COMING SOON! PQDT Open is getting a new home!

ProQuest Open Access Dissertations & Theses will remain freely available as part of a new and enhanced search experience at

Questions? Please refer to this FAQ.

Dissertation/Thesis Abstract

Using Accounting Data to Predict Firm-level and Aggregate Stock Returns
by Zhu, Wei, Ph.D., Yale University, 2013, 141; 3578482
Abstract (Summary)

This dissertation consists of three essays studying the role of accounting data in predicting distributions of stock returns. In the first essay, I explore the ability of accruals to predict future price (earnings) crashes and jumps, representing extreme negative and positive observations in the distribution of firm-level weekly returns (changes in quarterly ROA). I find that high (low) accruals predict a higher probability of price and earnings crashes (jumps) than medium accruals. In the second essay, I re-examine the ability of asset turnover growth, which reflects growth in both assets and sales, to predict future stock returns. While the prevailing view is that this relation is due to the spread between sales and asset growth, my results suggest it is driven mainly by the asset growth component. I do, however, find that this spread is positively related to future returns for a subsample of firms that did not make significant acquisitions or divestitures. In the third essay, I re-examine the puzzling negative correlation between aggregate stock returns and aggregate earnings at the quarterly level. I find that the negative aggregate returns-earnings correlation is unstable and the negative correlation for the period of 1976-2000 is mainly caused by the negative correlation between aggregate earnings and discount rate news.

Indexing (document details)
Advisor: Thomas, Jake
School: Yale University
School Location: United States -- Connecticut
Source: DAI-A 75/05(E), Dissertation Abstracts International
Subjects: Accounting, Finance
Keywords: Accruals, Aggregate returns, Asset Turnover, Earnings crash, Price Crash
Publication Number: 3578482
ISBN: 978-1-303-71570-9
Copyright © 2021 ProQuest LLC. All rights reserved. Terms and Conditions Privacy Policy Cookie Policy