The Loan Fee Anomaly: A Short Seller's Best Ideas.
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| Authors: | Engelberg, Joseph E.1 (AUTHOR) jengelberg@ucsd.edu, Evans, Richard B.2 (AUTHOR) evansr@darden.virginia.edu, Leonard, Greg3 (AUTHOR) gregory_leonard@kenan-flagler.unc.edu, Reed, Adam V.4 (AUTHOR) adam_reed@unc.edu, Ringgenberg, Matthew C.5 (AUTHOR) matthew.ringgenberg@eccles.utah.edu |
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| Source: | Management Science (INFORMS). Jul2025, Vol. 71 Issue 7, p5529-5551. 23p. |
| Subject Terms: | *Short selling (Securities), *Transaction costs, *Investment management, *Financial markets, *Forecasting, Outliers (Statistics) |
| Abstract: | We find that equity loan fees, which have been largely ignored by the anomalies literature, are the best predictor of cross-sectional returns. When compared with 102 other anomalies and other short-selling measures, the loan fee anomaly has the highest monthly long-short return (4.01%), the highest monthly Sharpe Ratio (0.66), and, unlike other anomalies, exhibits strong persistence throughout the sample. Although prior work has shown that existing anomalies reside in high loan fee stocks, we find that 42% of loan fee outperformance is due to unique information not contained in other anomalies. Future papers that examine cross-sectional predictors of returns should include the single most effective predictor: loan fees. This paper was accepted by Victoria Ivashina, finance. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.00152. [ABSTRACT FROM AUTHOR] |
| Database: | Entrepreneurial Studies Source |
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| Header | DbId: ent DbLabel: Entrepreneurial Studies Source An: 187524664 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Engelberg%2C+Joseph+E%2E%22">Engelberg, Joseph E.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> jengelberg@ucsd.edu</i><br /><searchLink fieldCode="AR" term="%22Evans%2C+Richard+B%2E%22">Evans, Richard B.</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> evansr@darden.virginia.edu</i><br /><searchLink fieldCode="AR" term="%22Leonard%2C+Greg%22">Leonard, Greg</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> gregory_leonard@kenan-flagler.unc.edu</i><br /><searchLink fieldCode="AR" term="%22Reed%2C+Adam+V%2E%22">Reed, Adam V.</searchLink><relatesTo>4</relatesTo> (AUTHOR)<i> adam_reed@unc.edu</i><br /><searchLink fieldCode="AR" term="%22Ringgenberg%2C+Matthew+C%2E%22">Ringgenberg, Matthew C.</searchLink><relatesTo>5</relatesTo> (AUTHOR)<i> matthew.ringgenberg@eccles.utah.edu</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Management+Science+%28INFORMS%29%22">Management Science (INFORMS)</searchLink>. Jul2025, Vol. 71 Issue 7, p5529-5551. 23p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Short+selling+%28Securities%29%22">Short selling (Securities)</searchLink><br />*<searchLink fieldCode="DE" term="%22Transaction+costs%22">Transaction costs</searchLink><br />*<searchLink fieldCode="DE" term="%22Investment+management%22">Investment management</searchLink><br />*<searchLink fieldCode="DE" term="%22Financial+markets%22">Financial markets</searchLink><br />*<searchLink fieldCode="DE" term="%22Forecasting%22">Forecasting</searchLink><br /><searchLink fieldCode="DE" term="%22Outliers+%28Statistics%29%22">Outliers (Statistics)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: We find that equity loan fees, which have been largely ignored by the anomalies literature, are the best predictor of cross-sectional returns. When compared with 102 other anomalies and other short-selling measures, the loan fee anomaly has the highest monthly long-short return (4.01%), the highest monthly Sharpe Ratio (0.66), and, unlike other anomalies, exhibits strong persistence throughout the sample. Although prior work has shown that existing anomalies reside in high loan fee stocks, we find that 42% of loan fee outperformance is due to unique information not contained in other anomalies. Future papers that examine cross-sectional predictors of returns should include the single most effective predictor: loan fees. This paper was accepted by Victoria Ivashina, finance. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.00152. [ABSTRACT FROM AUTHOR] |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1287/mnsc.2023.00152 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 23 StartPage: 5529 Subjects: – SubjectFull: Short selling (Securities) Type: general – SubjectFull: Transaction costs Type: general – SubjectFull: Investment management Type: general – SubjectFull: Financial markets Type: general – SubjectFull: Forecasting Type: general – SubjectFull: Outliers (Statistics) Type: general Titles: – TitleFull: The Loan Fee Anomaly: A Short Seller's Best Ideas. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Engelberg, Joseph E. – PersonEntity: Name: NameFull: Evans, Richard B. – PersonEntity: Name: NameFull: Leonard, Greg – PersonEntity: Name: NameFull: Reed, Adam V. – PersonEntity: Name: NameFull: Ringgenberg, Matthew C. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: Jul2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 00251909 Numbering: – Type: volume Value: 71 – Type: issue Value: 7 Titles: – TitleFull: Management Science (INFORMS) Type: main |
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