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

Record Type:	Electronic resources : Monograph/item
Title/Author:	Measuring Cross-Sectional Variation in Expected Returns: A Machine Learning Approach.
Author:	Laporte, Douglas Jean.
Published:	Ann Arbor : ProQuest Dissertations & Theses, 2023
Description:	100 p.
Notes:	Source: Dissertations Abstracts International, Volume: 85-06, Section: B.
Notes:	Advisor: Piotroski, Joseph D.;Lee, Charles;McNichols, Maureen;Smith, Kevin.
Contained By:	Dissertations Abstracts International85-06B.
Subject:	Investments.
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30726825
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. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 100 p.

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

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

This item must not be sold to any third party vendors.

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.

ISBN: 9798381020045Subjects--Topical Terms:

187451
Investments.

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