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Measuring Cross-Sectional Variation in Expected Returns : = A Machine Learning Approach.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Measuring Cross-Sectional Variation in Expected Returns :/
Reminder of title:	A Machine Learning Approach.
Author:	Laporte, Douglas Jean.
Description:	1 online resource (100 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-06, Section: B.
Contained By:	Dissertations Abstracts International85-06B.
Subject:	Investments. -
Online resource:	click for full text (PQDT)
ISBN:	9798381020045

Measuring Cross-Sectional Variation in Expected Returns : = A Machine Learning Approach.
Laporte, Douglas Jean.

Measuring Cross-Sectional Variation in Expected Returns :A Machine Learning Approach. - 1 online resource (100 pages)

Source: Dissertations Abstracts International, Volume: 85-06, Section: B.

Thesis (Ph.D.)--Stanford University, 2023.

Includes bibliographical references

I develop and test a new machine learning method for estimating cross-sectional firm-level expected returns. My approach adapts the loss function of a random forest algorithm to minimize the variance of measurement errors instead of trading off bias and variance. Outof-sample tests show this approach yields reliably higher cross-sectional accuracy relative to: (a) commonly used implied cost of capital estimates, (b) factor-based estimates, and (c) estimates based on other state-of-the-art machine learning algorithms. In more detailed analyses, I find that while a small number of firm characteristics explain most of the returns predictability, the relative importance of these characteristics vary by holding horizon. Further, cross-sectional differences in expected returns exhibit limited persistence beyond two years. I also use this new approach to revisit the reported association between earnings smoothness and expected returns. Contrary to prior studies, I show that firms whose earnings are smoother relative to their cash flows earn higher (not lower) expected returns, despite being safer on many dimensions.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798381020045Subjects--Topical Terms:

557691
Investments.
Index Terms--Genre/Form:

554714
Electronic books.

Measuring Cross-Sectional Variation in Expected Returns : = A Machine Learning Approach.
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500 $a Source: Dissertations Abstracts International, Volume: 85-06, Section: B.
500 $a Advisor: Piotroski, Joseph D.;Lee, Charles;McNichols, Maureen;Smith, Kevin.
502 $a Thesis (Ph.D.)--Stanford University, 2023.
504 $a Includes bibliographical references
520 $a I develop and test a new machine learning method for estimating cross-sectional firm-level expected returns. My approach adapts the loss function of a random forest algorithm to minimize the variance of measurement errors instead of trading off bias and variance. Outof-sample tests show this approach yields reliably higher cross-sectional accuracy relative to: (a) commonly used implied cost of capital estimates, (b) factor-based estimates, and (c) estimates based on other state-of-the-art machine learning algorithms. In more detailed analyses, I find that while a small number of firm characteristics explain most of the returns predictability, the relative importance of these characteristics vary by holding horizon. Further, cross-sectional differences in expected returns exhibit limited persistence beyond two years. I also use this new approach to revisit the reported association between earnings smoothness and expected returns. Contrary to prior studies, I show that firms whose earnings are smoother relative to their cash flows earn higher (not lower) expected returns, despite being safer on many dimensions.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
650 4 $a Investments. $3 557691
650 4 $a Risk factors. $3 1466329
650 4 $a Research design. $3 1466003
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650 4 $a Design. $3 595500
650 4 $a Finance. $3 559073
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