{"id":14216,"date":"2022-08-25T13:17:26","date_gmt":"2022-08-25T17:17:26","guid":{"rendered":"https:\/\/jasonapollovoss.com\/web\/?p=14216"},"modified":"2025-09-05T14:02:59","modified_gmt":"2025-09-05T20:02:59","slug":"data-beats-the-sp-500-decision-rule-ii","status":"publish","type":"post","link":"https:\/\/jasonapollovoss.com\/web\/2022\/08\/25\/data-beats-the-sp-500-decision-rule-ii\/","title":{"rendered":"DATA Beats the S&#038;P 500 &#8211; Decision Rule II"},"content":{"rendered":"<p>[et_pb_section fb_built=&#8221;1&#8243; admin_label=&#8221;section&#8221; _builder_version=&#8221;4.16&#8243; global_colors_info=&#8221;{}&#8221; theme_builder_area=&#8221;post_content&#8221;][et_pb_row admin_label=&#8221;row&#8221; _builder_version=&#8221;4.16&#8243; background_size=&#8221;initial&#8221; background_position=&#8221;top_left&#8221; background_repeat=&#8221;repeat&#8221; global_colors_info=&#8221;{}&#8221; theme_builder_area=&#8221;post_content&#8221;][et_pb_column type=&#8221;4_4&#8243; _builder_version=&#8221;4.16&#8243; custom_padding=&#8221;|||&#8221; global_colors_info=&#8221;{}&#8221; custom_padding__hover=&#8221;|||&#8221; theme_builder_area=&#8221;post_content&#8221;][et_pb_text admin_label=&#8221;Text&#8221; _builder_version=&#8221;4.16&#8243; background_size=&#8221;initial&#8221; background_position=&#8221;top_left&#8221; background_repeat=&#8221;repeat&#8221; global_colors_info=&#8221;{}&#8221; theme_builder_area=&#8221;post_content&#8221;]<\/p>\n<figure class=\"x-el x-el-figure c2-1 c2-2 c2-3x c2-i c2-h c2-21 c2-2c c2-29 c2-2a c2-43 c2-51 c2-3 c2-4 c2-5 c2-6 c2-7 c2-8\">\n<div><\/div>\n<\/figure>\n<p><span style=\"font-size: 14px;\">Deception And Truth Analysis (D.A.T.A.), Inc. has spent almost a decade working with deception scientists<\/span><a class=\"x-el x-el-a c2-2w c2-2x c2-69 c2-v c2-w c2-x c2-j c2-6a c2-3 c2-30 c2-31 c2-11 c2-32\" href=\"https:\/\/blogging.godaddy.com\/blog\/a6d795a4-a672-4120-a6ba-07384a52a2d8\/posts\/17acec79-ae87-424b-b9bf-0f56d80b7c03#_ftn1\" rel=\"\" style=\"font-size: 14px;\">[1]<\/a><span style=\"font-size: 14px;\">and studying multiple methods of deception and truth detection. Based on this knowledge we developed Deception And Truth Analysis, a proprietary algorithm based on scientific research and making use of textual data to rapidly identify deceptiveness and truthfulness in business communications.<\/span><\/p>\n<p><span style=\"font-size: 14px;\"><\/span><\/p>\n<div>\n<h3 class=\"x-el x-el-h4 c2-6h c2-6i c2-v c2-w c2-42 c2-2c c2-2a c2-29 c2-2b c2-3 c2-z c2-44 c2-10 c2-45 c2-46 c2-47 c2-48\"><span style=\"font-family: futural;\"><strong class=\"x-el x-el-span c2-2w c2-2x c2-3 c2-65 c2-13 c2-3v c2-66\">Our Methodology<\/strong><\/span><\/h3>\n<\/div>\n<p><span style=\"font-family: futural;\">A practical demonstration of its power is our recent work where use of D.A.T.A.\u2019s algorithm was able to best the performance of the Standard &amp; Poor\u2019s 500 Index. Using the 500 components of the S&amp;P 500 we created a simple decision rule: whenever a new 10(k) is published score it for its level of deceptiveness and truthfulness and if its resulting D.A.T.A. Score is greater than 18% (i.e. approximately one standard deviation above the mean)*, purchase it. Hold for a year. More specifically the methodology is, as follows:<\/span><\/p>\n<ol>\n<li><span style=\"font-family: futural;\">Starting with 2008 and continuing through 2021 each of the 500 S&amp;P 500 components\u2019 10(k)s were assessed the date of public release by D.A.T.A. to create a D.A.T.A. Score for each firm. DATA Scores range between -100% and +100% with any deceptive score being negative and any truthful score being positive.<\/span><\/li>\n<li><span style=\"font-family: futural;\">Then, on the publication date of the 10(k) we purchase the company if its DATA Score is 18% or higher. Each of the purchased companies is equal weighted in a portfolio. The average lag from assessment by a D.A.T.A. Score to purchase is less than one day.<\/span><\/li>\n<li><span style=\"font-family: futural;\">The above decision rule is repeated each year.<\/span><\/li>\n<li><span style=\"font-family: futural;\">A separate analysis was done where instead of selling a security from the portfolio where DATA Scores were 18% or higher after holding for one year, they were instead held for 5 years. But the methodology was identical otherwise.<\/span><\/li>\n<\/ol>\n<div>\n<h4 class=\"x-el x-el-h4 c2-6h c2-6i c2-v c2-w c2-42 c2-2c c2-2a c2-29 c2-2b c2-3 c2-z c2-44 c2-10 c2-45 c2-46 c2-47 c2-48\"><span style=\"font-family: futural;\"><strong class=\"x-el x-el-span c2-2w c2-2x c2-3 c2-65 c2-13 c2-3v c2-66\"><\/strong><\/span><\/h4>\n<h3 class=\"x-el x-el-h4 c2-6h c2-6i c2-v c2-w c2-42 c2-2c c2-2a c2-29 c2-2b c2-3 c2-z c2-44 c2-10 c2-45 c2-46 c2-47 c2-48\"><span style=\"font-family: futural;\"><strong class=\"x-el x-el-span c2-2w c2-2x c2-3 c2-65 c2-13 c2-3v c2-66\">Results<\/strong><\/span><\/h3>\n<\/div>\n<p><span style=\"font-family: futural;\">Here are the results of our simple decision rule just described:<\/span><\/p>\n<ol>\n<li><span style=\"font-family: futural;\">Where the strategy is to purchase the firms whose DATA Scores are 18% or higher, equal weighting each:<\/span>\n<ol>\n<li><span style=\"font-family: futural;\">Annual average outperformance of 164 bps for a compounded total return advantage of 21,185 bps (2008-2021), with a standard deviation of 3.59%. This comparison is our equal weighted holdings vs. equal weight S&amp;P 500. Note: results against the actual S&amp;P 500 are quoted below; and they are much higher.<\/span><\/li>\n<li><span style=\"font-family: futural;\">Outperformance in 10 of 14 years (2008-2021).<\/span><\/li>\n<li><span style=\"font-family: futural;\">Worst performance is -5.21% in 2021.<\/span><\/li>\n<li><span style=\"font-family: futural;\">Best performance is +7.13% in 2012.<\/span><\/li>\n<\/ol>\n<\/li>\n<li><span style=\"font-family: futural;\">Where the strategy is to purchase the firms whose DATA Scores are 18% or higher, equal weighting each at the start of each year and\u00a0<u class=\"x-el x-el-span c2-2w c2-2x c2-3 c2-65 c2-13 c2-31 c2-66 c2-69\">hold for 5 years<\/u>:<\/span>\n<ol>\n<li><span style=\"font-family: futural;\">Average rolling 5-year outperformance of 19.95%, with a standard deviation of 12.10%.<\/span><\/li>\n<li><span style=\"font-family: futural;\">Outperformance in 10 of the 10 five-year rolling periods.<\/span><\/li>\n<li><span style=\"font-family: futural;\">Worst performance is +2.74% for the five-year period 2017-2021.<\/span><\/li>\n<li><span style=\"font-family: futural;\">Best performance is +46.72% for the five-year period 2008-2012.<\/span><\/li>\n<\/ol>\n<\/li>\n<li><span style=\"font-family: futural;\">If a direct comparison is made of our strategy against the actual S&amp;P 500 returns (i.e. not equal-weighted S&amp;P 500), the results are, as follows:<\/span>\n<ol>\n<li><span style=\"font-family: futural;\">Average annual outperformance of 939 bps for a compound total return advantage of 97,416 bps (2008-2021), with a standard deviation of 33.13%.<\/span><\/li>\n<li><span style=\"font-family: futural;\">Outperformance in 9 of 14 years (2008-2021).<\/span><\/li>\n<li><span style=\"font-family: futural;\">Worst performance is -4,165 bps in 2021.<\/span><\/li>\n<li><span style=\"font-family: futural;\">Best performance is +10,614 bps in 2008.<\/span><\/li>\n<li><span style=\"font-family: futural;\">&lt;Aside: equal-weight is a badass strategy.&gt;<\/span><\/li>\n<\/ol>\n<\/li>\n<li><span style=\"font-family: futural;\">The results reported in #1 our summary above are first pass results and are not optimized in any way. An example of optimized results would be to relax the minimum threshold DATA Score of an arbitrary 18% and to instead find the threshold that maximizes the return advantage of the decision rule. If we did that the results would be to only purchase firms whose DATA Scores were 12% or higher.<\/span>\n<ol>\n<li><span style=\"font-family: futural;\">Annual average outperformance of 212 bps (2008-2021) vs. 164 bps for the simple decision rule of only buying companies whose DATA Scores exceeded 18% whose results are shared in #1, above. Purchasing those companies whose DATA Scores are 12% or higher results in a compounded total return advantage of 33,577 bps vs. 21,185 bps for the simple decision rule (2008-2021), with a standard deviation of 1.38%.<\/span><\/li>\n<li><span style=\"font-family: futural;\">Outperformance occurs in 13 of 14 years (2008-2021).<\/span><\/li>\n<li><span style=\"font-family: futural;\">Worst performance is -25 bps in 2015.<\/span><\/li>\n<li><span style=\"font-family: futural;\">Best performance is +500 bps in 2017.<\/span><\/li>\n<\/ol>\n<\/li>\n<li><span style=\"font-family: futural;\">The number of companies in the S&amp;P 500 each year that meet the decision rule criteria of having a DATA Score of 18% or greater fluctuates. Thus, it is important to know the number of companies that meet that criteria each year so that investors may be assured of being able to execute such a strategy. Here are the number of companies that meet the criteria each year (2008-2021):<\/span><\/li>\n<\/ol>\n<figure class=\"x-el x-el-figure c2-1 c2-2 c2-3x c2-i c2-h c2-21 c2-2c c2-29 c2-2a c2-43 c2-51 c2-3 c2-4 c2-5 c2-6 c2-7 c2-8\">\n<div>\n<div><span style=\"font-family: futural;\"><img decoding=\"async\" class=\"x-el x-el-img c2-1 c2-2 c2-k c2-21 c2-1x c2-1y c2-29 c2-2b c2-s c2-6b c2-4l c2-3 c2-4 c2-5 c2-6 c2-7 c2-8\" title=\"Assessment Accuracy - Decision Rule II\" src=\"https:\/\/img1.wsimg.com\/isteam\/ip\/b4167b12-c211-4a45-9c4b-489be14138f8\/DATA%20Beats%20the%20S%26P%20500%20-%20Decision%20Rule%20II%20-%20qu.png\/:\/rs=w:1280\" alt=\"Assessment Accuracy - Decision Rule II\" \/><\/span><\/div>\n<\/div><figcaption class=\"x-el x-el-figcaption c2-1 c2-2 c2-v c2-w c2-3d c2-29 c2-2b c2-4f c2-6c c2-6d c2-6e c2-6f c2-3 c2-6g c2-3e c2-10 c2-3f c2-3g c2-3h c2-3i\"><span style=\"font-family: futural;\">Assessment Accuracy &#8211; Decision Rule II<\/span><\/figcaption><\/figure>\n<p><span style=\"font-family: futural;\"><\/span><\/p>\n<p><span style=\"font-family: futural;\">Outperformance of such magnitude consistently occurring for multiple time-periods, in some of the most liquid and covered stocks in the world is noteworthy. Additionally, the DATA Scores utilized were for the entire 10(k) and not just the more extemporaneously authored MD&amp;A section.<\/span><\/p>\n<p><span style=\"font-family: futural;\"><\/span><\/p>\n<p><span style=\"font-family: futural;\">Note:<\/span><\/p>\n<ul>\n<li><span style=\"font-family: futural;\">No other analyses were applied, just a run of each S&amp;P 500 components\u2019 10(k)s through D.A.T.A.\u2019s algorithm.<\/span><\/li>\n<li><span style=\"font-family: futural;\">The D.A.T.A. algorithm was developed out-of-sample and based on how people pan culturally deceive using language, and not developed in-sample based on how businesses deceive through language.<\/span><\/li>\n<li><span style=\"font-family: futural;\">To avoid overfit, the main reported results are not optimized.<\/span><\/li>\n<li><span style=\"font-family: futural;\">There is a considerable advantage to equal-weighting, but the scores we report are \u201capples to apples,\u201d or equal-weight S&amp;P 500 compared to equal-weight S&amp;P 500.<\/span><\/li>\n<\/ul>\n<p><span style=\"font-family: futural;\"><strong class=\"x-el x-el-span c2-2w c2-2x c2-3 c2-65 c2-13 c2-3v c2-66\"><\/strong><\/span><\/p>\n<h3><span style=\"font-family: futural;\"><strong class=\"x-el x-el-span c2-2w c2-2x c2-3 c2-65 c2-13 c2-3v c2-66\">We Would Love to Hear from You<\/strong><\/span><\/h3>\n<p><span style=\"font-family: futural;\">We would of course love to hear from you so that we may demonstrate Deception And Truth Analysis (D.A.T.A.) technology for you and demonstrate the many practical uses beyond alpha-generation for you and your research team.<\/span><\/p>\n<p><span style=\"font-family: futural;\">\u00a0\u00a0\u00a0\u00a0<\/span><\/p>\n<p><span style=\"font-family: futural;\"><a class=\"x-el x-el-a c2-2w c2-2x c2-69 c2-v c2-w c2-x c2-j c2-6a c2-3 c2-30 c2-31 c2-11 c2-32\" href=\"https:\/\/blogging.godaddy.com\/blog\/a6d795a4-a672-4120-a6ba-07384a52a2d8\/posts\/17acec79-ae87-424b-b9bf-0f56d80b7c03#_ftnref1\" rel=\"\">[1]<\/a>See Jason Apollo Voss CFA\u2019s\u00a0<em class=\"x-el x-el-span c2-2w c2-2x c2-3 c2-65 c2-13 c2-31 c2-66 c2-67\">Lie Detection Guide: Theory and Practice for Investment Professionals<\/em>, for example.<\/span><\/p>\n<p><span style=\"font-family: futural;\">* Why was 18% chosen? Our mean D.A.T.A. Score is 4.9% and the standard deviation of the distribution of D.A.T.A. Scores is 13.2%, or roughly 18%. Note: from above, in case you missed it, if we were to optimize (and we hate optimized reported results) then our decision rule threshold would be 12%.<\/span><\/p>\n<p>[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Deception And Truth Analysis (D.A.T.A.), Inc. has spent almost a decade working with deception scientists[1]and studying multiple methods of deception and truth detection. Based on this knowledge we developed Deception And Truth Analysis, a proprietary algorithm based on scientific research and making use of textual data to rapidly identify deceptiveness and truthfulness in business communications. [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":14217,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"<figure class=\"x-el x-el-figure c2-1 c2-2 c2-3x c2-i c2-h c2-21 c2-2c c2-29 c2-2a c2-43 c2-51 c2-3 c2-4 c2-5 c2-6 c2-7 c2-8\">\r\n<div>\r\n<div><span style=\"font-family: futural;\"><img class=\"x-el x-el-img c2-1 c2-2 c2-k c2-21 c2-1x c2-1y c2-29 c2-2b c2-s c2-6b c2-4l c2-3 c2-4 c2-5 c2-6 c2-7 c2-8\" title=\"D.A.T.A. Beats the S&amp;P 500 - Decision Rule II\" src=\"https:\/\/img1.wsimg.com\/isteam\/ip\/b4167b12-c211-4a45-9c4b-489be14138f8\/DATA%20Beats%20the%20S%26P%20500%20-%20Decision%20Rule%20II.png\/:\/cr=t:0%25,l:0%25,w:100%25,h:100%25\/rs=w:1280\" alt=\"D.A.T.A. Beats the S&amp;P 500 - Decision Rule II\" \/><\/span><\/div>\r\n<\/div>\r\n<figcaption class=\"x-el x-el-figcaption c2-1 c2-2 c2-v c2-w c2-3d c2-29 c2-2b c2-4f c2-6c c2-6d c2-6e c2-6f c2-3 c2-6g c2-3e c2-10 c2-3f c2-3g c2-3h c2-3i\"><span style=\"font-family: futural;\">D.A.T.A. Beats the S&amp;P 500 - Decision Rule II<\/span><\/figcaption><\/figure>\r\n<span style=\"font-family: futural;\"><strong class=\"x-el x-el-span c2-2w c2-2x c2-3 c2-65 c2-13 c2-3v c2-66\">By Jason Apollo Voss, CFA<\/strong><\/span>\r\n\r\n<span style=\"font-family: futural;\">Deception And Truth Analysis (D.A.T.A.), Inc. has spent almost a decade working with deception scientists<a class=\"x-el x-el-a c2-2w c2-2x c2-69 c2-v c2-w c2-x c2-j c2-6a c2-3 c2-30 c2-31 c2-11 c2-32\" href=\"https:\/\/blogging.godaddy.com\/blog\/a6d795a4-a672-4120-a6ba-07384a52a2d8\/posts\/17acec79-ae87-424b-b9bf-0f56d80b7c03#_ftn1\" rel=\"\">[1]<\/a>and studying multiple methods of deception and truth detection. Based on this knowledge we developed Deception And Truth Analysis, a proprietary algorithm based on scientific research and making use of textual data to rapidly identify deceptiveness and truthfulness in business communications.<\/span>\r\n<div>\r\n<h4 class=\"x-el x-el-h4 c2-6h c2-6i c2-v c2-w c2-42 c2-2c c2-2a c2-29 c2-2b c2-3 c2-z c2-44 c2-10 c2-45 c2-46 c2-47 c2-48\"><span style=\"font-family: futural;\"><strong class=\"x-el x-el-span c2-2w c2-2x c2-3 c2-65 c2-13 c2-3v c2-66\">Our Methodology<\/strong><\/span><\/h4>\r\n<\/div>\r\n<span style=\"font-family: futural;\">A practical demonstration of its power is our recent work where use of D.A.T.A.\u2019s algorithm was able to best the performance of the Standard &amp; Poor\u2019s 500 Index. Using the 500 components of the S&amp;P 500 we created a simple decision rule: whenever a new 10(k) is published score it for its level of deceptiveness and truthfulness and if its resulting D.A.T.A. Score is greater than 18% (i.e. approximately one standard deviation above the mean)*, purchase it. Hold for a year. More specifically the methodology is, as follows:<\/span>\r\n<ol>\r\n \t<li><span style=\"font-family: futural;\">Starting with 2008 and continuing through 2021 each of the 500 S&amp;P 500 components\u2019 10(k)s were assessed the date of public release by D.A.T.A. to create a D.A.T.A. Score for each firm. DATA Scores range between -100% and +100% with any deceptive score being negative and any truthful score being positive.<\/span><\/li>\r\n \t<li><span style=\"font-family: futural;\">Then, on the publication date of the 10(k) we purchase the company if its DATA Score is 18% or higher. Each of the purchased companies is equal weighted in a portfolio. The average lag from assessment by a D.A.T.A. Score to purchase is less than one day.<\/span><\/li>\r\n \t<li><span style=\"font-family: futural;\">The above decision rule is repeated each year.<\/span><\/li>\r\n \t<li><span style=\"font-family: futural;\">A separate analysis was done where instead of selling a security from the portfolio where DATA Scores were 18% or higher after holding for one year, they were instead held for 5 years. But the methodology was identical otherwise.<\/span><\/li>\r\n<\/ol>\r\n<div>\r\n<h4 class=\"x-el x-el-h4 c2-6h c2-6i c2-v c2-w c2-42 c2-2c c2-2a c2-29 c2-2b c2-3 c2-z c2-44 c2-10 c2-45 c2-46 c2-47 c2-48\"><span style=\"font-family: futural;\"><strong class=\"x-el x-el-span c2-2w c2-2x c2-3 c2-65 c2-13 c2-3v c2-66\">Results<\/strong><\/span><\/h4>\r\n<\/div>\r\n<span style=\"font-family: futural;\">Here are the results of our simple decision rule just described:<\/span>\r\n<ol>\r\n \t<li><span style=\"font-family: futural;\">Where the strategy is to purchase the firms whose DATA Scores are 18% or higher, equal weighting each:<\/span>\r\n<ol>\r\n \t<li><span style=\"font-family: futural;\">Annual average outperformance of 164 bps for a compounded total return advantage of 21,185 bps (2008-2021), with a standard deviation of 3.59%. This comparison is our equal weighted holdings vs. equal weight S&amp;P 500. Note: results against the actual S&amp;P 500 are quoted below; and they are much higher.<\/span><\/li>\r\n \t<li><span style=\"font-family: futural;\">Outperformance in 10 of 14 years (2008-2021).<\/span><\/li>\r\n \t<li><span style=\"font-family: futural;\">Worst performance is -5.21% in 2021.<\/span><\/li>\r\n \t<li><span style=\"font-family: futural;\">Best performance is +7.13% in 2012.<\/span><\/li>\r\n<\/ol>\r\n<\/li>\r\n \t<li><span style=\"font-family: futural;\">Where the strategy is to purchase the firms whose DATA Scores are 18% or higher, equal weighting each at the start of each year and\u00a0<u class=\"x-el x-el-span c2-2w c2-2x c2-3 c2-65 c2-13 c2-31 c2-66 c2-69\">hold for 5 years<\/u>:<\/span>\r\n<ol>\r\n \t<li><span style=\"font-family: futural;\">Average rolling 5-year outperformance of 19.95%, with a standard deviation of 12.10%.<\/span><\/li>\r\n \t<li><span style=\"font-family: futural;\">Outperformance in 10 of the 10 five-year rolling periods.<\/span><\/li>\r\n \t<li><span style=\"font-family: futural;\">Worst performance is +2.74% for the five-year period 2017-2021.<\/span><\/li>\r\n \t<li><span style=\"font-family: futural;\">Best performance is +46.72% for the five-year period 2008-2012.<\/span><\/li>\r\n<\/ol>\r\n<\/li>\r\n \t<li><span style=\"font-family: futural;\">If a direct comparison is made of our strategy against the actual S&amp;P 500 returns (i.e. not equal-weighted S&amp;P 500), the results are, as follows:<\/span>\r\n<ol>\r\n \t<li><span style=\"font-family: futural;\">Average annual outperformance of 939 bps for a compound total return advantage of 97,416 bps (2008-2021), with a standard deviation of 33.13%.<\/span><\/li>\r\n \t<li><span style=\"font-family: futural;\">Outperformance in 9 of 14 years (2008-2021).<\/span><\/li>\r\n \t<li><span style=\"font-family: futural;\">Worst performance is -4,165 bps in 2021.<\/span><\/li>\r\n \t<li><span style=\"font-family: futural;\">Best performance is +10,614 bps in 2008.<\/span><\/li>\r\n \t<li><span style=\"font-family: futural;\">&lt;Aside: equal-weight is a badass strategy.&gt;<\/span><\/li>\r\n<\/ol>\r\n<\/li>\r\n \t<li><span style=\"font-family: futural;\">The results reported in #1 our summary above are first pass results and are not optimized in any way. An example of optimized results would be to relax the minimum threshold DATA Score of an arbitrary 18% and to instead find the threshold that maximizes the return advantage of the decision rule. If we did that the results would be to only purchase firms whose DATA Scores were 12% or higher.<\/span>\r\n<ol>\r\n \t<li><span style=\"font-family: futural;\">Annual average outperformance of 212 bps (2008-2021) vs. 164 bps for the simple decision rule of only buying companies whose DATA Scores exceeded 18% whose results are shared in #1, above. Purchasing those companies whose DATA Scores are 12% or higher results in a compounded total return advantage of 33,577 bps vs. 21,185 bps for the simple decision rule (2008-2021), with a standard deviation of 1.38%.<\/span><\/li>\r\n \t<li><span style=\"font-family: futural;\">Outperformance occurs in 13 of 14 years (2008-2021).<\/span><\/li>\r\n \t<li><span style=\"font-family: futural;\">Worst performance is -25 bps in 2015.<\/span><\/li>\r\n \t<li><span style=\"font-family: futural;\">Best performance is +500 bps in 2017.<\/span><\/li>\r\n<\/ol>\r\n<\/li>\r\n \t<li><span style=\"font-family: futural;\">The number of companies in the S&amp;P 500 each year that meet the decision rule criteria of having a DATA Score of 18% or greater fluctuates. Thus, it is important to know the number of companies that meet that criteria each year so that investors may be assured of being able to execute such a strategy. Here are the number of companies that meet the criteria each year (2008-2021):<\/span><\/li>\r\n<\/ol>\r\n<figure class=\"x-el x-el-figure c2-1 c2-2 c2-3x c2-i c2-h c2-21 c2-2c c2-29 c2-2a c2-43 c2-51 c2-3 c2-4 c2-5 c2-6 c2-7 c2-8\">\r\n<div>\r\n<div><span style=\"font-family: futural;\"><img class=\"x-el x-el-img c2-1 c2-2 c2-k c2-21 c2-1x c2-1y c2-29 c2-2b c2-s c2-6b c2-4l c2-3 c2-4 c2-5 c2-6 c2-7 c2-8\" title=\"Assessment Accuracy - Decision Rule II\" src=\"https:\/\/img1.wsimg.com\/isteam\/ip\/b4167b12-c211-4a45-9c4b-489be14138f8\/DATA%20Beats%20the%20S%26P%20500%20-%20Decision%20Rule%20II%20-%20qu.png\/:\/rs=w:1280\" alt=\"Assessment Accuracy - Decision Rule II\" \/><\/span><\/div>\r\n<\/div>\r\n<figcaption class=\"x-el x-el-figcaption c2-1 c2-2 c2-v c2-w c2-3d c2-29 c2-2b c2-4f c2-6c c2-6d c2-6e c2-6f c2-3 c2-6g c2-3e c2-10 c2-3f c2-3g c2-3h c2-3i\"><span style=\"font-family: futural;\">Assessment Accuracy - Decision Rule II<\/span><\/figcaption><\/figure>\r\n<span style=\"font-family: futural;\">Outperformance of such magnitude consistently occurring for multiple time-periods, in some of the most liquid and covered stocks in the world is noteworthy. Additionally, the DATA Scores utilized were for the entire 10(k) and not just the more extemporaneously authored MD&amp;A section.<\/span>\r\n\r\n<span style=\"font-family: futural;\">Note:<\/span>\r\n<ul>\r\n \t<li><span style=\"font-family: futural;\">No other analyses were applied, just a run of each S&amp;P 500 components\u2019 10(k)s through D.A.T.A.\u2019s algorithm.<\/span><\/li>\r\n \t<li><span style=\"font-family: futural;\">The D.A.T.A. algorithm was developed out-of-sample and based on how people pan culturally deceive using language, and not developed in-sample based on how businesses deceive through language.<\/span><\/li>\r\n \t<li><span style=\"font-family: futural;\">To avoid overfit, the main reported results are not optimized.<\/span><\/li>\r\n \t<li><span style=\"font-family: futural;\">There is a considerable advantage to equal-weighting, but the scores we report are \u201capples to apples,\u201d or equal-weight S&amp;P 500 compared to equal-weight S&amp;P 500.<\/span><\/li>\r\n<\/ul>\r\n<span style=\"font-family: futural;\"><strong class=\"x-el x-el-span c2-2w c2-2x c2-3 c2-65 c2-13 c2-3v c2-66\">We Would Love to Hear from You<\/strong><\/span>\r\n\r\n<span style=\"font-family: futural;\">We would of course love to hear from you so that we may demonstrate Deception And Truth Analysis (D.A.T.A.) technology for you and demonstrate the many practical uses beyond alpha-generation for you and your research team.<\/span>\r\n\r\n<span style=\"font-family: futural;\">\u00a0\u00a0\u00a0\u00a0<\/span>\r\n\r\n<span style=\"font-family: futural;\"><a class=\"x-el x-el-a c2-2w c2-2x c2-69 c2-v c2-w c2-x c2-j c2-6a c2-3 c2-30 c2-31 c2-11 c2-32\" href=\"https:\/\/blogging.godaddy.com\/blog\/a6d795a4-a672-4120-a6ba-07384a52a2d8\/posts\/17acec79-ae87-424b-b9bf-0f56d80b7c03#_ftnref1\" rel=\"\">[1]<\/a>See Jason Apollo Voss CFA\u2019s\u00a0<em class=\"x-el x-el-span c2-2w c2-2x c2-3 c2-65 c2-13 c2-31 c2-66 c2-67\">Lie Detection Guide: Theory and Practice for Investment Professionals<\/em>, for example.<\/span>\r\n\r\n<span style=\"font-family: futural;\">* Why was 18% chosen? Our mean D.A.T.A. Score is 4.9% and the standard deviation of the distribution of D.A.T.A. Scores is 13.2%, or roughly 18%. Note: from above, in case you missed it, if we were to optimize (and we hate optimized reported results) then our decision rule threshold would be 12%.<\/span>","_et_gb_content_width":"","footnotes":""},"categories":[3,465],"tags":[133,441],"class_list":["post-14216","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-the-blog","category-d-a-t-a","tag-sp-500","tag-validation"],"_links":{"self":[{"href":"https:\/\/jasonapollovoss.com\/web\/wp-json\/wp\/v2\/posts\/14216","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/jasonapollovoss.com\/web\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/jasonapollovoss.com\/web\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/jasonapollovoss.com\/web\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/jasonapollovoss.com\/web\/wp-json\/wp\/v2\/comments?post=14216"}],"version-history":[{"count":0,"href":"https:\/\/jasonapollovoss.com\/web\/wp-json\/wp\/v2\/posts\/14216\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/jasonapollovoss.com\/web\/wp-json\/wp\/v2\/media\/14217"}],"wp:attachment":[{"href":"https:\/\/jasonapollovoss.com\/web\/wp-json\/wp\/v2\/media?parent=14216"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/jasonapollovoss.com\/web\/wp-json\/wp\/v2\/categories?post=14216"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/jasonapollovoss.com\/web\/wp-json\/wp\/v2\/tags?post=14216"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}