | - |
dc.identifier.doi | 10.26845/KJFS.2024.04.53.2.309 | - |
dc.identifier.bibliographicCitation | 한국증권학회지, v.53, no.2, pp 309 - 331 | - |
dc.identifier.kciid | ART003074493 | - |
dc.identifier.scopusid | 2-s2.0-85192936731 | - |
dc.citation.endPage | 331 | - |
dc.citation.number | 2 | - |
dc.citation.startPage | 309 | - |
dc.citation.title | 한국증권학회지 | - |
dc.citation.volume | 53 | - |
dc.identifier.url | https://www.e-kjfs.org/journal/view.php?doi=10.26845/KJFS.2024.04.53.2.309 | - |
dc.publisher.location | 대한민국 | - |
dc.description.isOpenAccess | Y | - |
dc.subject.keywordAuthor | 헤지펀드 | - |
dc.subject.keywordAuthor | 정보보유 거래자 | - |
dc.subject.keywordAuthor | 미국 총요소생산성 | - |
dc.subject.keywordAuthor | 구조적 벡터자기회귀 모형 | - |
dc.subject.keywordAuthor | 벡터오차수정 모형 | - |
dc.subject.keywordAuthor | Hedge Funds | - |
dc.subject.keywordAuthor | Informed Traders | - |
dc.subject.keywordAuthor | U.S. Total Factor Productivity | - |
dc.subject.keywordAuthor | SVAR | - |
dc.subject.keywordAuthor | VECM | - |
dc.description.journalRegisteredClass | scopus | - |
dc.description.journalRegisteredClass | kci | - |
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