{"id":796,"date":"2018-01-24T22:27:02","date_gmt":"2018-01-24T22:27:02","guid":{"rendered":"http:\/\/quantpedia.com\/?p=796"},"modified":"2025-06-04T14:35:35","modified_gmt":"2025-06-04T12:35:35","slug":"are-there-any-simple-calendar-effects-in-bitcoin-market","status":"publish","type":"post","link":"https:\/\/vvv.quantpedia.com\/es\/are-there-any-simple-calendar-effects-in-bitcoin-market\/","title":{"rendered":"Are There Any Simple Calendar Effects in Bitcoin Market?"},"content":{"rendered":"<p>\n\t<strong>Is Bitcoin market efficient ? A new research study analyzes simple calendar effects:<\/strong><\/p>\n<p>\n\t<strong>Autores:<\/strong> Baur, Cahill, Godfrey, Liu<\/p>\n<p>\n\t<strong>T\u00edtulo: <\/strong>Bitcoin Time-of-Day, Day-of-Week and Month-of-Year Effects in Returns and Trading Volume<\/p>\n<p>\n\t<strong>Link:<\/strong> <a href=\"https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3088472\">https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3088472<\/a><\/p>\n<p>\n\t<strong>Abstracto:<\/strong><\/p>\n<p>\n\tThere is a large literature that reports time-specific anomalies in equity markets such as the Monday effect, the January effect and the Halloween effect. This study is the first to report intra-day time-of-day, day-of-week, and month-of-year effects for Bitcoin returns and trading volume. Using more than 15 million price and trading volume observations from seven global Bitcoin exchanges reveal time-varying effects but no consistent or persistent patterns across the sample period. The results suggest that Bitcoin markets are efficient.<\/p>\n<p>\n\t<strong>Fragmentos destacados del art\u00edculo de investigaci\u00f3n acad\u00e9mica:<\/strong><\/p>\n<p>\n\t&quot;The aim of this paper is to analyze market efficiency by testing for time-dependent anomalies in returns and trading volume. For example, do Bitcoin investors trade differently when major stock exchanges are open compared to when they are closed? Do they trade less on weekends and during the Northern hemisphere summer months (i.e., June &#8211; August)? To answer these types of questions, we analyze time-of-the-day (ToD), day-of-the-week (DoW) and month-of-the-year (MoY) patterns in Bitcoin returns and trading volume.<\/p>\n<p>\n\tGiven the conflict between efficient pricing and the seemingly irrational demand for Bitcoin, we look for any consistent patterns in returns that would contradict the efficient market hypothesis. Since Bitcoin is a relatively new and unregulated asset, it is possible that the market has been dominated by retail investors. This suggests that we can expect to find inefficiencies and return anomalies in Bitcoin pricing. The fact that Bitcoin is continuously and globally traded makes an analysis of time-of-day, day-of-week, and month-of-year effects particularly interesting to study. Moreover, since Bitcoin is traded in different currencies and in different geographic locations, the market provides an additional layer of complexity similar to foreign exchange and commodities.<\/p>\n<p>\n\tOur analysis of time-of-day, day-of-week and month-of-year patterns shows evidence of time-specific anomalies such as a lower weekend volume effect and a higher Monday return effect which are more consistent with currency markets.<\/p>\n<p>\n\tWe find increased trading activity on Bitcoin exchanges at times when U.S stock exchanges are open and lower trading activity between midnight and the early morning on most exchanges. Bitcoin exchanges denominated in USD display stronger patterns compared to exchanges denominated in Japanese yen and Chinese yuan. We use heatmaps to illustrate patterns in returns and trading volume both across time and across exchanges.<\/p>\n<p>\n\tThe results support the view that Bitcoin markets are weak-form efficient because we do not see any consistent price pattern that could be exploited based on historical information. We also use statistical tests to check the robustness of the heatmap analysis and to determine the statistical significance of the effects.&quot;<\/p>\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<p class=\"wp-block-paragraph\" id=\"block-854363cc-8450-4dc0-a06a-c737766e9431\"><strong>\u00bfBuscas m\u00e1s estrategias para leer? <a href=\"https:\/\/\\\/\\\/new-fmhwbzh6ghd9hede.swedencentral-01.azurewebsites.net\/sign-up-for-our-newsletter\/\">Suscr\u00edbete a nuestro bolet\u00edn informativo<\/a> o visite nuestra <a href=\"https:\/\/\\\/\\\/new-fmhwbzh6ghd9hede.swedencentral-01.azurewebsites.net\/blog\/\">Blog<\/a> o <a href=\"http:\/\/\\\/\\\/new-fmhwbzh6ghd9hede.swedencentral-01.azurewebsites.net\/Screener\">Evaluador<\/a><\/strong>.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\" id=\"block-65925002-6290-4d3b-b5cd-f3a277851ec8\"><strong>\u00bfQuieres saber m\u00e1s sobre el servicio Quantpedia Premium? 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Consulta nuestra lista de&nbsp;<a href=\"https:\/\/\\\/\\\/new-fmhwbzh6ghd9hede.swedencentral-01.azurewebsites.net\/links-tools\/?category=algo-trading-discounts\">Descuentos en Algo Trading<\/a><\/strong>.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>\u00bfTe gustar\u00eda tener acceso gratuito a? <a href=\"https:\/\/\\\/\\\/new-fmhwbzh6ghd9hede.swedencentral-01.azurewebsites.net\/pricing\/\" title=\"\">nuestros servicios<\/a>? Entonces, <a href=\"https:\/\/lightspeed.com\/lp\/quantpedia-lightspeed-financial-services-group-one-free-year-promotion\" title=\"\">Abre una cuenta con Lightspeed.<\/a> y disfrute de un a\u00f1o de Quantpedia Premium sin costo alguno.<\/strong><\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<p class=\"wp-block-paragraph\" id=\"block-4c45d6c9-c8dd-4283-8743-bf573cfa4d45\"><strong>O s\u00edguenos en:<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\" id=\"block-476e95ed-31a5-4c4d-b701-5203f9fb2e24\"><strong>Facebook <a href=\"https:\/\/www.facebook.com\/groups\/quantstrategies\">Grupo<\/a>, Facebook <a href=\"https:\/\/www.facebook.com\/quantpedia\/\">P\u00e1gina<\/a>, <a href=\"https:\/\/twitter.com\/quantpedia\">Gorjeo<\/a>, <a href=\"https:\/\/www.linkedin.com\/company\/quantpedia\">LinkedIn<\/a>, <a href=\"https:\/\/quantpedia.medium.com\/\">Medio<\/a> o <a href=\"https:\/\/www.youtube.com\/channel\/UC_YubnldxzNjLkIkEoL-FXg\">YouTube<\/a><\/strong><\/p>","protected":false},"excerpt":{"rendered":"<p>\n\t<strong>Is Bitcoin market efficient ? A new research study analyzes simple calendar effects:<\/strong><\/p>\n<p>\n\t<strong>Autores:<\/strong> Baur, Cahill, Godfrey, Liu<\/p>\n<p>\n\t<strong>T\u00edtulo: <\/strong>Bitcoin Time-of-Day, Day-of-Week and Month-of-Year Effects in Returns and Trading Volume<\/p>\n<p>\n\t<strong>Link:<\/strong> <a href=\"https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3088472\">https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3088472<\/a><\/p>\n<p>\n\t<strong>Abstracto:<\/strong><\/p>\n<p>\n\tThere is a large literature that reports time-specific anomalies in equity markets such as the Monday effect, the January effect and the Halloween effect. This study is the first to report intra-day time-of-day, day-of-week, and month-of-year effects for Bitcoin returns and trading volume. Using more than 15 million price and trading volume observations from seven global Bitcoin exchanges reveal time-varying effects but no consistent or persistent patterns across the sample period. The results suggest that Bitcoin markets are efficient.<\/p>\n<p>\n\t<strong>Fragmentos destacados del art\u00edculo de investigaci\u00f3n acad\u00e9mica:<\/strong><\/p>\n<p>\n\t&quot;The aim of this paper is to analyze market efficiency by testing for time-dependent anomalies in returns and trading volume. For example, do Bitcoin investors trade differently when major stock exchanges are open compared to when they are closed? Do they trade less on weekends and during the Northern hemisphere summer months (i.e., June &#8211; August)? To answer these types of questions, we analyze time-of-the-day (ToD), day-of-the-week (DoW) and month-of-the-year (MoY) patterns in Bitcoin returns and trading volume.<\/p>\n<p>\n\tGiven the conflict between efficient pricing and the seemingly irrational demand for Bitcoin, we look for any consistent patterns in returns that would contradict the efficient market hypothesis. Since Bitcoin is a relatively new and unregulated asset, it is possible that the market has been dominated by retail investors. This suggests that we can expect to find inefficiencies and return anomalies in Bitcoin pricing. The fact that Bitcoin is continuously and globally traded makes an analysis of time-of-day, day-of-week, and month-of-year effects particularly interesting to study. Moreover, since Bitcoin is traded in different currencies and in different geographic locations, the market provides an additional layer of complexity similar to foreign exchange and commodities.<\/p>\n<p>\n\tOur analysis of time-of-day, day-of-week and month-of-year patterns shows evidence of time-specific anomalies such as a lower weekend volume effect and a higher Monday return effect which are more consistent with currency markets.<\/p>\n<p>\n\tWe find increased trading activity on Bitcoin exchanges at times when U.S stock exchanges are open and lower trading activity between midnight and the early morning on most exchanges. Bitcoin exchanges denominated in USD display stronger patterns compared to exchanges denominated in Japanese yen and Chinese yuan. We use heatmaps to illustrate patterns in returns and trading volume both across time and across exchanges.<\/p>\n<p>\n\tThe results support the view that Bitcoin markets are weak-form efficient because we do not see any consistent price pattern that could be exploited based on historical information. We also use statistical tests to check the robustness of the heatmap analysis and to determine the statistical significance of the effects.&quot;<\/p>\n<hr \/>\n<p>\n\t<strong>Are you looking for more strategies to read about? Check <a href=\"http:\/\/\\\/\\\/new-fmhwbzh6ghd9hede.swedencentral-01.azurewebsites.net\/Screener\">http:\/\/\\\/\\\/new-fmhwbzh6ghd9hede.swedencentral-01.azurewebsites.net\/Screener<\/a><\/strong><\/p>\n<p>\n\t<strong>Do you want to see performance of trading systems we described? Check<\/strong> <strong><a href=\"http:\/\/\\\/\\\/new-fmhwbzh6ghd9hede.swedencentral-01.azurewebsites.net\/Chart\/Performance\">http:\/\/\\\/\\\/new-fmhwbzh6ghd9hede.swedencentral-01.azurewebsites.net\/Chart\/Performance<\/a><\/strong><\/p>\n<p>\n\t<strong>Do you want to know more about us? Check<\/strong> <strong><a href=\"http:\/\/\\\/\\\/new-fmhwbzh6ghd9hede.swedencentral-01.azurewebsites.net\/Home\/About\">http:\/\/\\\/\\\/new-fmhwbzh6ghd9hede.swedencentral-01.azurewebsites.net\/Home\/About<\/a><\/strong><\/p>","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-796","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts\/796","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/comments?post=796"}],"version-history":[{"count":0,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts\/796\/revisions"}],"wp:attachment":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/media?parent=796"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/categories?post=796"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/tags?post=796"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}