{"id":43225,"date":"2026-01-16T10:00:00","date_gmt":"2026-01-16T02:00:00","guid":{"rendered":"https:\/\/www.tejwin.com\/?post_type=insight&#038;p=43225"},"modified":"2026-02-02T13:10:09","modified_gmt":"2026-02-02T05:10:09","slug":"from-news-to-markets-investment-signals-from-media-coverage-part-i-an-empirical-analysis-of-tcri-watchdog-n-news-media-events","status":"publish","type":"insight","link":"https:\/\/www.tejwin.com\/en\/insight\/from-news-to-markets-investment-signals-from-media-coverage-part-i-an-empirical-analysis-of-tcri-watchdog-n-news-media-events\/","title":{"rendered":"From News to Markets: Investment Signals from Media Coverage (Part I) \u2014 An Empirical Analysis of TCRI Watchdog \u201cN News Media\u201d Events"},"content":{"rendered":"\n<figure class=\"wp-block-image aligncenter size-large is-style-default\"><img fetchpriority=\"high\" decoding=\"async\" width=\"1024\" height=\"683\" src=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/\/austin-distel-EMPZ7yRZoGw-unsplash-1-1024x683.jpg\" alt=\"From News to Markets: Investment Signals from Media Coverage (Part I) \u2014 An Empirical Analysis of TCRI Watchdog \u201cN News Media\u201d Events\" class=\"wp-image-42912\" style=\"aspect-ratio:16\/9;object-fit:cover\" srcset=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/austin-distel-EMPZ7yRZoGw-unsplash-1-1024x683.jpg 1024w, https:\/\/www.tejwin.com\/wp-content\/uploads\/austin-distel-EMPZ7yRZoGw-unsplash-1-300x200.jpg 300w, https:\/\/www.tejwin.com\/wp-content\/uploads\/austin-distel-EMPZ7yRZoGw-unsplash-1-150x100.jpg 150w, https:\/\/www.tejwin.com\/wp-content\/uploads\/austin-distel-EMPZ7yRZoGw-unsplash-1-768x512.jpg 768w, https:\/\/www.tejwin.com\/wp-content\/uploads\/austin-distel-EMPZ7yRZoGw-unsplash-1-1536x1024.jpg 1536w, https:\/\/www.tejwin.com\/wp-content\/uploads\/austin-distel-EMPZ7yRZoGw-unsplash-1-2048x1365.jpg 2048w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><figcaption class=\"wp-element-caption\">Photo by <a href=\"https:\/\/unsplash.com\/@moneyphotos?utm_content=creditCopyText&amp;utm_medium=referral&amp;utm_source=unsplash\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">rc.xyz NFT gallery<\/a> on <a href=\"https:\/\/unsplash.com\/photos\/a-pile-of-money-sitting-on-top-of-a-wooden-floor-o-MyHqEEHoM?utm_content=creditCopyText&amp;utm_medium=referral&amp;utm_source=unsplash\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">Unsplash<\/a><\/figcaption><\/figure>\n\n\n\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_81 counter-hierarchy ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<label for=\"ez-toc-cssicon-toggle-item-69f0d3a97c280\" class=\"ez-toc-cssicon-toggle-label\"><span class=\"ez-toc-cssicon\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/label><input type=\"checkbox\"  id=\"ez-toc-cssicon-toggle-item-69f0d3a97c280\"  aria-label=\"Toggle\" \/><nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/www.tejwin.com\/en\/insight\/from-news-to-markets-investment-signals-from-media-coverage-part-i-an-empirical-analysis-of-tcri-watchdog-n-news-media-events\/#Introduction_News_as_an_Event-Based_Market_Signal\" >Introduction: News as an Event-Based Market Signal<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/www.tejwin.com\/en\/insight\/from-news-to-markets-investment-signals-from-media-coverage-part-i-an-empirical-analysis-of-tcri-watchdog-n-news-media-events\/#The_TCRI_Watchdog_Product_Converting_News_into_Quantifiable_Event_Data\" >The TCRI Watchdog Product: Converting News into Quantifiable Event Data<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/www.tejwin.com\/en\/insight\/from-news-to-markets-investment-signals-from-media-coverage-part-i-an-empirical-analysis-of-tcri-watchdog-n-news-media-events\/#Core_Features_of_TCRI_Watchdog\" >Core Features of TCRI Watchdog<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/www.tejwin.com\/en\/insight\/from-news-to-markets-investment-signals-from-media-coverage-part-i-an-empirical-analysis-of-tcri-watchdog-n-news-media-events\/#Research_Motivation_and_Methodology_Capturing_Market_Reactions_through_Event_Studies\" >Research Motivation and Methodology: Capturing Market Reactions through Event Studies<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/www.tejwin.com\/en\/insight\/from-news-to-markets-investment-signals-from-media-coverage-part-i-an-empirical-analysis-of-tcri-watchdog-n-news-media-events\/#Empirical_Results_How_News_Events_Are_Absorbed_and_Corrected_by_the_Market\" >Empirical Results: How News Events Are Absorbed and Corrected by the Market<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/www.tejwin.com\/en\/insight\/from-news-to-markets-investment-signals-from-media-coverage-part-i-an-empirical-analysis-of-tcri-watchdog-n-news-media-events\/#Distribution_of_News_Events\" >Distribution of News Events<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/www.tejwin.com\/en\/insight\/from-news-to-markets-investment-signals-from-media-coverage-part-i-an-empirical-analysis-of-tcri-watchdog-n-news-media-events\/#Market_Reactions_by_Event_Intensity\" >Market Reactions by Event Intensity<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/www.tejwin.com\/en\/insight\/from-news-to-markets-investment-signals-from-media-coverage-part-i-an-empirical-analysis-of-tcri-watchdog-n-news-media-events\/#The_Time_Structure_of_Market_Reactions_Has_Prior_Reaction_Already_Occurred\" >The Time Structure of Market Reactions: Has Prior Reaction Already Occurred?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/www.tejwin.com\/en\/insight\/from-news-to-markets-investment-signals-from-media-coverage-part-i-an-empirical-analysis-of-tcri-watchdog-n-news-media-events\/#Conclusion_News_coverage_is_generally_effective_but_its_impact_is_highly_dependent_on_the_nature_of_the_event\" >Conclusion: News coverage is generally effective, but its impact is highly dependent on the nature of the event.<\/a><\/li><\/ul><\/nav><\/div>\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Introduction_News_as_an_Event-Based_Market_Signal\"><\/span><strong>Introduction: News as an Event-Based Market Signal<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>In today\u2019s highly real-time and information-saturated markets, news media no longer merely serve as post-hoc explanations of price movements. Instead, they have become a critical channel through which market expectations are formed and sentiment spreads. Compared with structured disclosures such as regulatory penalties or official disclosures via the Market Observation Post System (MOPS), news reports often reveal changes in corporate operations, latent risks, or emerging industry trends at an earlier stage. As a result, news is frequently regarded in practice as the <em>earliest but most difficult-to-interpret<\/em> source of information.<\/p>\n\n\n\n<p>Given the diversity of news content and the heterogeneity of narrative framing, the key challenge investors face is not whether they should pay attention to news, but rather <strong>how to determine whether a given piece of news truly contains actionable investment signals<\/strong>.<\/p>\n\n\n\n<p><a href=\"https:\/\/www.tejwin.com\/en\/news\/tcri-watchdog\/\" target=\"_blank\" rel=\"noreferrer noopener\">TCRI Watchdog (WD)<\/a> addresses this challenge by transforming highly unstructured news events into standardized and comparable <strong>event intensity scores ranging from \u20133 to +3<\/strong>, based on their potential impact on corporate credit risk. Through a systematic framework for event definition and quantification, news is converted from subjective textual narratives into analyzable and comparable quantitative data. Building on this foundation, this study applies an <strong>event study methodology<\/strong> to empirically examine how stock prices respond to news events of varying intensity, and to clarify the actual role that news plays in investment decision-making.<\/p>\n\n\n\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<p class=\"has-background has-medium-font-size\" style=\"background-color:#ffe9ae\"><strong><em>\ud83d\udc49<\/em> <\/strong><a href=\"https:\/\/www.tejwin.com\/en\/news\/tcri-watchdog\/\" target=\"_blank\" rel=\"noreferrer noopener\"><strong>Track how news moves the market with TCRI Watchdog<\/strong><\/a>!<\/p>\n\n\n\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"The_TCRI_Watchdog_Product_Converting_News_into_Quantifiable_Event_Data\"><\/span><strong>The TCRI Watchdog Product: Converting News into Quantifiable Event Data<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>In fast-moving markets, investors need more than simply knowing that an event has occurred; they require the ability to quantify its market impact in real time. TCRI Watchdog (WD) transforms unstructured textual information\u2014including corporate disclosures, regulatory announcements, and news coverage\u2014into observable, comparable, and traceable <strong>event signals (Event Intensity)<\/strong>.<\/p>\n\n\n\n<p>TCRI WD conducts daily coverage of all Taiwanese listed and OTC companies, including delisted firms, drawing from the following sources:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Market Observation Post System (MOPS):<\/strong> material disclosures and corporate announcements<\/li>\n\n\n\n<li><strong>Regulatory agencies:<\/strong> official releases from the Financial Supervisory Commission, Ministry of Economic Affairs, Fair Trade Commission, and other authorities<\/li>\n\n\n\n<li><strong>News media and online platforms:<\/strong> major financial and industry news outlets<\/li>\n<\/ul>\n\n\n\n<p>Each event is jointly calibrated by TEJ\u2019s analytical team and models and assigned an event intensity score from \u20133 to +3 according to its potential impact on corporate credit risk. Events are further classified into <strong>5 major dimensions and more than 100 subcategories<\/strong>.<\/p>\n\n\n\n<p><strong>Table1\uff1a 5 dimensions of TCRI Watchdog event<\/strong><\/p>\n\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><td><strong>Code<\/strong><\/td><td><strong>Event Category<\/strong><strong><\/strong><\/td><td><strong>Meaning<\/strong><strong><\/strong><\/td><\/tr><tr><td>A<\/td><td><strong>Accounting<\/strong><\/td><td>Events related to financial reporting, accounting treatments, and disclosure practices, reflecting financial transparency and stability.<\/td><\/tr><tr><td>I<\/td><td><strong>Industry <\/strong><\/td><td>Events related to operating environment, capacity, costs, R&amp;D progress, and financing, revealing operating momentum and industry trends.<\/td><\/tr><tr><td>M<\/td><td><strong>Management<\/strong><\/td><td>Events involving corporate governance, board and executive changes, internal or external fraud, labor disputes, information security, and internal control deficiencies, reflecting governance and management stability.<\/td><\/tr><tr><td>F<\/td><td><strong>Market Trading<\/strong><\/td><td>Events related to capital market trading, rating changes, abnormal price movements, and listing status, reflecting market perceptions of credit and liquidity.<\/td><\/tr><tr><td>R<\/td><td><strong>Risk<\/strong><\/td><td>Events involving financial distress, default, delisting, or restructuring, representing the most direct signals of credit risk.<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p>This framework allows investors to evaluate and compare market events using a unified standard, whether for individual stock risk monitoring or broader assessments of market risk sentiment.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Core_Features_of_TCRI_Watchdog\"><\/span><strong>Core Features of TCRI Watchdog<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>\ud83d\udccaSystemic Monitoring:<\/strong> A daily, event-based monitoring framework suitable for portfolio surveillance and thematic screening.<\/li>\n\n\n\n<li><strong>\ud83d\udca1Quantitative Consistency:<\/strong> Full preservation of announcement dates and version histories to avoid look-ahead bias; inclusion of delisted companies to eliminate survivorship bias.<\/li>\n\n\n\n<li><strong>\ud83e\uddedSentiment Signal:<\/strong> Event intensity serves as a sentiment factor, compensating for the timeliness limitations of traditional financial statements.<\/li>\n\n\n\n<li><strong>\ud83d\udd0dRisk Integration:<\/strong> Event data can be integrated into credit risk models and quantitative investment strategies to enhance real-time decision-making.<\/li>\n<\/ul>\n\n\n\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<p class=\"has-background has-medium-font-size\" style=\"background-color:#ffe9ae\"><strong>\ud83d\udc49<\/strong><a href=\"https:\/\/www.tejwin.com\/en\/news\/factor-library\/\"><strong>You <\/strong><\/a><strong><a href=\"https:\/\/www.tejwin.com\/en\/news\/factor-library\/\" target=\"_blank\" rel=\"noreferrer noopener\">may also be interested in TEJ\u2019s Factor Library , which provides standardized factor data for systematic market analysis.<\/a><\/strong><\/p>\n\n\n\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Research_Motivation_and_Methodology_Capturing_Market_Reactions_through_Event_Studies\"><\/span><strong>Research Motivation and Methodology: Capturing Market Reactions through Event Studies<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>Compared with highly standardized and consistently disclosed information sources, news events exhibit substantial heterogeneity in source, narrative perspective, and disclosure timing. As a result, their market impact is more difficult to anticipate ex ante. Some news merely reiterates existing information and has limited price impact, while other reports reveal operational risks, governance issues, or latent changes before formal disclosures appear, thereby acting as leading indicators of price adjustments.<\/p>\n\n\n\n<p>This characteristic\u2014that similar news items may carry vastly different levels of informational content\u2014has long made news both highly scrutinized and difficult to utilize systematically in investment practice.<\/p>\n\n\n\n<p>Because the impact of news is driven not by whether it is reported, but by the <strong>risk implications and informational intensity embedded within it<\/strong>, anecdotal evidence or subjective judgment alone is insufficient to determine whether news has stable predictive power. Accordingly, the core objective of this study is to place heterogeneous news events on a common, comparable scale through event-intensity quantification, and to empirically test their stock-price impact using an event study framework. Through empirical evidence, the study seeks to determine whether news functions primarily as a short-term sentiment trigger or as an information source suitable for systematic analysis.<\/p>\n\n\n\n<p><strong>Research Scope and Event Design<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Market coverage:<\/strong> Taiwanese listed and OTC companies<\/li>\n\n\n\n<li><strong>Event source:<\/strong> \u201cN Source\u201d \u2013 news media<\/li>\n\n\n\n<li><strong>Sample period:<\/strong> January 1, 2018 to August 31, 2025<\/li>\n\n\n\n<li><strong>Number of events:<\/strong> approximately 30,000 news events<\/li>\n\n\n\n<li><strong>Event categories:<\/strong> A, I, M, F, and R<\/li>\n\n\n\n<li><strong>Event grouping:<\/strong> Each event is assigned an intensity score and grouped accordingly to examine whether markets respond systematically to varying levels of embedded risk, such as through advance reactions, delayed responses, or post-event corrections.<\/li>\n<\/ul>\n\n\n\n<p><strong>Table 2. Definition of Event Intensity Groups<\/strong><\/p>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><tbody><tr><td class=\"has-text-align-right\" data-align=\"right\"><strong>Group<\/strong><\/td><td class=\"has-text-align-right\" data-align=\"right\"><strong>Event Intensity<\/strong><\/td><\/tr><tr><td class=\"has-text-align-right\" data-align=\"right\"><strong>negative<\/strong><\/td><td class=\"has-text-align-right\" data-align=\"right\"><strong>&lt;0<\/strong><\/td><\/tr><tr><td class=\"has-text-align-right\" data-align=\"right\"><strong>-1<\/strong><\/td><td class=\"has-text-align-right\" data-align=\"right\"><strong>=-1<\/strong><\/td><\/tr><tr><td class=\"has-text-align-right\" data-align=\"right\"><strong>-2<\/strong><\/td><td class=\"has-text-align-right\" data-align=\"right\"><strong>=-2<\/strong><\/td><\/tr><tr><td class=\"has-text-align-right\" data-align=\"right\"><strong>-3<\/strong><\/td><td class=\"has-text-align-right\" data-align=\"right\"><strong>=-3<\/strong><\/td><\/tr><tr><td class=\"has-text-align-right\" data-align=\"right\"><strong>positive<\/strong><\/td><td class=\"has-text-align-right\" data-align=\"right\"><strong>&gt;0<\/strong><\/td><\/tr><tr><td class=\"has-text-align-right\" data-align=\"right\"><strong>1<\/strong><\/td><td class=\"has-text-align-right\" data-align=\"right\"><strong>=1<\/strong><\/td><\/tr><tr><td class=\"has-text-align-right\" data-align=\"right\"><strong>2<\/strong><\/td><td class=\"has-text-align-right\" data-align=\"right\"><strong>=2<\/strong><\/td><\/tr><tr><td class=\"has-text-align-right\" data-align=\"right\"><strong>3<\/strong><\/td><td class=\"has-text-align-right\" data-align=\"right\"><strong>=3<\/strong><\/td><\/tr><tr><td class=\"has-text-align-right\" data-align=\"right\"><strong>neutral<\/strong><\/td><td class=\"has-text-align-right\" data-align=\"right\"><strong>=0<\/strong><\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p><strong>Event Study Design<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Estimation window:<\/strong> 250 trading days; normal returns estimated using the Fama\u2013French three-factor model<\/li>\n\n\n\n<li><strong>Gap days:<\/strong> 10 (to avoid overlap between estimation and event windows)<\/li>\n\n\n\n<li><strong>Event window:<\/strong> (\u201310, +10), used to calculate abnormal returns (AR) and cumulative abnormal returns (CAR)<\/li>\n<\/ul>\n\n\n\n<p>For multi-event analysis, average abnormal returns (AAR) and cumulative average abnormal returns (CAAR) are computed to assess overall market responses to specific event intensities or categories. To further analyze the timing of market reactions, event-window effects are decomposed into pre-event, event-day, and post-event phases, with additional ratio-based and marginal-effect metrics used to evaluate the speed and structure of information absorption.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Empirical_Results_How_News_Events_Are_Absorbed_and_Corrected_by_the_Market\"><\/span><strong>Empirical Results: How News Events Are Absorbed and Corrected by the Market<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>This section focuses on market reactions to news media (N-source) events, beginning with event-intensity groupings and further decomposing price reactions before and after event occurrences. It also examines whether systematic differences exist across event categories.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Distribution_of_News_Events\"><\/span><strong>Distribution of News Events<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>Figure 3 shows the distribution of event intensities. Neutral events (score = 0) and mildly positive events (+1) dominate the sample, accounting for 51.5% and 30.9%, respectively. Events with a score of \u20131 represent 15.8%. Severe negative events (\u20132, \u20133) and strongly positive events (+2 or above) together account for only about 2% of observations.<\/p>\n\n\n\n<p><\/p>\n\n\n\n<p><strong>Figure3 \uff1aEvent Distribution Chart<\/strong><\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" width=\"739\" height=\"423\" src=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/image-765.png\" alt=\"\" class=\"wp-image-43231\" srcset=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/image-765.png 739w, https:\/\/www.tejwin.com\/wp-content\/uploads\/image-765-300x172.png 300w, https:\/\/www.tejwin.com\/wp-content\/uploads\/image-765-150x86.png 150w\" sizes=\"(max-width: 739px) 100vw, 739px\" \/><\/figure>\n\n\n\n<p><\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Market_Reactions_by_Event_Intensity\"><\/span><strong>Market Reactions by Event Intensity<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>Empirical results indicate that news events exhibit clear directional effects. The final CAAR (at t+10) for negative events increases monotonically with event intensity: \u20131, \u20132, and \u20133 groups show final CAARs of \u20131.89%, \u20134.43%, and \u20137.97%, respectively. Although statistical significance for the \u20133 group is weaker due to limited sample size, the economic magnitude remains substantial.<\/p>\n\n\n\n<p>Positive news generates positive market reactions, but with significantly smaller magnitudes. The +1 group shows a CAAR of approximately +1.40%, while higher-intensity positive events do not exhibit linear amplification, suggesting a more cautious market response to favorable news. Neutral events show virtually no persistent price impact, with a final CAAR of only +0.14%, indicating rapid market absorption of low-information news.<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><td class=\"has-text-align-right\" data-align=\"right\">Group<\/td><td class=\"has-text-align-right\" data-align=\"right\">Event Count<\/td><td class=\"has-text-align-right\" data-align=\"right\">Mean AAR (%)<\/td><td class=\"has-text-align-right\" data-align=\"right\">Final CAAR (%)<\/td><\/tr><tr><td class=\"has-text-align-right\" data-align=\"right\">-1<\/td><td class=\"has-text-align-right\" data-align=\"right\">4691<\/td><td class=\"has-text-align-right\" data-align=\"right\">-0.09%<\/td><td class=\"has-text-align-right\" data-align=\"right\">-1.89%***<\/td><\/tr><tr><td class=\"has-text-align-right\" data-align=\"right\">-2<\/td><td class=\"has-text-align-right\" data-align=\"right\">452<\/td><td class=\"has-text-align-right\" data-align=\"right\">-0.20%<\/td><td class=\"has-text-align-right\" data-align=\"right\">-4.43%***<\/td><\/tr><tr><td class=\"has-text-align-right\" data-align=\"right\">-3<\/td><td class=\"has-text-align-right\" data-align=\"right\">58<\/td><td class=\"has-text-align-right\" data-align=\"right\">-0.38%<\/td><td class=\"has-text-align-right\" data-align=\"right\">-7.97%<\/td><\/tr><tr><td class=\"has-text-align-right\" data-align=\"right\">1<\/td><td class=\"has-text-align-right\" data-align=\"right\">9179<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.07%<\/td><td class=\"has-text-align-right\" data-align=\"right\">1.40%***<\/td><\/tr><tr><td class=\"has-text-align-right\" data-align=\"right\">2<\/td><td class=\"has-text-align-right\" data-align=\"right\">45<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.02%<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.50%<\/td><\/tr><tr><td class=\"has-text-align-right\" data-align=\"right\">negative<\/td><td class=\"has-text-align-right\" data-align=\"right\">5201<\/td><td class=\"has-text-align-right\" data-align=\"right\">-0.11%<\/td><td class=\"has-text-align-right\" data-align=\"right\">-2.25%***<\/td><\/tr><tr><td class=\"has-text-align-right\" data-align=\"right\">neutral<\/td><td class=\"has-text-align-right\" data-align=\"right\">15311<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.01%<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.14%<\/td><\/tr><tr><td class=\"has-text-align-right\" data-align=\"right\">positive<\/td><td class=\"has-text-align-right\" data-align=\"right\">9224<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.07%<\/td><td class=\"has-text-align-right\" data-align=\"right\">1.40%***<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p>Note: Mean AAR (%) : The average of the daily AAR values during the event period.<\/p>\n\n\n\n<p>\u3000\u3000Final CAAR (%) : The CAAR value at the end of the event period (t+10).<\/p>\n\n\n\n<p><\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"The_Time_Structure_of_Market_Reactions_Has_Prior_Reaction_Already_Occurred\"><\/span><strong>The Time Structure of Market Reactions: Has Prior Reaction Already Occurred?<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>A defining characteristic of news events is that <strong>market reactions are highly concentrated before the event date<\/strong>. For negative news, approximately 60\u201370% of the final CAAR is realized by t\u20131 for the \u20131 and \u20132 groups, while the \u20133 group exhibits pre-event reactions exceeding 100%, implying that price adjustments are largely completed before the news becomes the market\u2019s focal point.<\/p>\n\n\n\n<p>Event-day (t=0) marginal effects are limited, reflecting substantial information absorption in advance. Post-event reactions diverge: moderate negative events (\u20131 and \u20132) display small but persistent negative drift, whereas extreme negative events (\u20133) show post-event reversals, suggesting prior overreaction. Positive events exhibit similar patterns, with gains largely realized pre-event and subsequent corrections afterward. Overall, news behaves more like a <strong>leading indicator<\/strong> than a source of sustained post-event price discovery.<\/p>\n\n\n\n<p><strong>Table 5<\/strong>\uff1a<strong>Effect Distribution Table<\/strong><\/p>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><tbody><tr><td><strong>Group<\/strong><\/td><td class=\"has-text-align-right\" data-align=\"right\"><strong>CAAR(t-1)%<\/strong><\/td><td class=\"has-text-align-right\" data-align=\"right\"><strong>CAAR(t=0)%<\/strong><\/td><td class=\"has-text-align-right\" data-align=\"right\"><strong>CAAR(t+10)%<\/strong><\/td><td class=\"has-text-align-right\" data-align=\"right\"><strong>AAR(t-1)%<\/strong><\/td><td class=\"has-text-align-right\" data-align=\"right\"><strong>AAR(t=0)%<\/strong><\/td><td class=\"has-text-align-right\" data-align=\"right\"><strong>Pre%<\/strong><\/td><td class=\"has-text-align-right\" data-align=\"right\"><strong>t=0%<\/strong><\/td><td class=\"has-text-align-right\" data-align=\"right\"><strong>Post%<\/strong><\/td><td class=\"has-text-align-right\" data-align=\"right\"><strong>Marginal%<\/strong><\/td><td class=\"has-text-align-right\" data-align=\"right\"><strong>Post-Ret%<\/strong><\/td><\/tr><tr><td>-1<\/td><td class=\"has-text-align-right\" data-align=\"right\">-1.25<\/td><td class=\"has-text-align-right\" data-align=\"right\">-1.43<\/td><td class=\"has-text-align-right\" data-align=\"right\">-1.89<\/td><td class=\"has-text-align-right\" data-align=\"right\">-0.47<\/td><td class=\"has-text-align-right\" data-align=\"right\">-0.18<\/td><td class=\"has-text-align-right\" data-align=\"right\">66.2<\/td><td class=\"has-text-align-right\" data-align=\"right\">9.4<\/td><td class=\"has-text-align-right\" data-align=\"right\">24.4<\/td><td class=\"has-text-align-right\" data-align=\"right\">-62.4<\/td><td class=\"has-text-align-right\" data-align=\"right\">-0.46<\/td><\/tr><tr><td>-2<\/td><td class=\"has-text-align-right\" data-align=\"right\">-3.14<\/td><td class=\"has-text-align-right\" data-align=\"right\">-4.11<\/td><td class=\"has-text-align-right\" data-align=\"right\">-4.43<\/td><td class=\"has-text-align-right\" data-align=\"right\">-1.33<\/td><td class=\"has-text-align-right\" data-align=\"right\">-0.97<\/td><td class=\"has-text-align-right\" data-align=\"right\">70.9<\/td><td class=\"has-text-align-right\" data-align=\"right\">21.8<\/td><td class=\"has-text-align-right\" data-align=\"right\">7.3<\/td><td class=\"has-text-align-right\" data-align=\"right\">-27.1<\/td><td class=\"has-text-align-right\" data-align=\"right\">-0.32<\/td><\/tr><tr><td>-3<\/td><td class=\"has-text-align-right\" data-align=\"right\">-9.25<\/td><td class=\"has-text-align-right\" data-align=\"right\">-10.39<\/td><td class=\"has-text-align-right\" data-align=\"right\">-7.97<\/td><td class=\"has-text-align-right\" data-align=\"right\">-3.20<\/td><td class=\"has-text-align-right\" data-align=\"right\">-1.14<\/td><td class=\"has-text-align-right\" data-align=\"right\">116.0<\/td><td class=\"has-text-align-right\" data-align=\"right\">14.4<\/td><td class=\"has-text-align-right\" data-align=\"right\">-30.4<\/td><td class=\"has-text-align-right\" data-align=\"right\">-64.2<\/td><td class=\"has-text-align-right\" data-align=\"right\">2.42<\/td><\/tr><tr><td>1<\/td><td class=\"has-text-align-right\" data-align=\"right\">1.77<\/td><td class=\"has-text-align-right\" data-align=\"right\">1.82<\/td><td class=\"has-text-align-right\" data-align=\"right\">1.40<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.48<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.05<\/td><td class=\"has-text-align-right\" data-align=\"right\">126.6<\/td><td class=\"has-text-align-right\" data-align=\"right\">3.6<\/td><td class=\"has-text-align-right\" data-align=\"right\">-30.2<\/td><td class=\"has-text-align-right\" data-align=\"right\">-89.6<\/td><td class=\"has-text-align-right\" data-align=\"right\">-0.42<\/td><\/tr><tr><td>2<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.82<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.86<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.50<\/td><td class=\"has-text-align-right\" data-align=\"right\">-0.48<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.04<\/td><td class=\"has-text-align-right\" data-align=\"right\">165.4<\/td><td class=\"has-text-align-right\" data-align=\"right\">7.5<\/td><td class=\"has-text-align-right\" data-align=\"right\">-72.9<\/td><td class=\"has-text-align-right\" data-align=\"right\">-107.7<\/td><td class=\"has-text-align-right\" data-align=\"right\">-0.36<\/td><\/tr><tr><td>negative<\/td><td class=\"has-text-align-right\" data-align=\"right\">-1.54<\/td><td class=\"has-text-align-right\" data-align=\"right\">-1.81<\/td><td class=\"has-text-align-right\" data-align=\"right\">-2.25<\/td><td class=\"has-text-align-right\" data-align=\"right\">-0.58<\/td><td class=\"has-text-align-right\" data-align=\"right\">-0.27<\/td><td class=\"has-text-align-right\" data-align=\"right\">68.6<\/td><td class=\"has-text-align-right\" data-align=\"right\">11.9<\/td><td class=\"has-text-align-right\" data-align=\"right\">19.5<\/td><td class=\"has-text-align-right\" data-align=\"right\">-53.9<\/td><td class=\"has-text-align-right\" data-align=\"right\">-0.44<\/td><\/tr><tr><td>neutral<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.64<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.62<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.14<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.19<\/td><td class=\"has-text-align-right\" data-align=\"right\">-0.02<\/td><td class=\"has-text-align-right\" data-align=\"right\">457.6<\/td><td class=\"has-text-align-right\" data-align=\"right\">-11.6<\/td><td class=\"has-text-align-right\" data-align=\"right\">-346.0<\/td><td class=\"has-text-align-right\" data-align=\"right\">-108.3<\/td><td class=\"has-text-align-right\" data-align=\"right\">-0.48<\/td><\/tr><tr><td>positive<\/td><td class=\"has-text-align-right\" data-align=\"right\">1.77<\/td><td class=\"has-text-align-right\" data-align=\"right\">1.82<\/td><td class=\"has-text-align-right\" data-align=\"right\">1.40<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.48<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.05<\/td><td class=\"has-text-align-right\" data-align=\"right\">126.6<\/td><td class=\"has-text-align-right\" data-align=\"right\">3.6<\/td><td class=\"has-text-align-right\" data-align=\"right\">-30.2<\/td><td class=\"has-text-align-right\" data-align=\"right\">-89.5<\/td><td class=\"has-text-align-right\" data-align=\"right\">-0.42<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p>Note: Pre-event weight: CAAR(t-1) \/ CAAR(t+10) \u00d7 100%<\/p>\n\n\n\n<p>\u3000\u3000Event-day weight: AAR(t=0) \/ CAAR(t+10) \u00d7 100%<\/p>\n\n\n\n<p>\u3000\u3000Post-event weight: [CAAR(t+10) &#8211; CAAR(t=0)] \/ CAAR(t+10) \u00d7 100%<\/p>\n\n\n\n<p>\u3000\u3000Marginal reaction strength: [AAR(t=0) \/ AAR(t-1) &#8211; 1] \u00d7 100%<\/p>\n\n\n\n<p>\u3000\u3000Post-event reward: CAAR(t+10) &#8211; CAAR(t=0)<\/p>\n\n\n\n<p><\/p>\n\n\n\n<p><strong>Figure 6<\/strong>\uff1a<strong>Group Comparison Chart<\/strong><\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" width=\"865\" height=\"837\" src=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/image-766.png\" alt=\"\" class=\"wp-image-43235\" srcset=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/image-766.png 865w, https:\/\/www.tejwin.com\/wp-content\/uploads\/image-766-300x290.png 300w, https:\/\/www.tejwin.com\/wp-content\/uploads\/image-766-150x145.png 150w, https:\/\/www.tejwin.com\/wp-content\/uploads\/image-766-768x743.png 768w\" sizes=\"(max-width: 865px) 100vw, 865px\" \/><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Conclusion_News_coverage_is_generally_effective_but_its_impact_is_highly_dependent_on_the_nature_of_the_event\"><\/span><strong>Conclusion: News coverage is generally effective, but its impact is highly dependent on the nature of the event.<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>Taken together, these findings demonstrate that news events do exert systematic effects on stock prices, but their impact depends critically on embedded risk intensity and timing. This raises a further question: <strong>do different types of news carry different levels of informational content?<\/strong> The next section (Part II) addresses this question by decomposing news events into five categories and examining cross-category differences in market impact.<\/p>\n\n\n\n<p><\/p>\n\n\n\n<p class=\"has-background has-medium-font-size\" style=\"background-color:#ffe9ae\"><strong><em>\ud83d\udc49<\/em><\/strong> News matters, but not all news has the same impact. <a href=\"https:\/\/www.tejwin.com\/en\/insight\/from-news-to-markets-investment-signals-from-media-coverage-part-ii-an-empirical-analysis-of-tcri-watchdog-n-news-media-events\/\" target=\"_blank\" data-type=\"link\" data-id=\"https:\/\/www.tejwin.com\/en\/insight\/from-news-to-markets-investment-signals-from-media-coverage-part-ii-an-empirical-analysis-of-tcri-watchdog-n-news-media-events\/\" rel=\"noreferrer noopener\">Part II breaks down the difference.<\/a><\/p>\n\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Introduction: News as an Event-Based Market Signal In today\u2019s highly real-time and information-saturated markets, news media no longer merely serve as post-hoc explanations of price movements. Instead, they have become a critical channel through which market expectations are formed and sentiment spreads. Compared with structured disclosures such as regulatory penalties or official disclosures via the [&hellip;]<\/p>\n","protected":false},"featured_media":42913,"template":"","tags":[3264,3476],"insight-category":[690,3509,1356],"class_list":["post-43225","insight","type-insight","status-publish","has-post-thumbnail","hentry","tag-alternative-data","tag-tcri-watchdog","insight-category-data-analysis","insight-category-fintech-en","insight-category-tquant-lab-en"],"acf":[],"_links":{"self":[{"href":"https:\/\/www.tejwin.com\/en\/wp-json\/wp\/v2\/insight\/43225","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.tejwin.com\/en\/wp-json\/wp\/v2\/insight"}],"about":[{"href":"https:\/\/www.tejwin.com\/en\/wp-json\/wp\/v2\/types\/insight"}],"version-history":[{"count":20,"href":"https:\/\/www.tejwin.com\/en\/wp-json\/wp\/v2\/insight\/43225\/revisions"}],"predecessor-version":[{"id":43280,"href":"https:\/\/www.tejwin.com\/en\/wp-json\/wp\/v2\/insight\/43225\/revisions\/43280"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.tejwin.com\/en\/wp-json\/wp\/v2\/media\/42913"}],"wp:attachment":[{"href":"https:\/\/www.tejwin.com\/en\/wp-json\/wp\/v2\/media?parent=43225"}],"wp:term":[{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.tejwin.com\/en\/wp-json\/wp\/v2\/tags?post=43225"},{"taxonomy":"insight-category","embeddable":true,"href":"https:\/\/www.tejwin.com\/en\/wp-json\/wp\/v2\/insight-category?post=43225"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}