PE Ratios, PEG Ratios, and Estimating the Implied Expected Rate of Return on Equity Capital

衡平法 货币经济学 金融经济学 文件夹 资本成本 精算学 资本资产定价模型
作者
Peter D. Easton
出处
期刊:The Accounting Review [American Accounting Association]
卷期号:79 (1): 73-95 被引量:1128
标识
DOI:10.2308/accr.2004.79.1.73
摘要

I describe a model of earnings and earnings growth and I demonstrate how this model may be used to obtain estimates of the expected rate of return on equity capital. These estimates are compared with estimates of the expected rate of return implied by commonly used heuristics—viz., the PEG ratio and the PE ratio. Proponents of the PEG ratio (which is the price-earnings [PE] ratio divided by the short-term earnings growth rate) argue that this ratio takes account of differences in short-run earnings growth, providing a ranking that is superior to the ranking based on PE ratios. But even though the PEG ratio may provide an improvement over the PE ratio, it is arguably still too simplistic because it implicitly assumes that the short-run growth forecast also captures the long-run future. I provide a means of simultaneously estimating the expected rate of return and the rate of change in abnormal growth in earnings beyond the (short) forecast horizon—thereby refining the PEG ratio ranking. The method may also be used by researchers interested in determining the effects of various factors (such as disclosure quality, cross-listing, etc.) on the cost of equity capital. Although the correlation between the refined estimates and estimates of the expected rate of return implied by the PEG ratio is high, supporting the use of the PEG ratio as a parsimonious way to rank stocks, the estimates of the expected rate of return based on the PEG ratio are biased downward. This correlation is much lower and the downward bias is much larger for estimates of the expected rate of return based on the PE ratio. I provide evidence that stocks for which the downward bias is higher can be identified a priori.

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
神揽星辰入梦完成签到,获得积分10
刚刚
NovermberRain发布了新的文献求助10
刚刚
一名不知死活的研究生完成签到,获得积分10
3秒前
浮荒发布了新的文献求助10
3秒前
123发布了新的文献求助10
4秒前
4秒前
甜甜圈完成签到,获得积分10
5秒前
天天完成签到,获得积分10
5秒前
7秒前
biu完成签到,获得积分10
11秒前
李蛋完成签到,获得积分20
11秒前
北冥鱼完成签到,获得积分10
11秒前
王娇完成签到 ,获得积分10
11秒前
天天发布了新的文献求助10
12秒前
wuxunxun2015发布了新的文献求助10
12秒前
shelly7788完成签到 ,获得积分10
12秒前
Pattis完成签到 ,获得积分10
13秒前
13秒前
可爱的函函应助李治稳采纳,获得10
14秒前
田様应助零零零零采纳,获得10
15秒前
张爱学发布了新的文献求助10
15秒前
浮荒完成签到,获得积分20
15秒前
Trueman发布了新的文献求助10
16秒前
万能图书馆应助NovermberRain采纳,获得10
16秒前
爱科研的小朋友完成签到 ,获得积分10
17秒前
biu发布了新的文献求助10
17秒前
18秒前
20秒前
小马甲应助浮荒采纳,获得10
20秒前
鸭鸭乐园完成签到,获得积分10
22秒前
23秒前
JZ发布了新的文献求助10
23秒前
柠檬完成签到 ,获得积分10
24秒前
24秒前
25秒前
量子星尘发布了新的文献求助10
26秒前
JKL发布了新的文献求助10
27秒前
27秒前
华仔应助清秀的沉鱼采纳,获得30
27秒前
零零零零发布了新的文献求助10
29秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
人脑智能与人工智能 1000
King Tyrant 720
Silicon in Organic, Organometallic, and Polymer Chemistry 500
Principles of Plasma Discharges and Materials Processing, 3rd Edition 400
Pharmacology for Chemists: Drug Discovery in Context 400
El poder y la palabra: prensa y poder político en las dictaduras : el régimen de Franco ante la prensa y el periodismo 400
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 计算机科学 有机化学 物理 生物化学 纳米技术 复合材料 内科学 化学工程 人工智能 催化作用 遗传学 数学 基因 量子力学 物理化学
热门帖子
关注 科研通微信公众号,转发送积分 5603979
求助须知:如何正确求助?哪些是违规求助? 4688850
关于积分的说明 14856611
捐赠科研通 4695971
什么是DOI,文献DOI怎么找? 2541092
邀请新用户注册赠送积分活动 1507256
关于科研通互助平台的介绍 1471832