Random Forest Prediction of IPO Underpricing
The prediction of initial returns on initial public offerings (IPOs) is a complex matter. The independent variables identified in the literature mix strong and weak predictors, their explanatory power is limited, and samples include a sizable number of outliers. In this context, we suggest that rand...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2017-06-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | http://www.mdpi.com/2076-3417/7/6/636 |
| _version_ | 1819060867482779648 |
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| author | David Quintana Yago Sáez Pedro Isasi |
| author_facet | David Quintana Yago Sáez Pedro Isasi |
| author_sort | David Quintana |
| collection | DOAJ |
| description | The prediction of initial returns on initial public offerings (IPOs) is a complex matter. The independent variables identified in the literature mix strong and weak predictors, their explanatory power is limited, and samples include a sizable number of outliers. In this context, we suggest that random forests are a potentially powerful tool. In this paper, we benchmark this algorithm against a set of eight classic machine learning algorithms. The results of this comparison show that random forests outperform the alternatives in terms of mean and median predictive accuracy. The technique also provided the second smallest error variance among the stochastic algorithms. The experimental work also supports the potential of random forests for two practical applications: IPO pricing and IPO trading. |
| first_indexed | 2024-12-21T14:33:49Z |
| format | Article |
| id | doaj.art-efa56842fc20422b95e093d13f2ea56b |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-12-21T14:33:49Z |
| publishDate | 2017-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-efa56842fc20422b95e093d13f2ea56b2022-12-21T19:00:25ZengMDPI AGApplied Sciences2076-34172017-06-017663610.3390/app7060636app7060636Random Forest Prediction of IPO UnderpricingDavid Quintana0Yago Sáez1Pedro Isasi2Department of Computer Science and Engineering, Universidad Carlos III de Madrid, 28903 Madrid, SpainDepartment of Computer Science and Engineering, Universidad Carlos III de Madrid, 28903 Madrid, SpainDepartment of Computer Science and Engineering, Universidad Carlos III de Madrid, 28903 Madrid, SpainThe prediction of initial returns on initial public offerings (IPOs) is a complex matter. The independent variables identified in the literature mix strong and weak predictors, their explanatory power is limited, and samples include a sizable number of outliers. In this context, we suggest that random forests are a potentially powerful tool. In this paper, we benchmark this algorithm against a set of eight classic machine learning algorithms. The results of this comparison show that random forests outperform the alternatives in terms of mean and median predictive accuracy. The technique also provided the second smallest error variance among the stochastic algorithms. The experimental work also supports the potential of random forests for two practical applications: IPO pricing and IPO trading.http://www.mdpi.com/2076-3417/7/6/636random forestinitial public offeringpredictionunderpricing |
| spellingShingle | David Quintana Yago Sáez Pedro Isasi Random Forest Prediction of IPO Underpricing Applied Sciences random forest initial public offering prediction underpricing |
| title | Random Forest Prediction of IPO Underpricing |
| title_full | Random Forest Prediction of IPO Underpricing |
| title_fullStr | Random Forest Prediction of IPO Underpricing |
| title_full_unstemmed | Random Forest Prediction of IPO Underpricing |
| title_short | Random Forest Prediction of IPO Underpricing |
| title_sort | random forest prediction of ipo underpricing |
| topic | random forest initial public offering prediction underpricing |
| url | http://www.mdpi.com/2076-3417/7/6/636 |
| work_keys_str_mv | AT davidquintana randomforestpredictionofipounderpricing AT yagosaez randomforestpredictionofipounderpricing AT pedroisasi randomforestpredictionofipounderpricing |