1.0UNI FRhttps://www.uni.lu/frUNI FRhttps://www.uni.lu/frrich600338<blockquote class="wp-embedded-content" data-secret="ZRywCXtFr1"><a href="https://www.uni.lu/fr/events/identifying-price-infomativeness/">Identifying Price Infomativeness</a></blockquote><iframe sandbox="allow-scripts" security="restricted" src="https://www.uni.lu/fr/events/identifying-price-infomativeness/embed/#?secret=ZRywCXtFr1" width="600" height="338" title="« Identifying Price Infomativeness » — UNI FR" data-secret="ZRywCXtFr1" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" class="wp-embedded-content"></iframe><script> /*! This file is auto-generated */ !function(c,d){"use strict";var e=!1,o=!1;if(d.querySelector)if(c.addEventListener)e=!0;if(c.wp=c.wp||{},c.wp.receiveEmbedMessage);else if(c.wp.receiveEmbedMessage=function(e){var t=e.data;if(!t);else if(!(t.secret||t.message||t.value));else if(/[^a-zA-Z0-9]/.test(t.secret));else{for(var r,s,a,i=d.querySelectorAll('iframe[data-secret="'+t.secret+'"]'),n=d.querySelectorAll('blockquote[data-secret="'+t.secret+'"]'),o=new RegExp("^https?:$","i"),l=0;l<n.length;l++)n[l].style.display="none";for(l=0;l<i.length;l++)if(r=i[l],e.source!==r.contentWindow);else{if(r.removeAttribute("style"),"height"===t.message){if(1e3<(s=parseInt(t.value,10)))s=1e3;else if(~~s<200)s=200;r.height=s}if("link"===t.message)if(s=d.createElement("a"),a=d.createElement("a"),s.href=r.getAttribute("src"),a.href=t.value,!o.test(a.protocol));else if(a.host===s.host)if(d.activeElement===r)c.top.location.href=t.value}}},e)c.addEventListener("message",c.wp.receiveEmbedMessage,!1),d.addEventListener("DOMContentLoaded",t,!1),c.addEventListener("load",t,!1);function t(){if(o);else{o=!0;for(var e,t,r,s=-1!==navigator.appVersion.indexOf("MSIE 10"),a=!!navigator.userAgent.match(/Trident.*rv:11\./),i=d.querySelectorAll("iframe.wp-embedded-content"),n=0;n<i.length;n++){if(!(r=(t=i[n]).getAttribute("data-secret")))r=Math.random().toString(36).substr(2,10),t.src+="#?secret="+r,t.setAttribute("data-secret",r);if(s||a)(e=t.cloneNode(!0)).removeAttribute("security"),t.parentNode.replaceChild(e,t);t.contentWindow.postMessage({message:"ready",secret:r},"*")}}}}(window,document); </script> https://www.uni.lu/wp-content/uploads/sites/11/2026/03/03120045/UNIV_SM-Profile_1600x1600px-scaled.jpg25602560AbstractWe show that outcomes (parameter estimates and R-squareds) of regressions of prices on fundamentals allow usto recover exact measures of the ability of asset prices to aggregate dispersed information. Formally, we show how torecover absolute and relative price informativeness in dynamic environments with rich heterogeneity across investors(regarding signals, private trading needs, or preferences), minimal distributional assumptions, multiple risky assets,and allowing for stationary and non-stationary asset payoffs. We implement our methodology empirically, findingstock-specific measures of price informativeness for U.S. stocks. We find a right-skewed distribution of priceinformativeness, measured in the form of the Kalman gain used by an external observer that conditions its posteriorbelief on the asset price. The recovered mean and median are 0.05 and 0.02 respectively. We find that priceinformativeness is higher for stocks with higher market capitalization and higher trading volume.