{"id":2039,"date":"2023-05-30T14:45:27","date_gmt":"2023-05-30T12:45:27","guid":{"rendered":"https:\/\/www.uni.lu\/fdef-fr\/events\/df-lunch-seminar-social-media-as-a-bank-run-catalyst\/"},"modified":"2024-01-19T15:39:11","modified_gmt":"2024-01-19T14:39:11","slug":"df-lunch-seminar-social-media-as-a-bank-run-catalyst","status":"publish","type":"events","link":"https:\/\/www.uni.lu\/fdef-fr\/events\/df-lunch-seminar-social-media-as-a-bank-run-catalyst\/","title":{"rendered":"DF Lunch Seminar &#8211; Social Media as a Bank Run Catalyst"},"content":{"rendered":"\n<section class=\"wp-block-unilux-blocks-free-section section\"><div class=\"container xl:max-w-screen-xl\">\n<p>Abstract:&nbsp;<\/p>\n\n\n\n<p>Social media fueled a bank run on Silicon Valley Bank (SVB), and the effects were<\/p>\n\n\n\n<p>felt broadly in the U.S. banking industry. We employ comprehensive Twitter data to<\/p>\n\n\n\n<p>show that preexisting exposure to social media predicts bank stock market losses in<\/p>\n\n\n\n<p>the run period even after controlling for bank characteristics related to run risk (i.e.,<\/p>\n\n\n\n<p>mark-to-market losses and uninsured deposits). Moreover, we show that social media<\/p>\n\n\n\n<p>amplifies these bank run risk factors. During the run period, we find the intensity of<\/p>\n\n\n\n<p>Twitter conversation about a bank predicts stock market losses at the hourly frequency.<\/p>\n\n\n\n<p>This effect is stronger for banks with bank run risk factors. At even higher frequency,<\/p>\n\n\n\n<p>tweets in the run period with negative sentiment translate into immediate stock market<\/p>\n\n\n\n<p>losses. These high frequency effects are stronger when tweets are authored by members<\/p>\n\n\n\n<p>of the Twitter startup community (who are likely depositors) and contain keywords<\/p>\n\n\n\n<p>related to contagion. These results are consistent with depositors using Twitter to<\/p>\n\n\n\n<p>communicate in real time during the bank run.<\/p>\n\n\n\n<p><i>This event is Supported by the Luxembourg National Research Fund (2022\/17573036)<\/i><\/p>\n\n\n<figure class=\"wp-block-dev4-reusable-blocks-image  object-fit--contain\">\n    \n<img decoding=\"async\" class=\"wp-block-image unilux-custom-image-block\"\n                alt=\"\"\n            src=\"https:\/\/www.uni.lu\/wp-content\/uploads\/sites\/18\/2024\/01\/quadri_gris_transparent_72ppi_1024x207-6.png\"\n                srcset=\"https:\/\/www.uni.lu\/wp-content\/uploads\/sites\/18\/2024\/01\/quadri_gris_transparent_72ppi_1024x207-6-300x61.png 300w, https:\/\/www.uni.lu\/wp-content\/uploads\/sites\/18\/2024\/01\/quadri_gris_transparent_72ppi_1024x207-6-768x155.png 768w, https:\/\/www.uni.lu\/wp-content\/uploads\/sites\/18\/2024\/01\/quadri_gris_transparent_72ppi_1024x207-6.png 1024w\"\n                style=\"object-position: 50.00% 50.00%; font-family: &quot;object-fit: contain; object-position: 50.00% 50.00%;&quot;; aspect-ratio: 16\/9; object-fit: contain; width: 100%;\"\n        loading=\"lazy\"\n\/>    <\/figure><\/div><\/section>\n","protected":false},"excerpt":{"rendered":"<p>Abstract:\u00a0Social media fueled a bank run on Silicon Valley Bank (SVB), and the effects werefelt broadly in the U.S. banking industry. We employ comprehensive Twitter data toshow that preexisting exposure to social media predicts bank stock market losses inthe run period even after controlling for bank characteristics related to run risk (i.e.,mark-to-market losses and uninsured deposits). Moreover, we show that social mediaamplifies these bank run risk factors. During the run period, we find the intensity ofTwitter conversation about a bank predicts stock market losses at the hourly frequency.This effect is stronger for banks with bank run risk factors. At even higher frequency,tweets in the run period with negative sentiment translate into immediate stock marketlosses. These high frequency effects are stronger when tweets are authored by membersof the Twitter startup community (who are likely depositors) and contain keywordsrelated to contagion. These results are consistent with depositors using Twitter tocommunicate in real time during the bank run.<\/p>\n","protected":false},"author":44,"featured_media":2040,"parent":0,"menu_order":0,"comment_status":"open","ping_status":"closed","template":"","format":"standard","meta":{"featured_image_focal_point":[],"show_featured_caption":false,"ulux_newsletter_groups":"","uluxPostTitle":"","uluxPrePostTitle":"","_trash_the_other_posts":false,"_price":"","_stock":"","_tribe_ticket_header":"","_tribe_default_ticket_provider":"","_tribe_ticket_capacity":"0","_ticket_start_date":"","_ticket_end_date":"","_tribe_ticket_show_description":"","_tribe_ticket_show_not_going":false,"_tribe_ticket_use_global_stock":"","_tribe_ticket_global_stock_level":"","_global_stock_mode":"","_global_stock_cap":"","_tribe_rsvp_for_event":"","_tribe_ticket_going_count":"","_tribe_ticket_not_going_count":"","_tribe_tickets_list":"[]","_tribe_ticket_has_attendee_info_fields":false,"event_start_date":"2023-06-15 12:15:00","event_end_date":"2023-06-15 13:45:00","event_speaker_name":"Prof. Juan F. Imbet - Universit\u00e9 Paris Dauphine","event_speaker_link":"","event_is_online":false,"event_location":"Weicker Building, Room B001","event_street":"4 rue Alphonse Weicker","event_location_link":"","event_zip_code":"L-2721","event_city":"Luxembourg ","event_country":"LU"},"events-topic":[301],"events-type":[315,322],"organisation":[115],"authorship":[],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v22.3 (Yoast SEO v22.3) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>DF Lunch Seminar - Social Media as a Bank Run Catalyst - FDEF I Uni.lu<\/title>\n<meta name=\"description\" content=\"Abstract:\u00a0Social media fueled a bank run on Silicon Valley Bank (SVB), and the effects werefelt broadly in the U.S. banking industry. We employ comprehensive Twitter data toshow that preexisting exposure to social media predicts bank stock market losses inthe run period even after controlling for bank characteristics related to run risk (i.e.,mark-to-market losses and uninsured deposits). Moreover, we show that social mediaamplifies these bank run risk factors. During the run period, we find the intensity ofTwitter conversation about a bank predicts stock market losses at the hourly frequency.This effect is stronger for banks with bank run risk factors. At even higher frequency,tweets in the run period with negative sentiment translate into immediate stock marketlosses. These high frequency effects are stronger when tweets are authored by membersof the Twitter startup community (who are likely depositors) and contain keywordsrelated to contagion. These results are consistent with depositors using Twitter tocommunicate in real time during the bank run.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.uni.lu\/fdef-fr\/events\/df-lunch-seminar-social-media-as-a-bank-run-catalyst\/\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"DF Lunch Seminar - Social Media as a Bank Run Catalyst\" \/>\n<meta property=\"og:description\" content=\"Abstract:\u00a0Social media fueled a bank run on Silicon Valley Bank (SVB), and the effects werefelt broadly in the U.S. banking industry. 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