Returns around Earnings Announcements for Companies with Seasonality in Earnings
| dc.contributor.author | Dokania, Ritika | en |
| dc.contributor.committeechair | Singal, Vijay | en |
| dc.contributor.committeemember | Deng, Xinwei | en |
| dc.contributor.committeemember | Kim, Inyoung | en |
| dc.contributor.department | Statistics | en |
| dc.date.accessioned | 2018-07-03T08:01:12Z | en |
| dc.date.available | 2018-07-03T08:01:12Z | en |
| dc.date.issued | 2018-07-02 | en |
| dc.description.abstract | This thesis examines returns around earnings announcements for companies with seasonality in earnings. Earnrank is used as a measure of seasonality where earnrank for a company is calculated quarterly by taking last five years of earnings data, ranking them and taking the average of the ranks for the respective quarter. For seasonal firms, we find robust evidence that abnormal returns are created when such firms announce their earnings for the highest seasonality quarter as measured by their earnrank. Additionally, the results were consistent for different time periods and abnormal returns were found to increase over time. We also performed the analysis industry-wise and found significant difference in returns for most and least seasonal firms in Manufacturing, Financial and Construction sectors. The results for Construction sector is in conflict to our hypothesis and require further exploration. We also study which kind of firms exhibit seasonality and found evidence for high seasonality in large firms, value firms, old firms, firms with lower turnover and firms with lower accruals. Lastly, we studied factors determining abnormal returns relative to the four-factor model and found size to be a significant explanatory variable. The long-short portfolio based on seasonality generated an alpha of 62 basis points per month. | en |
| dc.description.abstractgeneral | This thesis examines returns around earnings announcements for companies with seasonality in earnings. Earning Seasonality is a phenomenon wherein firms show predictably higher earnings in one quarter of the year due to the underlying cyclical nature of the firms business. The quarter with the highest earnings is termed as positive seasonality quarter. Earnrank is used as a measure of seasonality where earnrank for a company is calculated quarterly by taking last five years of earnings data, ranking them and taking the average of the ranks for the respective quarter. For seasonal firms, we find robust evidence that abnormal returns are created when such firms announce their earnings for the highest seasonality quarter as measured by their earnrank. Additionally, the results were consistent for different time periods and abnormal returns were found to increase over time. We also performed the analysis industry-wise and found significant difference in returns for most and least seasonal firms in Manufacturing, Financial and Construction sectors. The results for Construction sector is in conflict to our hypothesis and require further exploration. We also study which kind of firms exhibit seasonality and found evidence for high seasonality in large firms, value firms, old firms, firms with lower turnover and firms with lower accruals. Lastly, we studied factors determining abnormal returns relative to the four-factor model and found size to be a significant explanatory variable. The long-short portfolio based on seasonality generated an alpha of 62 basis points per month. | en |
| dc.description.degree | Master of Science | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:16460 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/83845 | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | Abnormal Returns | en |
| dc.subject | Seasonality | en |
| dc.title | Returns around Earnings Announcements for Companies with Seasonality in Earnings | en |
| dc.type | Thesis | en |
| thesis.degree.discipline | Statistics | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | masters | en |
| thesis.degree.name | Master of Science | en |
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