<?xml version="1.0" encoding="UTF-8"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Using Recurrent Neural Networks To Forecasting of Forex · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/cond-mat_0304469/</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/cond-mat_0304469/feed.xml" rel="self" type="application/rss+xml" /><item><title>Using Recurrent Neural Networks To Forecasting of Forex — mentioned by dsacco</title><link>https://news.ycombinator.com/item?id=12457642</link><guid isPermaLink="false">urn:hackerpapers:mention:cond-mat_0304469:12457642</guid><pubDate>Thu, 08 Sep 2016 21:47:29 GMT</pubDate><description>&lt;p&gt;To your first question: yes, for some definitions of &amp;quot;successfully&amp;quot;[1][2][3][4]. It&amp;#x27;s difficult, and it isn&amp;#x27;t as though you train your algorithm and watch it make money. It&amp;#x27;s more that some functions of data analysis become incrementally easier or superior. It&amp;#x27;s fundamentally similar to applying RNNs to any other signal processing problem. To your (implied) second question: tech companies like Google, Facebook and Microsoft have a strong incentive to publish their researchers&amp;#x27; work. It&amp;#x27;s good for recruiting and pushing progress forward overall. Financial companies have a strong incentive not…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/cond-mat_0304469/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cond-mat.dis-nn</category></item></channel></rss>
