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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Deep Learning for Market by Order Data · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2102.08811/</link><description>Collected HN comments and submissions linking this paper.</description><language>en</language><lastBuildDate>Mon, 02 Nov 2026 11:16:22 GMT</lastBuildDate><atom:link href="https://hackerpaper.kaipfister.de/p/2102.08811/feed.xml" rel="self" type="application/rss+xml" /><item><title>Deep Learning for Market by Order Data — mentioned by Bostonian</title><link>https://news.ycombinator.com/item?id=28551841</link><guid isPermaLink="false">urn:hackerpapers:mention:2102.08811:28551841</guid><pubDate>Thu, 16 Sep 2021 13:56:24 GMT</pubDate><description>&lt;p&gt;I meant to post the arXiv link: https://arxiv.org/abs/2102.08811 . Also published in Applied Mathematical Finance. High-frequency trading firms don&amp;#x27;t disclose their models, but I&amp;#x27;d guess that they look at such things. The ability of the limit order book [LOB] to predict returns has been studied. The LOB, consisting of bid and offer sizes at various price levels, is the result of individual orders, which is what this paper studies. The authors have other machine-learning-in-finance papers on arXiv.&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2102.08811/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>q-fin.TR</category></item></channel></rss>
