dc.contributor.author |
Geertsema, Paul |
|
dc.contributor.author |
Lu, Helen |
|
dc.date.accessioned |
2021-03-19T00:54:02Z |
|
dc.date.available |
2021-03-19T00:54:02Z |
|
dc.date.issued |
2021-1-27 |
|
dc.identifier.uri |
https://hdl.handle.net/2292/54753 |
|
dc.description.abstract |
We predict cumulative stock returns over horizons from 1 month to 10 years using a tree-based machine learning approach. Cumulative stock returns are significantly predictable in the cross-section over all horizons. A hedge portfolio generates 250 bp/month at a 1 year horizon and 110 bp/month at a 10 year horizon. Individual stock returns are significantly predictable at all horizons in panel data. Cashflow and momentum related predictors are mostly important at shorter horizons while dividend yield and value related predictors are more important at longer horizons. By contrast, variables related to turnover and volatility are influential at all horizons. |
|
dc.relation.ispartofseries |
SSRN |
|
dc.rights |
Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher. |
|
dc.rights.uri |
https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm |
|
dc.title |
The Cross-section of Long-run Expected Stock Returns |
|
dc.type |
Journal Article |
|
dc.date.updated |
2021-02-02T04:09:59Z |
|
dc.rights.holder |
Copyright: The author |
en |
pubs.author-url |
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3774548 |
|
dc.rights.accessrights |
http://purl.org/eprint/accessRights/RestrictedAccess |
en |
pubs.subtype |
Article |
|
pubs.elements-id |
837052 |
|
pubs.online-publication-date |
2021-1-27 |
|