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https://hdl.handle.net/2440/130562
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DC Field | Value | Language |
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dc.contributor.author | Sun, M. | - |
dc.contributor.author | Glabadanidis, P. | - |
dc.date.issued | 2022 | - |
dc.identifier.citation | International Review of Finance, 2022; 22(1):114-142 | - |
dc.identifier.issn | 1369-412X | - |
dc.identifier.issn | 1468-2443 | - |
dc.identifier.uri | http://hdl.handle.net/2440/130562 | - |
dc.description | First published: 31 January 2021 | - |
dc.description.abstract | We find that technical indicators have substantial predictive power over the Chinese equity risk premium. Technical indicators complement macroeconomic variables in predicting the Chinese equity risk premium. The predictive power is more pronounced at a weekly frequency rather than a monthly frequency as suggested by the outof- sample tests. Furthermore, weekly-level technical indicators can predict the firm-level excess returns while monthly-level indicators cannot. The weekly-level indicators can also predict sorted portfolio excess return and risk factors. Overall, in comparison with the US stock market, the Chinese stock market seems to have higherfrequency price trends. The cross-sectional predictive power of the technical indicators is closely related to market capitalization rather than volatility. | - |
dc.description.statementofresponsibility | Mingwei Sun, Paskalis Glabadanidis | - |
dc.language.iso | en | - |
dc.publisher | Wiley | - |
dc.rights | © 2021 International Review of Finance Ltd. 2021 | - |
dc.source.uri | http://dx.doi.org/10.1111/irfi.12344 | - |
dc.subject | equity risk premium predictability; macroeconomic variables; momentum; moving averages; out-of-sample forecasts; shortterm trend; technical analysis; the Chinese stock market | - |
dc.title | Can technical indicators predict the Chinese equity risk premium? | - |
dc.type | Journal article | - |
dc.identifier.doi | 10.1111/irfi.12344 | - |
pubs.publication-status | Published | - |
dc.identifier.orcid | Glabadanidis, P. [0000-0003-0247-8430] | - |
Appears in Collections: | Aurora harvest 8 Business School publications |
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