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Using Accounting Data to Predict Fir...

Zhu, Wei.

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Using Accounting Data to Predict Firm-level and Aggregate Stock Returns.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Using Accounting Data to Predict Firm-level and Aggregate Stock Returns./
Author:	Zhu, Wei.
Description:	141 p.
Notes:	Source: Dissertation Abstracts International, Volume: 75-05(E), Section: A.
Contained By:	Dissertation Abstracts International75-05A(E).
Subject:	Business Administration, Accounting. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3578482
ISBN:	9781303715709

Using Accounting Data to Predict Firm-level and Aggregate Stock Returns.
Zhu, Wei.

Using Accounting Data to Predict Firm-level and Aggregate Stock Returns. - 141 p.

Source: Dissertation Abstracts International, Volume: 75-05(E), Section: A.

Thesis (Ph.D.)--Yale University, 2013.

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.

ISBN: 9781303715709Subjects--Topical Terms:

1020666
Business Administration, Accounting.

Using Accounting Data to Predict Firm-level and Aggregate Stock Returns.
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020 $a 9781303715709
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100 1 $a Zhu, Wei. $3 1928594
245 1 0 $a Using Accounting Data to Predict Firm-level and Aggregate Stock Returns.
300 $a 141 p.
500 $a Source: Dissertation Abstracts International, Volume: 75-05(E), Section: A.
500 $a Adviser: Jake Thomas.
502 $a Thesis (Ph.D.)--Yale University, 2013.
520 $a 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.
590 $a School code: 0265.
650 4 $a Business Administration, Accounting. $3 1020666
650 4 $a Economics, Finance. $3 626650
690 $a 0272
690 $a 0508
710 2 $a Yale University. $3 515640
773 0 $t Dissertation Abstracts International $g 75-05A(E).
790 $a 0265
791 $a Ph.D.
792 $a 2013
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3578482