{"id":35101,"date":"2024-08-19T16:32:51","date_gmt":"2024-08-19T14:32:51","guid":{"rendered":"https:\/\/quantpedia.com\/?p=35101"},"modified":"2025-06-04T14:01:24","modified_gmt":"2025-06-04T12:01:24","slug":"payout-adjusted-cape","status":"publish","type":"post","link":"https:\/\/vvv.quantpedia.com\/es\/payout-adjusted-cape\/","title":{"rendered":"Payout-Adjusted CAPE"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\"><strong>Professor Robert Shiller&#8217;s <a href=\"https:\/\/\\\/\\\/new-fmhwbzh6ghd9hede.swedencentral-01.azurewebsites.net\/strategies\/value-factor-effect-within-countries\/\" title=\"\">CAPE (cyclically adjusted price-to-earnings) ratio<\/a> is well-known among the investment community. His methodology for assessing a valuation of the U.S. equity market is undoubtedly the most cited and discussed. Therefore, it&#8217;s not surprising that there exists quite a lot of papers that try to refine and <a href=\"https:\/\/\\\/\\\/new-fmhwbzh6ghd9hede.swedencentral-01.azurewebsites.net\/the-cape-ratio-and-machine-learning\/\" title=\"\">expand the CAPE&#8217;s methodology<\/a>. One such last attempt is the work of James White and Victor Haghani, whose research paper revolves around the use of a modified version of the Cyclically-Adjusted Price Earnings (CAPE) ratio, termed P-CAPE. Their methodology aims to improve the estimation of long-term expected real returns of the stock market by incorporating the dividend payout ratio into the traditional CAPE metric.<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The traditional CAPE ratio, introduced by Campbell and Shiller, uses a 10-year moving average of inflation-adjusted earnings to smooth out business cycle fluctuations and provide a more stable measure of fundamental value. The reciprocal of CAPE, known as the Cyclically-Adjusted Earnings Yield (CAEY), is commonly used to estimate long-term real returns.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">However, a shortcoming of the traditional CAPE is that it does not account for the portion of earnings not paid out as dividends, which are either reinvested in the business or used for stock buybacks. These retained earnings can lead to future earnings growth, which should be considered when estimating long-term returns.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The proposed strategy, P-CAPE, adjusts the cyclically-adjusted earnings by factoring in the dividend payout ratio. Specifically, it calculates the payout and cyclically-adjusted earnings (P-CAE) by bringing forward the earnings not paid out as dividends at a growth rate equal to the CAEY at the time of those earnings. This adjustment results in a higher and, theoretically, more accurate measure of cyclically-adjusted earnings, especially in periods of low dividend payout ratios.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">By using P-CAE to compute the earnings yield (P-CAEY), investors can obtain a better estimate of the long-term expected real return of the stock market. This methodology is supported by historical data, which shows that P-CAEY explains a higher percentage of the variance in 10-year prospective real returns compared to the traditional CAEY.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Authors: <\/strong><a title=\"View other papers by this author\" href=\"https:\/\/papers.ssrn.com\/sol3\/cf_dev\/AbsByAuth.cfm?per_id=2619904\" target=\"_blank\" rel=\"noopener\">James White<\/a> and <a title=\"View other papers by this author\" href=\"https:\/\/papers.ssrn.com\/sol3\/cf_dev\/AbsByAuth.cfm?per_id=2500639\" target=\"_blank\" rel=\"noopener\">Victor Haghani<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Title: Introducing P-CAPE: Incorporating the Dividend Payout Ratio Improves Investors&#8217; Favorite Estimator of Stock Market Returns<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Link<\/strong>: <a title=\"https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=4874559\" href=\"https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=4874559\" target=\"_blank\" rel=\"nofollow noopener\">https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=4874559<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Abstract:<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The Cyclically-Adjusted Price Earnings ratio, known as CAPE, is the most commonly used metric for estimating the long-term expected real return of the stock market. The reciprocal of CAPE (1\/CAPE), known as the Cyclically-Adjusted Earnings Yield (CAEY), is the metric many investors use to estimate the long-term expected real return of the stock market. A shortcoming of Shiller and Campbell\u2019s definition of cyclically-adjusted earnings is that it doesn\u2019t take account of the fact that, in general, companies don\u2019t pay out all their earnings as dividends each year. The fraction of earnings not paid out in dividends is either reinvested in the business or paid out via stock buybacks. Reinvesting earnings in the business is done in the expectation of growing future earnings, and this earnings growth should ideally be accounted for when smoothing earnings over the previous ten years for the purpose of predicting long-term future earnings. Buying back stock doesn\u2019t grow top line earnings, but it does reduce shares outstanding and hence increases earnings per share. We believe a better measure of cyclically-adjusted earnings should directly account for the logic that retained earnings should increase earnings per share over time in addition to the inflation adjustment already part of Campbell and Shiller\u2019s measure. We show that such an adjustment is simple to implement and, when used to compute earnings yield, should and does provide a better measure of the long-term expected real return of the stock market. Note that, for dividend payout ratios of less than 100% and for positive earnings yields, P-CAE will be higher than Shiller and Campbell\u2019s cyclically-adjusted earnings, which are only adjusted for inflation. Those who were attracted to the logic of Campbell and Shiller\u2019s CAPE to begin with should find their measure adjusted for dividend payouts an adjustment worth adopting.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">As always, we present several exciting figures and tables:<\/p>\n\n\n\n<figure class=\"wp-block-image aligncenter\"><img fetchpriority=\"high\" decoding=\"async\" width=\"766\" height=\"1024\" src=\"https:\/\/\\\/\\\/new-fmhwbzh6ghd9hede.swedencentral-01.azurewebsites.net\/wp-content\/uploads\/2024\/08\/ssrn-4874559_page-0005-scaled-e1722706622104-766x1024.jpg\" alt=\"\" class=\"wp-image-35115\" srcset=\"https:\/\/vvv.quantpedia.com\/wp-content\/uploads\/2024\/08\/ssrn-4874559_page-0005-scaled-e1722706622104-766x1024.jpg 766w, https:\/\/vvv.quantpedia.com\/wp-content\/uploads\/2024\/08\/ssrn-4874559_page-0005-scaled-e1722706622104-225x300.jpg 225w, https:\/\/vvv.quantpedia.com\/wp-content\/uploads\/2024\/08\/ssrn-4874559_page-0005-scaled-e1722706622104-1150x1536.jpg 1150w, https:\/\/vvv.quantpedia.com\/wp-content\/uploads\/2024\/08\/ssrn-4874559_page-0005-scaled-e1722706622104-768x1026.jpg 768w, https:\/\/vvv.quantpedia.com\/wp-content\/uploads\/2024\/08\/ssrn-4874559_page-0005-scaled-e1722706622104.jpg 1395w\" sizes=\"(max-width: 766px) 100vw, 766px\" \/><\/figure>\n\n\n\n<figure class=\"wp-block-image aligncenter\"><img decoding=\"async\" width=\"1024\" height=\"728\" src=\"https:\/\/\\\/\\\/new-fmhwbzh6ghd9hede.swedencentral-01.azurewebsites.net\/wp-content\/uploads\/2024\/08\/ssrn-4874559_page-0004-scaled-e1722706593505-1024x728.jpg\" alt=\"\" class=\"wp-image-35114\" srcset=\"https:\/\/vvv.quantpedia.com\/wp-content\/uploads\/2024\/08\/ssrn-4874559_page-0004-scaled-e1722706593505-1024x728.jpg 1024w, https:\/\/vvv.quantpedia.com\/wp-content\/uploads\/2024\/08\/ssrn-4874559_page-0004-scaled-e1722706593505-300x213.jpg 300w, https:\/\/vvv.quantpedia.com\/wp-content\/uploads\/2024\/08\/ssrn-4874559_page-0004-scaled-e1722706593505-1536x1093.jpg 1536w, https:\/\/vvv.quantpedia.com\/wp-content\/uploads\/2024\/08\/ssrn-4874559_page-0004-scaled-e1722706593505-768x546.jpg 768w, https:\/\/vvv.quantpedia.com\/wp-content\/uploads\/2024\/08\/ssrn-4874559_page-0004-scaled-e1722706593505.jpg 1774w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<figure class=\"wp-block-image aligncenter\"><img decoding=\"async\" width=\"1024\" height=\"228\" src=\"https:\/\/\\\/\\\/new-fmhwbzh6ghd9hede.swedencentral-01.azurewebsites.net\/wp-content\/uploads\/2024\/08\/ssrn-4874559_page-0006-scaled-e1722706657501-1024x228.jpg\" alt=\"\" class=\"wp-image-35116\" srcset=\"https:\/\/vvv.quantpedia.com\/wp-content\/uploads\/2024\/08\/ssrn-4874559_page-0006-scaled-e1722706657501-1024x228.jpg 1024w, https:\/\/vvv.quantpedia.com\/wp-content\/uploads\/2024\/08\/ssrn-4874559_page-0006-scaled-e1722706657501-300x67.jpg 300w, https:\/\/vvv.quantpedia.com\/wp-content\/uploads\/2024\/08\/ssrn-4874559_page-0006-scaled-e1722706657501-1536x342.jpg 1536w, https:\/\/vvv.quantpedia.com\/wp-content\/uploads\/2024\/08\/ssrn-4874559_page-0006-scaled-e1722706657501-768x171.jpg 768w, https:\/\/vvv.quantpedia.com\/wp-content\/uploads\/2024\/08\/ssrn-4874559_page-0006-scaled-e1722706657501.jpg 1668w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<figure class=\"wp-block-image size-large\"><\/figure>\n\n\n\n<figure class=\"wp-block-image size-large\"><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Notable quotations from the academic research paper:<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">\u201cHow should we decide whether this is an improvement, and big enough to warrant its adoption? First and foremost, does it make sense? Doing something that has a stronger logical foundation is usually worth it, and we think this adjustment passes that first test. This is particularly important to think about before looking at the empirical results, as we just don\u2019t have enough historical data to draw strong statistically-based conclusions.<sup>4<\/sup><br>Second, with the caveat that 140 years of data isn\u2019t that much when looking at 10-year stock market returns, we\u2019ll want to compare how each metric has done in forecasting future earnings and returns.<sup>5<\/sup> The table [on our first featured choice in section above] shows a few summary statistics, which are supportive of the hypothesis that our suggested P-CAE metric is more useful than the same metric without the payout adjustment.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Notice that the standard Shiller and Campbell metric underestimates future earnings by 13% and 15% in the two periods, which we\u2019d expect since that metric is not taking account of companies retaining earnings or repurchasing shares. Also, the shortfall is bigger in the more recent period, which is consistent with dividend payout ratios being lower over the second half of the 1890 &#8211; 2024 sample period. It\u2019s also supportive that P-CAEY explains more of the next ten years of real returns, and by a decent margin in both samples.<sup>6<\/sup> The chart [we featured first] shows P-CAEY and the next ten-year US real stock market return.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">We recognize that there are many who are critical of the use of CAEY as an estimator of future stock market real returns. We find most of these criticisms take the form of: <em>\u201cTwenty years ago, the CAEY of the US equity market was about 4.5% &#8211; but over the next 10 years, the actual was so much higher, coming in at 9.3% pa.\u201d<\/em><sup>8<\/sup> We don\u2019t think the modification we\u2019re suggesting in P-CAEY will go very far in changing the minds of such critics. We also think this isn\u2019t a particularly valid criticism, as CAEY being a useful estimator doesn\u2019t require that it explains all (or even most) long-term return variation. But, if you were attracted to the logic of Campbell and Shiller\u2019s CAPE to begin with, we think you\u2019ll find their measure adjusted for dividend payouts a worthwhile improvement.\u201d<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<p class=\"wp-block-paragraph\" id=\"block-854363cc-8450-4dc0-a06a-c737766e9431\"><strong>Are you looking for more strategies to read about? <a href=\"https:\/\/\\\/\\\/new-fmhwbzh6ghd9hede.swedencentral-01.azurewebsites.net\/sign-up-for-our-newsletter\/\">Sign up for our newsletter<\/a> or visit our <a href=\"https:\/\/\\\/\\\/new-fmhwbzh6ghd9hede.swedencentral-01.azurewebsites.net\/blog\/\">Blog<\/a> or <a href=\"http:\/\/\\\/\\\/new-fmhwbzh6ghd9hede.swedencentral-01.azurewebsites.net\/Screener\">Screener<\/a><\/strong>.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\" id=\"block-65925002-6290-4d3b-b5cd-f3a277851ec8\"><strong>Do you want to learn more about Quantpedia Premium service? 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His methodology for assessing a valuation of the U.S. equity market is undoubtedly the most cited and discussed. Therefore, it&#8217;s not surprising that there exists quite a lot of papers that try to refine and <a href=\"https:\/\/\\\/\\\/new-fmhwbzh6ghd9hede.swedencentral-01.azurewebsites.net\/the-cape-ratio-and-machine-learning\/\"><strong>expand the CAPE&#8217;s methodology<\/strong><\/a>. One such last attempt is the work of James White and Victor Haghani, whose research paper revolves around the use of a modified version of the Cyclically-Adjusted Price Earnings (CAPE) ratio, termed P-CAPE. Their methodology aims to improve the estimation of long-term expected real returns of the stock market by incorporating the dividend payout ratio into the traditional CAPE metric.<\/strong><\/p>","protected":false},"author":25210,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[47,162,51,59],"class_list":["post-35101","post","type-post","status-publish","format-standard","hentry","category-uncategorized","tag-asset-class-picking","tag-fundamental-analysis","tag-market-timing","tag-reversal"],"_links":{"self":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts\/35101","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/users\/25210"}],"replies":[{"embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/comments?post=35101"}],"version-history":[{"count":0,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts\/35101\/revisions"}],"wp:attachment":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/media?parent=35101"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/categories?post=35101"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/tags?post=35101"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}