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Arto Thurlin<p>The promises and pitfalls of applying machine learning to asset management<br />A review of the existing <a href="https://sigmoid.social/tags/ML" class="mention hashtag" rel="tag">#<span>ML</span></a> literature from the perspective of a prudent practitioner.<br /><a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4321398" target="_blank" rel="nofollow noopener noreferrer" translate="no"><span class="invisible">https://</span><span class="ellipsis">papers.ssrn.com/sol3/papers.cf</span><span class="invisible">m?abstract_id=4321398</span></a><br /><a href="https://sigmoid.social/tags/QuantPaper" class="mention hashtag" rel="tag">#<span>QuantPaper</span></a> <a href="https://sigmoid.social/tags/AssetManagement" class="mention hashtag" rel="tag">#<span>AssetManagement</span></a></p>
Arto Thurlin<p>&quot;Predicting Stock Price Changes Based on the Limit Order Book: A Survey&quot;<br /><a href="https://sigmoid.social/tags/QuantLink" class="mention hashtag" rel="tag">#<span>QuantLink</span></a> <a href="https://sigmoid.social/tags/QuantPaper" class="mention hashtag" rel="tag">#<span>QuantPaper</span></a> <a href="https://sigmoid.social/tags/Survey" class="mention hashtag" rel="tag">#<span>Survey</span></a> <a href="https://sigmoid.social/tags/LSTM" class="mention hashtag" rel="tag">#<span>LSTM</span></a> <a href="https://sigmoid.social/tags/LOB" class="mention hashtag" rel="tag">#<span>LOB</span></a> <a href="https://sigmoid.social/tags/TimeSeries" class="mention hashtag" rel="tag">#<span>TimeSeries</span></a> <a href="https://sigmoid.social/tags/DeepLearning" class="mention hashtag" rel="tag">#<span>DeepLearning</span></a></p><p>A comprehensive survey of the latest papers in this exciting new field. </p><p><a href="https://www.mdpi.com/2227-7390/10/8/1234/htm" target="_blank" rel="nofollow noopener noreferrer" translate="no"><span class="invisible">https://www.</span><span class="ellipsis">mdpi.com/2227-7390/10/8/1234/h</span><span class="invisible">tm</span></a></p>
Arto Thurlin<p>An implementation of DeepLOB (Zhang, 2018), using the FI-2010 dataset. <br /><a href="https://sigmoid.social/tags/LSTM" class="mention hashtag" rel="tag">#<span>LSTM</span></a> <a href="https://sigmoid.social/tags/LOB" class="mention hashtag" rel="tag">#<span>LOB</span></a></p><p><a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3985631" target="_blank" rel="nofollow noopener noreferrer" translate="no"><span class="invisible">https://</span><span class="ellipsis">papers.ssrn.com/sol3/papers.cf</span><span class="invisible">m?abstract_id=3985631</span></a></p><p><a href="https://sigmoid.social/tags/QuantLink" class="mention hashtag" rel="tag">#<span>QuantLink</span></a> <a href="https://sigmoid.social/tags/QuantPaper" class="mention hashtag" rel="tag">#<span>QuantPaper</span></a> <a href="https://sigmoid.social/tags/PapersWithCode" class="mention hashtag" rel="tag">#<span>PapersWithCode</span></a> <a href="https://sigmoid.social/tags/DeepLearning" class="mention hashtag" rel="tag">#<span>DeepLearning</span></a></p>
Arto Thurlin<p>&quot;Value Premium, Network Adoption, and Factor Pricing ofCrypto Assets&quot;<a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3985631" target="_blank" rel="nofollow noopener noreferrer" translate="no"><span class="invisible">https://</span><span class="ellipsis">papers.ssrn.com/sol3/papers.cf</span><span class="invisible">m?abstract_id=3985631</span></a></p><p>Return momentum in the large-cap group.</p><p><a href="https://sigmoid.social/tags/QuantLink" class="mention hashtag" rel="tag">#<span>QuantLink</span></a> <a href="https://sigmoid.social/tags/QuantPaper" class="mention hashtag" rel="tag">#<span>QuantPaper</span></a><br /><a href="https://sigmoid.social/tags/Blockchain" class="mention hashtag" rel="tag">#<span>Blockchain</span></a>, <a href="https://sigmoid.social/tags/Cryptocurrency" class="mention hashtag" rel="tag">#<span>Cryptocurrency</span></a>, <a href="https://sigmoid.social/tags/DeFi" class="mention hashtag" rel="tag">#<span>DeFi</span></a>, <a href="https://sigmoid.social/tags/FactorModels" class="mention hashtag" rel="tag">#<span>FactorModels</span></a></p>
Arto Thurlin<p><a href="https://sigmoid.social/tags/QuantLink" class="mention hashtag" rel="tag">#<span>QuantLink</span></a> <a href="https://sigmoid.social/tags/QuantPaper" class="mention hashtag" rel="tag">#<span>QuantPaper</span></a> <a href="https://sigmoid.social/tags/PapersWithCode" class="mention hashtag" rel="tag">#<span>PapersWithCode</span></a><br />TFTS (TensorFlow Time Series)<br /><a href="https://github.com/LongxingTan/Time-series-prediction" target="_blank" rel="nofollow noopener noreferrer" translate="no"><span class="invisible">https://</span><span class="ellipsis">github.com/LongxingTan/Time-se</span><span class="invisible">ries-prediction</span></a></p>
Arto Thurlin<p><a href="https://sigmoid.social/tags/QuantLink" class="mention hashtag" rel="tag">#<span>QuantLink</span></a> <a href="https://sigmoid.social/tags/QuantPaper" class="mention hashtag" rel="tag">#<span>QuantPaper</span></a> <a href="https://sigmoid.social/tags/PapersWithCode" class="mention hashtag" rel="tag">#<span>PapersWithCode</span></a><br />High Frequency Trading Framework with Machine/Deep Learning<br /><a href="https://github.com/bradleyboyuyang/ML-HFT" target="_blank" rel="nofollow noopener noreferrer" translate="no"><span class="invisible">https://</span><span class="ellipsis">github.com/bradleyboyuyang/ML-</span><span class="invisible">HFT</span></a><br />Use of Level-2 order book data, <a href="https://sigmoid.social/tags/ML" class="mention hashtag" rel="tag">#<span>ML</span></a> and <a href="https://sigmoid.social/tags/deeplearning" class="mention hashtag" rel="tag">#<span>deeplearning</span></a>.</p>
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