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Please use this identifier to cite or link to this item: http://arks.princeton.edu/ark:/88435/dsp012f75rb75k
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dc.contributor.advisorShapiro, Harold T.-
dc.contributor.authorSender, Ben-
dc.date.accessioned2018-08-03T14:51:52Z-
dc.date.available2018-08-03T14:51:52Z-
dc.date.created2018-04-10-
dc.date.issued2018-08-03-
dc.identifier.urihttp://arks.princeton.edu/ark:/88435/dsp012f75rb75k-
dc.description.abstractPrevious 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.mimetypeapplication/pdf-
dc.language.isoenen_US
dc.titlePredicting the Accuracy of Earnings Forecasts Using Machine Learningen_US
dc.typePrinceton University Senior Theses-
pu.date.classyear2018en_US
pu.departmentEconomicsen_US
pu.pdf.coverpageSeniorThesisCoverPage-
pu.contributor.authorid960962655-
pu.certificateApplications of Computing Programen_US
pu.certificateFinance Programen_US
Appears in Collections:Economics, 1927-2024

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