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DC Field | Value | Language |
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dc.contributor.advisor | Shapiro, Harold T. | - |
dc.contributor.author | Sender, Ben | - |
dc.date.accessioned | 2018-08-03T14:51:52Z | - |
dc.date.available | 2018-08-03T14:51:52Z | - |
dc.date.created | 2018-04-10 | - |
dc.date.issued | 2018-08-03 | - |
dc.identifier.uri | http://arks.princeton.edu/ark:/88435/dsp012f75rb75k | - |
dc.description.abstract | Previous studies present models that predict the accuracy of earnings forecasts and provide evidence consistent with investors using these models when evaluating forecasts. In this study I mirror the framework of past studies but take a machine learning approach to predicting forecast accuracy. I evaluate the out-of-sample performance of random forest, neural network and k-nearest neighbors compared to linear regression. I find random forest and neural network have modestly stronger out-of-sample predictive performance than linear regression for annual and quarterly earnings periods. Then, I test whether the accuracy predictions from these models help explain investor reactions to forecasts. I find the predictions from each model help explain investor reactions to forecasts – consistent with the intuition that investors use these models when predicting forecast accuracy. | en_US |
dc.format.mimetype | application/pdf | - |
dc.language.iso | en | en_US |
dc.title | Predicting the Accuracy of Earnings Forecasts Using Machine Learning | en_US |
dc.type | Princeton University Senior Theses | - |
pu.date.classyear | 2018 | en_US |
pu.department | Economics | en_US |
pu.pdf.coverpage | SeniorThesisCoverPage | - |
pu.contributor.authorid | 960962655 | - |
pu.certificate | Applications of Computing Program | en_US |
pu.certificate | Finance Program | en_US |
Appears in Collections: | Economics, 1927-2024 |
Files in This Item:
File | Description | Size | Format | |
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SENDER-BEN-THESIS.pdf | 1.46 MB | Adobe PDF | Request a copy |
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