{"id":4357,"date":"2017-03-16T21:04:20","date_gmt":"2017-03-17T01:04:20","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-digit\/submission\/vetr-crowdsourcing-stock-ratings\/"},"modified":"2017-03-16T21:04:20","modified_gmt":"2017-03-17T01:04:20","slug":"vetr-crowdsourcing-stock-ratings","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-digit\/submission\/vetr-crowdsourcing-stock-ratings\/","title":{"rendered":"Vetr: Crowdsourcing Stock Ratings"},"content":{"rendered":"<figure id=\"attachment_4353\" aria-describedby=\"caption-attachment-4353\" style=\"width: 619px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/d3.harvard.edu\/platform-digit\/wp-content\/uploads\/sites\/2\/2017\/03\/price_targets.png\"><img loading=\"lazy\" decoding=\"async\" class=\" wp-image-4353\" src=\"https:\/\/d3.harvard.edu\/platform-digit\/wp-content\/uploads\/sites\/2\/2017\/03\/price_targets.png\" alt=\"price targets\" width=\"619\" height=\"173\" \/><\/a><figcaption id=\"caption-attachment-4353\" class=\"wp-caption-text\"><em>This looks familiar&#8230;<\/em><\/figcaption><\/figure>\n<p>We\u2019ve all seen headlines like this. For retail investors who don\u2019t have access to management teams or sophisticated tools, ratings from professional analysts are often the best source of information as to whether a stock looks like a good bet. But how reliable are those reports, and whose interests do analysts have in mind when publishing ratings? Vetr hopes to offer a better solution by crowdsourcing stock ratings and educating retail investors along the way.<\/p>\n<p>Equity research is famously complicated. Analysts with big name banks spend hundreds of hours poring over company financials, conversing with industry participants, and developing projections to produce what is essentially a ternary output: buy, hold, or sell. Research firms pay top dollar to recruit\/retain analysts and sink millions into complex tools and databases (sell-side firms alone spent $15B on research efforts in 2016). Nevertheless, it\u2019s not entirely clear whether that level of sophistication produces differential predictive power. Studies have shown top performing analysts <a href=\"https:\/\/www.scholars.northwestern.edu\/en\/publications\/do-security-analysts-exhibit-persistent-differences-in-stock-pick\">demonstrate persistence<\/a> in making profitable stock recommendations, but <a href=\"http:\/\/www8.gsb.columbia.edu\/rtfiles\/accounting\/BRADSHAW.pdf\">when it comes to target prices<\/a>, only 24% to 45% of analysts\u2019 forecasts are met. A retail investor deploying a trading strategy based on top analysts\u2019 recommendations <a href=\"https:\/\/www.scholars.northwestern.edu\/en\/publications\/do-security-analysts-exhibit-persistent-differences-in-stock-pick\">isn\u2019t likely to make money<\/a>. Moreover, the conflicts of interest that beset the sell-side research community are <a href=\"https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=2228373\">well documented<\/a>.<\/p>\n<p>Enter Vetr. The company\u2019s <a href=\"http:\/\/blog.vetr.com\/\">stated mission<\/a> is \u201cto connect the world\u2019s investors to make them more financially effective and successful.\u201d Its key piece of technology is a predictive platform that leverages the insights of a multitude of users to generate ratings for individual stocks. Here\u2019s how it works:<\/p>\n<ul>\n<li>Users make predictions about the future price of a stock<\/li>\n<li>Vetr aggregates user predictions then filters them through an algorithm to produce a target price and rating (based on five stars)<\/li>\n<li>Users\u2019 forecasts are tracked over time, and top performers are rewarded with recognition on the platform<\/li>\n<\/ul>\n<figure id=\"attachment_4354\" aria-describedby=\"caption-attachment-4354\" style=\"width: 608px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/d3.harvard.edu\/platform-digit\/wp-content\/uploads\/sites\/2\/2017\/03\/vetr_nvidia.png\"><img loading=\"lazy\" decoding=\"async\" class=\" wp-image-4354\" src=\"https:\/\/d3.harvard.edu\/platform-digit\/wp-content\/uploads\/sites\/2\/2017\/03\/vetr_nvidia.png\" alt=\"Vetr Nvidia\" width=\"608\" height=\"418\" \/><\/a><figcaption id=\"caption-attachment-4354\" class=\"wp-caption-text\"><em>Vetr&#8217;s position on Nvidia (accessed 3\/15\/2017)<\/em><\/figcaption><\/figure>\n<p>Vetr\u2019s model has some advantages over professional research. It is 100% free, is unencumbered by conflicts of interest, and offers users an opportunity to educate themselves on investment analysis. Furthermore, Vetr\u2019s crowdsourced price predictions <em>could<\/em> be more accurate than those of Wall Street analysts. <a href=\"http:\/\/blog.vetr.com\/wp-content\/uploads\/2016\/09\/MIT_Prediction_Markets.pdf\">According to the firm\u2019s CEO<\/a>, \u201cour research demonstrates that Vetr\u2019s aggregated predictions are more informative about future stock prices than any individual Wall Street analyst or the target price consensus of industry professionals only.\u201d<\/p>\n<p>Incentivizing widespread participation is difficult given the nature of stock picking. Quality stock analysts may not be willing to share their picks without compensation (e.g., in the form of cash prizes for performing well or some sort of subscription revenue sharing). This is highly problematic, as the idea that crowdsourced predictions can be more accurate than those of professionals depends largely on achieving a critical mass of quality predictors. The social recognition and portfolio building aspects of Vetr just may not be sufficient to attract enough users.<\/p>\n<figure id=\"attachment_4355\" aria-describedby=\"caption-attachment-4355\" style=\"width: 282px\" class=\"wp-caption alignright\"><a href=\"https:\/\/d3.harvard.edu\/platform-digit\/wp-content\/uploads\/sites\/2\/2017\/03\/top_vetrs.png\"><img loading=\"lazy\" decoding=\"async\" class=\" wp-image-4355\" src=\"https:\/\/d3.harvard.edu\/platform-digit\/wp-content\/uploads\/sites\/2\/2017\/03\/top_vetrs.png\" alt=\"top Vetrs\" width=\"282\" height=\"262\" \/><\/a><figcaption id=\"caption-attachment-4355\" class=\"wp-caption-text\"><em>Vetr&#8217;s top contributors and their one month returns<\/em><\/figcaption><\/figure>\n<p>Sell-side analysts typically capture value in the form of one time fees, subscriptions, or \u201csoft money\u201d (i.e., providing research in exchange for brokerage business). Vetr\u2019s aggregate predictions, if proven to be differentially accurate, could be extremely valuable, but capturing that value without destroying the community\u2019s trust or the platform\u2019s transparency would be challenging. Instead, the company is exploring four potential <a href=\"http:\/\/wmtoday.com\/2015\/07\/19\/can-the-power-of-the-crowd-disrupt-financial-advice\/\">sources of revenue<\/a>, including shifting to freemium and partnering with brokers to share fees on trades made by users.<\/p>\n<p>Vetr may indeed have value as an education tool, but as a quality source of information for investors looking to build portfolios, it has some serious disadvantages. Most, if not all, of Vetr\u2019s users lack access to management teams. <a href=\"https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=2228373)\">For sell-side analysts<\/a>, \u201cprivate communication with management is a more useful input\u201d than is primary research. In <em>The Wisdom of Crowds<\/em>, James Surowiecki recounts Francis Galton\u2019s famous county fair anecdote: the average of individual guesses as to the weight of an ox was surprisingly accurate. In the case of equity analysis, Vetr\u2019s crowd is competing against a handful of individuals with access to a scale.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Can crowds do better than sophisticated analysts when it comes to predicting future stock prices?<\/p>\n","protected":false},"author":404,"featured_media":4358,"comment_status":"open","ping_status":"closed","template":"","categories":[664,673,161,1278,1279,1013],"class_list":["post-4357","hck-submission","type-hck-submission","status-publish","has-post-thumbnail","hentry","category-crowds","category-crowdsourcing","category-investing","category-investments","category-stocks","category-wall-street"],"connected_submission_link":"https:\/\/d3.harvard.edu\/platform-digit\/assignment\/managing-digital-crowds\/","yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.3 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Vetr: Crowdsourcing Stock Ratings - Digital Innovation and Transformation<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/d3.harvard.edu\/platform-digit\/submission\/vetr-crowdsourcing-stock-ratings\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Vetr: Crowdsourcing Stock Ratings - 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