{"id":14303,"date":"2023-06-13T14:53:23","date_gmt":"2023-06-13T18:53:23","guid":{"rendered":"https:\/\/jasonapollovoss.com\/web\/?p=14303"},"modified":"2025-09-05T15:48:37","modified_gmt":"2025-09-05T21:48:37","slug":"use-case-4-the-buy-sell-decision","status":"publish","type":"post","link":"https:\/\/jasonapollovoss.com\/web\/2023\/06\/13\/use-case-4-the-buy-sell-decision\/","title":{"rendered":"Use Case 4: The Buy-Sell Decision"},"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-family: futural;\">This series of Deception And Truth Analysis (D.A.T.A.) articles has covered actionable investment insights use cases for the outputs of our DATA algorithm. In this piece we cover \u201cthe buy-sell decision.\u201d Admittedly this article is a bit more speculative than our other articles. Here\u2019s why:<\/span><\/p>\n<ul>\n<li><span style=\"font-family: futural;\">We consider ourselves a screening tool and not a diagnostic. That is, we seek to assist investors in their investment process by rapidly surfacing actionable investment insights, rather than making decisions for D.A.T.A.\u2019s Clients. We are your partners; we are not you.<\/span><\/li>\n<li><span style=\"font-family: futural;\">In the portfolio management asset allocation decision, our suggested use cases are entirely speculative. That\u2019s because to our knowledge no one is currently using D.A.T.A.\u2019s metrics and algorithm to construct\/asset allocate their portfolios. Nonetheless, we think you will see that our proposals are compelling.<\/span><\/li>\n<\/ul>\n<p><span style=\"font-family: futural;\">As a review, there are five major due-diligence steps in almost every investment process. They are:<\/span><\/p>\n<ol>\n<li><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:\/\/deceptionandtruthanalysis.com\/insights\/f\/use-case-1-establishing-your-investible-universe\" rel=\"\">Establishing your investible universe.<\/a><\/span><\/li>\n<li><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:\/\/deceptionandtruthanalysis.com\/insights\/f\/use-case-2-identifying-state-changes\" rel=\"\">Relying on high-level criteria to identify state changes.<\/a><\/span><\/li>\n<li><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:\/\/deceptionandtruthanalysis.com\/insights\/f\/use-case-3-deeper-dive-securities-analysis\" rel=\"\">Deeper dive analyses that establish an investment thesis.<\/a><\/span><\/li>\n<li><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\">The buy-sell decision.<\/strong><\/span><\/li>\n<li><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:\/\/deceptionandtruthanalysis.com\/insights\/f\/use-case-5-ongoing-due-diligence\" target=\"_blank\" rel=\"noopener\">Ongoing due-diligence of your investments.<\/a><\/span><\/li>\n<\/ol>\n<p><span style=\"font-family: futural;\">Deception And Truth Analysis (D.A.T.A.) provides a powerful assist in each of the above steps. Now let\u2019s discuss the buy-sell decision\u2026we think you will be pleasantly surprised.<\/span><\/p>\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\">The Buy-Sell Decision<\/strong><\/span><\/h3>\n<\/div>\n<p><span style=\"font-family: futural;\">Deception And Truth Analysis provides a number of outputs on its platform. That said, our primary output is our DATA Score. It ranges between -100% and +100%, with any negative score indicative of a document\u2019s level of deceptiveness and any positive score indicative of a document\u2019s level of truthfulness. Our back tests have demonstrated that\u00a0<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:\/\/deceptionandtruthanalysis.com\/insights?blogcategory=Validation\" rel=\"\">DATA Scores are predictive of future securities price returns<\/a>. Below we discuss seven possible asset allocation\/portfolio management ideas making use of DATA Scores.<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\/294d67d9-882d-4b14-a777-ce8f2fa2243c#_edn1\" rel=\"\">[i]<\/a><\/span><\/p>\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\">Asset Allocation\/Portfolio Management Strategies<\/strong><\/span><\/h3>\n<\/div>\n<p><span style=\"font-family: futural;\">Below we discuss seven possible asset allocation\/portfolio management strategies that can be implemented where the S&amp;P 500 is the investment performance benchmark. For each strategy we use:<\/span><\/p>\n<ol>\n<li><span style=\"font-family: futural;\">DATA Score assessments of each of the S&amp;P 500 components\u2019 10-Ks issued in 2021.<\/span><\/li>\n<li><span style=\"font-family: futural;\">Then the asset allocation\/portfolio management strategy is implemented on the first trading day of the following year, that is: 2 January 2022.<\/span><\/li>\n<li><span style=\"font-family: futural;\">We then look at the performance of the asset allocation\/portfolio management strategy on the last trading day of the year, 31 December 2022.<\/span><\/li>\n<\/ol>\n<p><span style=\"font-family: futural;\">\u00a0\u00a0\u00a0\u00a0\u00a0Note: This article is not meant as a validation of our algorithm, just a demonstration that our outputs may be used in constructing\/asset allocating for portfolios.<\/span><\/p>\n<p><span style=\"font-family: futural;\">Clearly, above, there is a lag in the implementation of the strategy from a document being assessed by us and its DATA Score then being used to implement an asset allocation\/portfolio management strategy many months later. The average lag from a 10-K being assessed with a DATA Score and then the purchase of the security is 9.17 months, or 279 days before implementation. The longest lag is 11.60 months and the shortest lag is 0.56 months.<\/span><\/p>\n<p><span style=\"font-family: futural;\">As you can see below that there remains signal in securities prices and as predicted by DATA Scores with such long lags is surprising. Yet, this is a result that we consistently find with DATA Scores. Our theory is that numbers are outcomes driven by managements\u2019 choices. In turn, managements\u2019 choices are driven by their behaviors. DATA Scores measure deceptive behavior and thus, we believe they are predictive. Or, at least that is our theory for why we consistently see the results we do in our tests.<\/span><\/p>\n<p><span style=\"font-family: futural;\">In 2022, the performance of the S&amp;P 500 was -19.6445%.<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\/294d67d9-882d-4b14-a777-ce8f2fa2243c#_edn2\" rel=\"\">[ii]<\/a>Each of the strategies below is compared with this performance.<\/span><\/p>\n<p><span style=\"font-family: futural;\"><\/span><\/p>\n<h4><span style=\"font-family: futural;\"><u class=\"x-el x-el-span c2-2w c2-2x c2-3 c2-65 c2-13 c2-31 c2-66 c2-69\">Strategy 1: Long-Short, Weights Determined by Median DATA Score<\/u><\/span><\/h4>\n<p><span style=\"font-family: futural;\">For asset allocation \/ portfolio management Strategy 1 we propose that DATA Scores may be used to establish the weights within a portfolio. In this instance we:<\/span><\/p>\n<ol>\n<li><span style=\"font-family: futural;\">Calculate the median DATA Score for the S&amp;P 500\u2019s component companies\u2019 10-Ks issued in 2021, which is 12.17%. This calculation is in cell G508.<\/span><\/li>\n<li><span style=\"font-family: futural;\">Next, in Column H we calculate a figure that is each company\u2019s DATA Score divided by the median DATA Score of 12.17%. This creates a series of weights to include in the portfolio.<\/span><\/li>\n<li><span style=\"font-family: futural;\">The average weight for S&amp;P 500 companies would be 100% divided by 495 holdings (some of the companies have dual share classes) = an average weight of 0.2020% for each holding if it were equal weighted. This calculation is in cell I503.<\/span><\/li>\n<li><span style=\"font-family: futural;\">In column I we then multiply the average weight of a holding of 0.2020% by the overweight calculated in Column H. This tells us the weighting for each company to include in the portfolio. Note: in some instances, because the company\u2019s DATA Score is below the median score it has the effect of creating a short position for the security.<\/span><\/li>\n<li><span style=\"font-family: futural;\">In Column Y the stock price for each company on the first trading day of the following year, 2 January 2022 is shown. In Column Z the stock price for each company on the last trading day of the following year, 31 December 2022. In Column AA, the change in stock price year over year is shown.<\/span><\/li>\n<\/ol>\n<p><span style=\"font-family: futural;\">The performance of Strategy 1 for 2022, described above is: -15.3878% vs. S&amp;P 500 performance of -19.6445%. Thus, the return advantage of Strategy 1 is:<\/span><\/p>\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\">+4.2566% or +425.66 bps<\/strong>.<\/span><\/p>\n<p><span style=\"font-family: futural;\"><u class=\"x-el x-el-span c2-2w c2-2x c2-3 c2-65 c2-13 c2-31 c2-66 c2-69\"><\/u><\/span><\/p>\n<h4><span style=\"font-family: futural;\"><u class=\"x-el x-el-span c2-2w c2-2x c2-3 c2-65 c2-13 c2-31 c2-66 c2-69\">Strategy 2: Long-Short, Weights Determined by DATA Scores<\/u><\/span><\/h4>\n<p><span style=\"font-family: futural;\">For asset allocation \/ portfolio management Strategy 2 we propose that DATA Scores may be used to establish the weights within a portfolio. In this instance we:<\/span><\/p>\n<ol>\n<li><span style=\"font-family: futural;\">Calculate the total of all DATA Scores for the S&amp;P 500\u2019s component companies\u2019 10-Ks issued in 2021, which is 59.0674. This calculation is in cell G502.<\/span><\/li>\n<li><span style=\"font-family: futural;\">Next, in Column J we calculate a figure that is each company\u2019s DATA Score divided by the total of all DATA Scores of 59.0674 to establish the portfolio weights for the strategy. Note: in some instances, because the company\u2019s DATA Score is negative, meaning in the aggregate its 10-K is assessed as deceptive it has the effect of creating a short position for the security.<\/span><\/li>\n<li><span style=\"font-family: futural;\">In Column Y the stock price for each company on the first trading day of the following year, 2 January 2022 is shown. In Column Z the stock price for each company on the last trading day of the following year, 31 December 2022. In Column AA, the change in stock price year over year is shown.<\/span><\/li>\n<\/ol>\n<p><span style=\"font-family: futural;\">The performance of Strategy 2 for 2022, described above is: -15.6937% vs. S&amp;P 500 performance of -19.6445%. Thus, the return advantage of Strategy 1 is:<\/span><\/p>\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\">+3.9507% or +395.07 bps<\/strong>.<\/span><\/p>\n<p><span style=\"font-family: futural;\"><u class=\"x-el x-el-span c2-2w c2-2x c2-3 c2-65 c2-13 c2-31 c2-66 c2-69\"><\/u><\/span><\/p>\n<h4><span style=\"font-family: futural;\"><u class=\"x-el x-el-span c2-2w c2-2x c2-3 c2-65 c2-13 c2-31 c2-66 c2-69\">Strategy 3: Long Only, Weights Determined by Median DATA Score<\/u><\/span><\/h4>\n<p><span style=\"font-family: futural;\">For Strategy 3 we employ exactly the same asset allocation \/ portfolio management strategy as in Strategy 1, except that we only look at the performance of the long companies; that is, the companies whose DATA Scores were assessed as Truthful (i.e. DATA Scores &gt;= 0%) in the aggregate.<\/span><\/p>\n<p><span style=\"font-family: futural;\">The performance of Strategy 3 for 2022, described above is: -15.3872% vs. S&amp;P 500 performance of -19.6445%. Thus, the return advantage of Strategy 1 is:<\/span><\/p>\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\">+4.2572% or +425.72 bps<\/strong>.<\/span><\/p>\n<p><span style=\"font-family: futural;\"><u class=\"x-el x-el-span c2-2w c2-2x c2-3 c2-65 c2-13 c2-31 c2-66 c2-69\"><\/u><\/span><\/p>\n<h4><span style=\"font-family: futural;\"><u class=\"x-el x-el-span c2-2w c2-2x c2-3 c2-65 c2-13 c2-31 c2-66 c2-69\">Strategy 4: Long Only, Weights Determined by DATA Scores<\/u><\/span><\/h4>\n<p><span style=\"font-family: futural;\">For Strategy 4 we employ exactly the same asset allocation \/ portfolio management strategy as in Strategy 2, except that we only look at the performance of the long companies; that is, the companies whose DATA Scores were assessed as Truthful (i.e. DATA Scores &gt;= 0%) in the aggregate.<\/span><\/p>\n<p><span style=\"font-family: futural;\">The performance of Strategy 4 for 2022, described above is: -15.6931% vs. S&amp;P 500 performance of -19.6445%. Thus, the return advantage of Strategy 1 is:<\/span><\/p>\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\">+3.9514% or +395.14 bps<\/strong>.<\/span><\/p>\n<p><span style=\"font-family: futural;\"><u class=\"x-el x-el-span c2-2w c2-2x c2-3 c2-65 c2-13 c2-31 c2-66 c2-69\"><\/u><\/span><\/p>\n<h4><span style=\"font-family: futural;\"><u class=\"x-el x-el-span c2-2w c2-2x c2-3 c2-65 c2-13 c2-31 c2-66 c2-69\">Strategy 5: Long Only Truthful Companies, Equal Weight<\/u><\/span><\/h4>\n<p><span style=\"font-family: futural;\">For Strategy 5 we shift gears entirely by taking away the asset allocation \/ portfolio management decision entirely and simply equal weight all of the companies whose DATA Scores are assessed as truthful (i.e. DATA Scores &gt;= 0%). This strategy features 441 truthful 10-Ks from companies and excludes 54 deceptive 10-Ks from companies. Thus the portfolio weights for each holding are 1 divided by 441, or 0.2268%.<\/span><\/p>\n<p><span style=\"font-family: futural;\">The performance of Strategy 5 for 2022, described above is: -12.4322% vs. S&amp;P 500 performance of -19.6445%. Thus, the return advantage of Strategy 1 is:<\/span><\/p>\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\">+7.2122% or +721.22 bps<\/strong>.<\/span><\/p>\n<p><span style=\"font-family: futural;\"><u class=\"x-el x-el-span c2-2w c2-2x c2-3 c2-65 c2-13 c2-31 c2-66 c2-69\"><\/u><\/span><\/p>\n<h4><span style=\"font-family: futural;\"><u class=\"x-el x-el-span c2-2w c2-2x c2-3 c2-65 c2-13 c2-31 c2-66 c2-69\">Strategy 6: Long Only Companies with DATA Scores &gt; 1 Standard Deviation Above the Mean, Equal Weight<\/u><\/span><\/h4>\n<p><span style=\"font-family: futural;\">For Strategy 6 we indulge an assumption. Namely, that the companies issuing the most truthful documents probably outperform the companies issuing less truthful documents. Thus, we build a portfolio that only includes companies whose documents score 1 standard deviation above the mean. The mean DATA Score was 11.93% and the standard deviation was 9.78%. Thus, the threshold DATA Score for inclusion is 21.71%, as shown in cell N506.<\/span><\/p>\n<p><span style=\"font-family: futural;\">Again, we take away the asset allocation \/ portfolio management decision entirely and simply equal weight all of the companies whose DATA Scores are assessed as truthful. This strategy features 67 10-Ks from companies and excludes the remainder of the 495 companies. Thus the portfolio weights for each holding are 1 divided by 67, or 1.49%.<\/span><\/p>\n<p><span style=\"font-family: futural;\">The performance of Strategy 6 for 2022, described above is: -19.1358% vs. S&amp;P 500 performance of -19.6445%. Thus, the return advantage of Strategy 1 is:<\/span><\/p>\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\">+0.5086% or +50.86 bps<\/strong>.<\/span><\/p>\n<p><span style=\"font-family: futural;\"><u class=\"x-el x-el-span c2-2w c2-2x c2-3 c2-65 c2-13 c2-31 c2-66 c2-69\"><\/u><\/span><\/p>\n<h4><span style=\"font-family: futural;\"><u class=\"x-el x-el-span c2-2w c2-2x c2-3 c2-65 c2-13 c2-31 c2-66 c2-69\">Strategy 7: Use DATA Score Year on Year Change Rank, Equal Weight<\/u><\/span><\/h4>\n<p><span style=\"font-family: futural;\">Our very last example, Strategy 7, does not directly use our primary output, the DATA Score. Instead, we create a portfolio of the securities that have the greatest year on year change in DATA Scores. Our thought here is that companies whose performance is improving likely want to tell the world about this performance. No need to be bashful if you are kickin\u2019 it performance-wise.<\/span><\/p>\n<p><span style=\"font-family: futural;\">Once again, we equal weight the companies. But here there is a bit of trickiness. Namely, what constitutes the \u201cgreatest improvement\u201d in DATA Scores year on year? Because this decision is arbitrary we show a number of possibilities below. To calculate the weighting for each security, simply divide 1 by the number of companies included in the inclusive category of: greatest year on year change.<\/span><\/p>\n<p><span style=\"font-family: futural;\">For the following\u00a0<em class=\"x-el x-el-span c2-2w c2-2x c2-3 c2-65 c2-13 c2-31 c2-66 c2-67\">x<\/em>\u00a0companies to include here is the performance vs. the S&amp;P 500:<\/span><\/p>\n<ul>\n<li><span style=\"font-family: futural;\">40 companies: -18.3863% vs. -19.6445%,\u00a0<strong class=\"x-el x-el-span c2-2w c2-2x c2-3 c2-65 c2-13 c2-3v c2-66\">+1.2582% or +125.82 bps<\/strong><\/span><\/li>\n<li><span style=\"font-family: futural;\">50 companies: -17.4071% vs. -19.6445%,\u00a0<strong class=\"x-el x-el-span c2-2w c2-2x c2-3 c2-65 c2-13 c2-3v c2-66\">+2.2374% or +223.74 bps<\/strong><\/span><\/li>\n<li><span style=\"font-family: futural;\">75 companies: -15.7633% vs. -19.6445%,\u00a0<strong class=\"x-el x-el-span c2-2w c2-2x c2-3 c2-65 c2-13 c2-3v c2-66\">+3.8812% or +388.12 bps<\/strong><\/span><\/li>\n<li><span style=\"font-family: futural;\">100 companies: -13.6297% vs. -19.6445%,\u00a0<strong class=\"x-el x-el-span c2-2w c2-2x c2-3 c2-65 c2-13 c2-3v c2-66\">+4.2566% or +425.66 bps<\/strong><\/span><\/li>\n<\/ul>\n<p><span style=\"font-family: futural;\">\u00a0\u00a0\u00a0\u00a0\u00a0Note: you can download our spreadsheet and toggle the figure yourself by changing the number of companies to include in cell G526.<\/span><\/p>\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\">Conclusion<\/strong><\/span><\/h3>\n<\/div>\n<p><span style=\"font-family: futural;\">We have demonstrated in this article the ways in which outputs from Deception And Truth Analysis may be used to help investors engaged in \u201cthe buy-sell decision.\u201d Specifically, by proposing ways that our scores may be used in the asset allocation \/ portfolio management decision. While the above work is far from complete, and is certainly not robust from a validation perspective, we believe the above results are intriguing.<\/span><\/p>\n<p><span style=\"font-family: futural;\">We would love to hear about your adventures in asset allocation \/ portfolio management using our DATA Scores and other outputs. Let\u2019s have fun out there!<\/span><\/p>\n<p><span style=\"font-family: futural;\">__________\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\/294d67d9-882d-4b14-a777-ce8f2fa2243c#_ednref1\" rel=\"\">[i]<\/a>Each of these strategies is shown in the following spreadsheet: \u00a0<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:\/\/netorgft8403294-my.sharepoint.com\/:x:\/g\/personal\/jvoss_deceptionandtruthanalysis_com\/ETtUa7TUEIJGjyMF6WQN4HkBJhz1ywnazp1FfK89r4JCWA?e=aOsncK\" rel=\"\">Deception And Truth Analysis &#8211; Use Case 4 &#8211; The Buy-Sell Decision.xlsx<\/a>\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\/294d67d9-882d-4b14-a777-ce8f2fa2243c#_ednref2\" rel=\"\">[ii]<\/a>Comes from Yahoo! Finance download of S&amp;P 500 open on 2 January 2022 of 4,778.14 and the adjusted close on 31 December 2022 of 3,839.50.<\/span><\/p>\n<p>[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>\n","protected":false},"excerpt":{"rendered":"<p>This series of Deception And Truth Analysis (D.A.T.A.) articles has covered actionable investment insights use cases for the outputs of our DATA algorithm. In this piece we cover \u201cthe buy-sell decision.\u201d Admittedly this article is a bit more speculative than our other articles. Here\u2019s why: We consider ourselves a screening tool and not a diagnostic. [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":14304,"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=\"Use Case 4: The Buy-Sell Decision\" src=\"https:\/\/img1.wsimg.com\/isteam\/ip\/b4167b12-c211-4a45-9c4b-489be14138f8\/Eggs%20in%20One%20Basket%2002.jpg\/:\/rs=w:1280\" alt=\"Use Case 4: The Buy-Sell Decision\" \/><\/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;\">Use Case 4: The Buy-Sell Decision<\/span><\/figcaption><\/figure>\r\n<span style=\"font-family: futural;\"><em>By Jason A. Voss, CFA<\/em><\/span>\r\n\r\n<span style=\"font-family: futural;\">This series of Deception And Truth Analysis (D.A.T.A.) articles has covered actionable investment insights use cases for the outputs of our DATA algorithm. In this piece we cover \u201cthe buy-sell decision.\u201d Admittedly this article is a bit more speculative than our other articles. Here\u2019s why:<\/span>\r\n<ul>\r\n \t<li><span style=\"font-family: futural;\">We consider ourselves a screening tool and not a diagnostic. That is, we seek to assist investors in their investment process by rapidly surfacing actionable investment insights, rather than making decisions for D.A.T.A.\u2019s Clients. We are your partners; we are not you.<\/span><\/li>\r\n \t<li><span style=\"font-family: futural;\">In the portfolio management asset allocation decision, our suggested use cases are entirely speculative. That\u2019s because to our knowledge no one is currently using D.A.T.A.\u2019s metrics and algorithm to construct\/asset allocate their portfolios. Nonetheless, we think you will see that our proposals are compelling.<\/span><\/li>\r\n<\/ul>\r\n<span style=\"font-family: futural;\">As a review, there are five major due-diligence steps in almost every investment process. They are:<\/span>\r\n<ol>\r\n \t<li><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:\/\/deceptionandtruthanalysis.com\/insights\/f\/use-case-1-establishing-your-investible-universe\" rel=\"\">Establishing your investible universe.<\/a><\/span><\/li>\r\n \t<li><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:\/\/deceptionandtruthanalysis.com\/insights\/f\/use-case-2-identifying-state-changes\" rel=\"\">Relying on high-level criteria to identify state changes.<\/a><\/span><\/li>\r\n \t<li><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:\/\/deceptionandtruthanalysis.com\/insights\/f\/use-case-3-deeper-dive-securities-analysis\" rel=\"\">Deeper dive analyses that establish an investment thesis.<\/a><\/span><\/li>\r\n \t<li><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\">The buy-sell decision.<\/strong><\/span><\/li>\r\n \t<li><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:\/\/deceptionandtruthanalysis.com\/insights\/f\/use-case-5-ongoing-due-diligence\" target=\"_blank\" rel=\"noopener\">Ongoing due-diligence of your investments.<\/a><\/span><\/li>\r\n<\/ol>\r\n<span style=\"font-family: futural;\">Deception And Truth Analysis (D.A.T.A.) provides a powerful assist in each of the above steps. Now let\u2019s discuss the buy-sell decision\u2026we think you will be pleasantly surprised.<\/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\">The Buy-Sell Decision<\/strong><\/span><\/h4>\r\n<\/div>\r\n<span style=\"font-family: futural;\">Deception And Truth Analysis provides a number of outputs on its platform. That said, our primary output is our DATA Score. It ranges between -100% and +100%, with any negative score indicative of a document\u2019s level of deceptiveness and any positive score indicative of a document\u2019s level of truthfulness. Our back tests have demonstrated that\u00a0<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:\/\/deceptionandtruthanalysis.com\/insights?blogcategory=Validation\" rel=\"\">DATA Scores are predictive of future securities price returns<\/a>. Below we discuss seven possible asset allocation\/portfolio management ideas making use of DATA Scores.<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\/294d67d9-882d-4b14-a777-ce8f2fa2243c#_edn1\" rel=\"\">[i]<\/a><\/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\">Asset Allocation\/Portfolio Management Strategies<\/strong><\/span><\/h4>\r\n<\/div>\r\n<span style=\"font-family: futural;\">Below we discuss seven possible asset allocation\/portfolio management strategies that can be implemented where the S&amp;P 500 is the investment performance benchmark. For each strategy we use:<\/span>\r\n<ol>\r\n \t<li><span style=\"font-family: futural;\">DATA Score assessments of each of the S&amp;P 500 components\u2019 10-Ks issued in 2021.<\/span><\/li>\r\n \t<li><span style=\"font-family: futural;\">Then the asset allocation\/portfolio management strategy is implemented on the first trading day of the following year, that is: 2 January 2022.<\/span><\/li>\r\n \t<li><span style=\"font-family: futural;\">We then look at the performance of the asset allocation\/portfolio management strategy on the last trading day of the year, 31 December 2022.<\/span><\/li>\r\n<\/ol>\r\n<span style=\"font-family: futural;\">\u00a0\u00a0\u00a0\u00a0\u00a0Note: This article is not meant as a validation of our algorithm, just a demonstration that our outputs may be used in constructing\/asset allocating for portfolios.<\/span>\r\n\r\n<span style=\"font-family: futural;\">Clearly, above, there is a lag in the implementation of the strategy from a document being assessed by us and its DATA Score then being used to implement an asset allocation\/portfolio management strategy many months later. The average lag from a 10-K being assessed with a DATA Score and then the purchase of the security is 9.17 months, or 279 days before implementation. The longest lag is 11.60 months and the shortest lag is 0.56 months.<\/span>\r\n\r\n<span style=\"font-family: futural;\">As you can see below that there remains signal in securities prices and as predicted by DATA Scores with such long lags is surprising. Yet, this is a result that we consistently find with DATA Scores. Our theory is that numbers are outcomes driven by managements\u2019 choices. In turn, managements\u2019 choices are driven by their behaviors. DATA Scores measure deceptive behavior and thus, we believe they are predictive. Or, at least that is our theory for why we consistently see the results we do in our tests.<\/span>\r\n\r\n<span style=\"font-family: futural;\">In 2022, the performance of the S&amp;P 500 was -19.6445%.<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\/294d67d9-882d-4b14-a777-ce8f2fa2243c#_edn2\" rel=\"\">[ii]<\/a>Each of the strategies below is compared with this performance.<\/span>\r\n\r\n<span style=\"font-family: futural;\"><u class=\"x-el x-el-span c2-2w c2-2x c2-3 c2-65 c2-13 c2-31 c2-66 c2-69\">Strategy 1: Long-Short, Weights Determined by Median DATA Score<\/u><\/span>\r\n\r\n<span style=\"font-family: futural;\">For asset allocation \/ portfolio management Strategy 1 we propose that DATA Scores may be used to establish the weights within a portfolio. In this instance we:<\/span>\r\n<ol>\r\n \t<li><span style=\"font-family: futural;\">Calculate the median DATA Score for the S&amp;P 500\u2019s component companies\u2019 10-Ks issued in 2021, which is 12.17%. This calculation is in cell G508.<\/span><\/li>\r\n \t<li><span style=\"font-family: futural;\">Next, in Column H we calculate a figure that is each company\u2019s DATA Score divided by the median DATA Score of 12.17%. This creates a series of weights to include in the portfolio.<\/span><\/li>\r\n \t<li><span style=\"font-family: futural;\">The average weight for S&amp;P 500 companies would be 100% divided by 495 holdings (some of the companies have dual share classes) = an average weight of 0.2020% for each holding if it were equal weighted. This calculation is in cell I503.<\/span><\/li>\r\n \t<li><span style=\"font-family: futural;\">In column I we then multiply the average weight of a holding of 0.2020% by the overweight calculated in Column H. This tells us the weighting for each company to include in the portfolio. Note: in some instances, because the company\u2019s DATA Score is below the median score it has the effect of creating a short position for the security.<\/span><\/li>\r\n \t<li><span style=\"font-family: futural;\">In Column Y the stock price for each company on the first trading day of the following year, 2 January 2022 is shown. In Column Z the stock price for each company on the last trading day of the following year, 31 December 2022. In Column AA, the change in stock price year over year is shown.<\/span><\/li>\r\n<\/ol>\r\n<span style=\"font-family: futural;\">The performance of Strategy 1 for 2022, described above is: -15.3878% vs. S&amp;P 500 performance of -19.6445%. Thus, the return advantage of Strategy 1 is:<\/span>\r\n\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\">+4.2566% or +425.66 bps<\/strong>.<\/span>\r\n\r\n<span style=\"font-family: futural;\"><u class=\"x-el x-el-span c2-2w c2-2x c2-3 c2-65 c2-13 c2-31 c2-66 c2-69\">Strategy 2: Long-Short, Weights Determined by DATA Scores<\/u><\/span>\r\n\r\n<span style=\"font-family: futural;\">For asset allocation \/ portfolio management Strategy 2 we propose that DATA Scores may be used to establish the weights within a portfolio. In this instance we:<\/span>\r\n<ol>\r\n \t<li><span style=\"font-family: futural;\">Calculate the total of all DATA Scores for the S&amp;P 500\u2019s component companies\u2019 10-Ks issued in 2021, which is 59.0674. This calculation is in cell G502.<\/span><\/li>\r\n \t<li><span style=\"font-family: futural;\">Next, in Column J we calculate a figure that is each company\u2019s DATA Score divided by the total of all DATA Scores of 59.0674 to establish the portfolio weights for the strategy. Note: in some instances, because the company\u2019s DATA Score is negative, meaning in the aggregate its 10-K is assessed as deceptive it has the effect of creating a short position for the security.<\/span><\/li>\r\n \t<li><span style=\"font-family: futural;\">In Column Y the stock price for each company on the first trading day of the following year, 2 January 2022 is shown. In Column Z the stock price for each company on the last trading day of the following year, 31 December 2022. In Column AA, the change in stock price year over year is shown.<\/span><\/li>\r\n<\/ol>\r\n<span style=\"font-family: futural;\">The performance of Strategy 2 for 2022, described above is: -15.6937% vs. S&amp;P 500 performance of -19.6445%. Thus, the return advantage of Strategy 1 is:<\/span>\r\n\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\">+3.9507% or +395.07 bps<\/strong>.<\/span>\r\n\r\n<span style=\"font-family: futural;\"><u class=\"x-el x-el-span c2-2w c2-2x c2-3 c2-65 c2-13 c2-31 c2-66 c2-69\">Strategy 3: Long Only, Weights Determined by Median DATA Score<\/u><\/span>\r\n\r\n<span style=\"font-family: futural;\">For Strategy 3 we employ exactly the same asset allocation \/ portfolio management strategy as in Strategy 1, except that we only look at the performance of the long companies; that is, the companies whose DATA Scores were assessed as Truthful (i.e. DATA Scores &gt;= 0%) in the aggregate.<\/span>\r\n\r\n<span style=\"font-family: futural;\">The performance of Strategy 3 for 2022, described above is: -15.3872% vs. S&amp;P 500 performance of -19.6445%. Thus, the return advantage of Strategy 1 is:<\/span>\r\n\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\">+4.2572% or +425.72 bps<\/strong>.<\/span>\r\n\r\n<span style=\"font-family: futural;\"><u class=\"x-el x-el-span c2-2w c2-2x c2-3 c2-65 c2-13 c2-31 c2-66 c2-69\">Strategy 4: Long Only, Weights Determined by DATA Scores<\/u><\/span>\r\n\r\n<span style=\"font-family: futural;\">For Strategy 4 we employ exactly the same asset allocation \/ portfolio management strategy as in Strategy 2, except that we only look at the performance of the long companies; that is, the companies whose DATA Scores were assessed as Truthful (i.e. DATA Scores &gt;= 0%) in the aggregate.<\/span>\r\n\r\n<span style=\"font-family: futural;\">The performance of Strategy 4 for 2022, described above is: -15.6931% vs. S&amp;P 500 performance of -19.6445%. Thus, the return advantage of Strategy 1 is:<\/span>\r\n\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\">+3.9514% or +395.14 bps<\/strong>.<\/span>\r\n\r\n<span style=\"font-family: futural;\"><u class=\"x-el x-el-span c2-2w c2-2x c2-3 c2-65 c2-13 c2-31 c2-66 c2-69\">Strategy 5: Long Only Truthful Companies, Equal Weight<\/u><\/span>\r\n\r\n<span style=\"font-family: futural;\">For Strategy 5 we shift gears entirely by taking away the asset allocation \/ portfolio management decision entirely and simply equal weight all of the companies whose DATA Scores are assessed as truthful (i.e. DATA Scores &gt;= 0%). This strategy features 441 truthful 10-Ks from companies and excludes 54 deceptive 10-Ks from companies. Thus the portfolio weights for each holding are 1 divided by 441, or 0.2268%.<\/span>\r\n\r\n<span style=\"font-family: futural;\">The performance of Strategy 5 for 2022, described above is: -12.4322% vs. S&amp;P 500 performance of -19.6445%. Thus, the return advantage of Strategy 1 is:<\/span>\r\n\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\">+7.2122% or +721.22 bps<\/strong>.<\/span>\r\n\r\n<span style=\"font-family: futural;\"><u class=\"x-el x-el-span c2-2w c2-2x c2-3 c2-65 c2-13 c2-31 c2-66 c2-69\">Strategy 6: Long Only Companies with DATA Scores &gt; 1 Standard Deviation Above the Mean, Equal Weight<\/u><\/span>\r\n\r\n<span style=\"font-family: futural;\">For Strategy 6 we indulge an assumption. Namely, that the companies issuing the most truthful documents probably outperform the companies issuing less truthful documents. Thus, we build a portfolio that only includes companies whose documents score 1 standard deviation above the mean. The mean DATA Score was 11.93% and the standard deviation was 9.78%. Thus, the threshold DATA Score for inclusion is 21.71%, as shown in cell N506.<\/span>\r\n\r\n<span style=\"font-family: futural;\">Again, we take away the asset allocation \/ portfolio management decision entirely and simply equal weight all of the companies whose DATA Scores are assessed as truthful. This strategy features 67 10-Ks from companies and excludes the remainder of the 495 companies. Thus the portfolio weights for each holding are 1 divided by 67, or 1.49%.<\/span>\r\n\r\n<span style=\"font-family: futural;\">The performance of Strategy 6 for 2022, described above is: -19.1358% vs. S&amp;P 500 performance of -19.6445%. Thus, the return advantage of Strategy 1 is:<\/span>\r\n\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\">+0.5086% or +50.86 bps<\/strong>.<\/span>\r\n\r\n<span style=\"font-family: futural;\"><u class=\"x-el x-el-span c2-2w c2-2x c2-3 c2-65 c2-13 c2-31 c2-66 c2-69\">Strategy 7: Use DATA Score Year on Year Change Rank, Equal Weight<\/u><\/span>\r\n\r\n<span style=\"font-family: futural;\">Our very last example, Strategy 7, does not directly use our primary output, the DATA Score. Instead, we create a portfolio of the securities that have the greatest year on year change in DATA Scores. Our thought here is that companies whose performance is improving likely want to tell the world about this performance. No need to be bashful if you are kickin\u2019 it performance-wise.<\/span>\r\n\r\n<span style=\"font-family: futural;\">Once again, we equal weight the companies. But here there is a bit of trickiness. Namely, what constitutes the \u201cgreatest improvement\u201d in DATA Scores year on year? Because this decision is arbitrary we show a number of possibilities below. To calculate the weighting for each security, simply divide 1 by the number of companies included in the inclusive category of: greatest year on year change.<\/span>\r\n\r\n<span style=\"font-family: futural;\">For the following\u00a0<em class=\"x-el x-el-span c2-2w c2-2x c2-3 c2-65 c2-13 c2-31 c2-66 c2-67\">x<\/em>\u00a0companies to include here is the performance vs. the S&amp;P 500:<\/span>\r\n<ul>\r\n \t<li><span style=\"font-family: futural;\">40 companies: -18.3863% vs. -19.6445%,\u00a0<strong class=\"x-el x-el-span c2-2w c2-2x c2-3 c2-65 c2-13 c2-3v c2-66\">+1.2582% or +125.82 bps<\/strong><\/span><\/li>\r\n \t<li><span style=\"font-family: futural;\">50 companies: -17.4071% vs. -19.6445%,\u00a0<strong class=\"x-el x-el-span c2-2w c2-2x c2-3 c2-65 c2-13 c2-3v c2-66\">+2.2374% or +223.74 bps<\/strong><\/span><\/li>\r\n \t<li><span style=\"font-family: futural;\">75 companies: -15.7633% vs. -19.6445%,\u00a0<strong class=\"x-el x-el-span c2-2w c2-2x c2-3 c2-65 c2-13 c2-3v c2-66\">+3.8812% or +388.12 bps<\/strong><\/span><\/li>\r\n \t<li><span style=\"font-family: futural;\">100 companies: -13.6297% vs. -19.6445%,\u00a0<strong class=\"x-el x-el-span c2-2w c2-2x c2-3 c2-65 c2-13 c2-3v c2-66\">+4.2566% or +425.66 bps<\/strong><\/span><\/li>\r\n<\/ul>\r\n<span style=\"font-family: futural;\">\u00a0\u00a0\u00a0\u00a0\u00a0Note: you can download our spreadsheet and toggle the figure yourself by changing the number of companies to include in cell G526.<\/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\">Conclusion<\/strong><\/span><\/h4>\r\n<\/div>\r\n<span style=\"font-family: futural;\">We have demonstrated in this article the ways in which outputs from Deception And Truth Analysis may be used to help investors engaged in \u201cthe buy-sell decision.\u201d Specifically, by proposing ways that our scores may be used in the asset allocation \/ portfolio management decision. While the above work is far from complete, and is certainly not robust from a validation perspective, we believe the above results are intriguing.<\/span>\r\n\r\n<span style=\"font-family: futural;\">We would love to hear about your adventures in asset allocation \/ portfolio management using our DATA Scores and other outputs. Let\u2019s have fun out there!<\/span>\r\n\r\n<span style=\"font-family: futural;\">__________\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\/294d67d9-882d-4b14-a777-ce8f2fa2243c#_ednref1\" rel=\"\">[i]<\/a>Each of these strategies is shown in the following spreadsheet: \u00a0<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:\/\/netorgft8403294-my.sharepoint.com\/:x:\/g\/personal\/jvoss_deceptionandtruthanalysis_com\/ETtUa7TUEIJGjyMF6WQN4HkBJhz1ywnazp1FfK89r4JCWA?e=aOsncK\" rel=\"\">Deception And Truth Analysis - Use Case 4 - The Buy-Sell Decision.xlsx<\/a>\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\/294d67d9-882d-4b14-a777-ce8f2fa2243c#_ednref2\" rel=\"\">[ii]<\/a>Comes from Yahoo! Finance download of S&amp;P 500 open on 2 January 2022 of 4,778.14 and the adjusted close on 31 December 2022 of 3,839.50.<\/span>","_et_gb_content_width":"","footnotes":""},"categories":[3,465],"tags":[463],"class_list":["post-14303","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-the-blog","category-d-a-t-a","tag-use-cases"],"_links":{"self":[{"href":"https:\/\/jasonapollovoss.com\/web\/wp-json\/wp\/v2\/posts\/14303","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=14303"}],"version-history":[{"count":0,"href":"https:\/\/jasonapollovoss.com\/web\/wp-json\/wp\/v2\/posts\/14303\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/jasonapollovoss.com\/web\/wp-json\/wp\/v2\/media\/14304"}],"wp:attachment":[{"href":"https:\/\/jasonapollovoss.com\/web\/wp-json\/wp\/v2\/media?parent=14303"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/jasonapollovoss.com\/web\/wp-json\/wp\/v2\/categories?post=14303"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/jasonapollovoss.com\/web\/wp-json\/wp\/v2\/tags?post=14303"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}