{"id":18779,"date":"2026-05-03T19:17:00","date_gmt":"2026-05-03T19:17:00","guid":{"rendered":"https:\/\/finteqc.ca\/?post_type=finteqc_paper&#038;p=18779"},"modified":"2026-05-03T19:23:27","modified_gmt":"2026-05-03T19:23:27","slug":"15954","status":"publish","type":"finteqc_paper","link":"https:\/\/finteqc.ca\/index.php\/papers\/15954\/","title":{"rendered":"Machine Learning for Bitcoin: Insights from Tick-Level Trades and Limit Order Book Microstructure"},"content":{"rendered":"<p>This paper uses machine learning models to study the high frequency trading of Bitcoin. Our out-of-sample empirical tests show that machine learning models, which use tick-level trade and limit order book information, outperform the auto-regressive benchmark models using only past log returns of bitcoin prices. A trading strategy using the sign of the predicted log returns as the buy\/sell signal each period produces a significant higher return than the buy-and-hold strategy of investing in Bitcoin. These findings suggest that microstructure information has predictive content and that machine learning can extract it more effectively than linear benchmarks.<\/p>\n","protected":false},"featured_media":0,"template":"","class_list":["post-18779","finteqc_paper","type-finteqc_paper","status-publish","hentry"],"acf":[],"_links":{"self":[{"href":"https:\/\/finteqc.ca\/index.php\/wp-json\/wp\/v2\/finteqc_paper\/18779","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/finteqc.ca\/index.php\/wp-json\/wp\/v2\/finteqc_paper"}],"about":[{"href":"https:\/\/finteqc.ca\/index.php\/wp-json\/wp\/v2\/types\/finteqc_paper"}],"wp:attachment":[{"href":"https:\/\/finteqc.ca\/index.php\/wp-json\/wp\/v2\/media?parent=18779"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}