Deep models for empirical asset pricing (risk-premia forecast) and their interpretability
Name
1227278065-MIT.pdf
Size
3.22 MB
Format
Adobe PDF
Checksum (MD5)
31abe3976ff2391ee47f64322b6e7a10
Author(s)
Singh, Manish,S.M.Massachusetts Institute of Technology.
Advisor(s)
Andrew W. Lo.
Date Issued
2020
Publisher
Massachusetts Institute of Technology
Abstract
Risk premia measurement is an essential problem in Asset Pricing. It is estimation of how much an asset will outperform risk-free assets. Problems like noisy and non-stationarity of returns makes risk-premia estimation using Machine Learning (ML) challenging. In this work, we develop ML models that solve the associated problems with risk-premia measurement by decoupling risk-premia prediction into two independent tasks and by using ideas from Deep Learning literature that enables deep neural networks training. The models are tested robustly using different metrics where we observe that our model outperforms existing standard ML models. One another problem with ML models is their black-box nature. We also interpret the deep neural networks using local approximation based techniques that make the predictions explainable.
Description
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, September, 2020
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 49-50).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.
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