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

<p> 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.</p>

Identiferoai:union.ndltd.org:PROQUEST/oai:pqdtoai.proquest.com:3578482
Date26 February 2014
CreatorsZhu, Wei
PublisherYale University
Source SetsProQuest.com
LanguageEnglish
Detected LanguageEnglish
Typethesis

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