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Deep Stock Representation Learning: From Candlestick Charts to Investment Decisions [article]

Guosheng Hu and Yuxin Hu and Kai Yang and Zehao Yu and Flood Sung and Zhihong Zhang and Fei Xie and Jianguo Liu and Neil Robertson and Timothy Hospedales and Qiangwei Miemie
<span title="2018-02-18">2018</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We propose a novel investment decision strategy (IDS) based on deep learning. The performance of many IDSs is affected by stock similarity.  ...  To solve these problems, we apply Convolutional AutoEncoder to learn a stock representation, based on which we propose a novel portfolio construction strategy by: (i) using the deeply learned representation  ...  Conclusions We propose a deep learned-based investment strategy, which includes: (1) novel stock representation learning by deep CAE encoding of candlestick charts, (2) diversification through modularity  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1709.03803v3">arXiv:1709.03803v3</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ehrfmdfubnhlpopxzpz4zzptie">fatcat:ehrfmdfubnhlpopxzpz4zzptie</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200901061038/https://arxiv.org/pdf/1709.03803v3.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/1e/c0/1ec0e6e71424c8c296c280a753177881140beb06.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1709.03803v3" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Deep Stock Representation Learning: From Candlestick Charts to Investment Decisions

Guosheng Hu, Yuxin Hu, Kai Yang, Zehao Yu, Flood Sung, Zhihong Zhang, Fei Xie, Jianguo Liu, Neil Robertson, Timpathy Hospedales, Qiangwei Miemie
<span title="">2018</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/rc5jnc4ldvhs3dswicq5wk3vsq" style="color: black;">2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)</a> </i> &nbsp;
We propose a novel investment decision strategy (IDS) based on deep learning. The performance of many IDSs is affected by stock similarity.  ...  To solve these problems, we apply Convolutional AutoEncoder to learn a stock representation, based on which we propose a novel portfolio construction strategy by: (i) using the deeply learned representation  ...  Conclusions We propose a deep learned-based investment strategy, which includes: (1) novel stock representation learning by deep CAE encoding of candlestick charts, (2) diversification through modularity  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/icassp.2018.8462215">doi:10.1109/icassp.2018.8462215</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/icassp/HuHYYSZXLRHM18.html">dblp:conf/icassp/HuHYYSZXLRHM18</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/bpw7h7abbnacde4op7d2ulyjiu">fatcat:bpw7h7abbnacde4op7d2ulyjiu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190426121212/https://www.pure.ed.ac.uk/ws/files/55717123/Deep_Stock_Representation_Learning.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/d6/09/d6096fb5a14b2d6f79fb87ef428f2d72b0cb1b38.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/icassp.2018.8462215"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

Using Deep Learning Neural Networks and Candlestick Chart Representation to Predict Stock Market [article]

Rosdyana Mangir Irawan Kusuma, Trang-Thi Ho, Wei-Chun Kao, Yu-Yen Ou, Kai-Lung Hua
<span title="2019-02-26">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
From stock market historical data, we converted it to candlestick charts. Finally, these candlestick charts will be feed as input for training a Convolutional Neural Network model.  ...  The constructed model have been implemented as a web-based system freely available at http://140.138.155.216/deepcandle/ for predicting stock market using candlestick chart and deep learning neural networks  ...  While, (Hu, Hu et al. 2017 ) used the candlestick chart to build a decision-making system in stock market investment.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1903.12258v1">arXiv:1903.12258v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/wx52c4gyqbgqlca4r6h5uornnu">fatcat:wx52c4gyqbgqlca4r6h5uornnu</a> </span>
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Stock2Vec: A Hybrid Deep Learning Framework for Stock Market Prediction with Representation Learning and Temporal Convolutional Network [article]

Xing Wang, Yijun Wang, Bin Weng, Aleksandr Vinel
<span title="2020-09-29">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We have proposed to develop a global hybrid deep learning framework to predict the daily prices in the stock market.  ...  With representation learning, we derived an embedding called Stock2Vec, which gives us insight for the relationship among different stocks, while the temporal convolutional layers are used for automatically  ...  Alternatively, [40] directly used the candlestick chart graphs as inputs to determine the Buy, Hold and Sell behavior as a classification task, while in [41] , the bar chart images were fed into CNN  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2010.01197v1">arXiv:2010.01197v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/qub27gaeabez3bzooraoddzji4">fatcat:qub27gaeabez3bzooraoddzji4</a> </span>
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Portfolio Learning Based on Deep Learning

Wei Pan, Jide Li, Xiaoqiang Li
<span title="2020-11-18">2020</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/hijy7jexkvcipg3tulqv73bck4" style="color: black;">Future Internet</a> </i> &nbsp;
Specifically, this method is based on the similarity of deep features extracted from candlestick charts.  ...  In this paper, we propose a novel portfolio learning approach based on deep learning and apply it to China's stock market.  ...  from raw time series rather than candlestick charts for investment decision-making.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/fi12110202">doi:10.3390/fi12110202</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/wr63if7zqjbppaoyenek3xflxm">fatcat:wr63if7zqjbppaoyenek3xflxm</a> </span>
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Learning Financial Asset-Specific Trading Rules via Deep Reinforcement Learning [article]

Mehran Taghian, Ahmad Asadi, Reza Safabakhsh
<span title="2020-10-27">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Recently, various deep reinforcement learning (DRL) methods are employed to learn the new trading rules for each asset.  ...  However, these kind of trading strategies are profitable, extracting new asset-specific trading rules from vast historical data to increase total return and decrease the risk of portfolios is difficult  ...  [32] proposed a novel investment decision strategy using convolutional autoencoder learning stock representation from candlestick charts. Thammakesorn et al.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2010.14194v1">arXiv:2010.14194v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ro5kerngqzhytdxqziikygvwfq">fatcat:ro5kerngqzhytdxqziikygvwfq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201031134957/https://arxiv.org/pdf/2010.14194v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/35/fb/35fb065781c47ebbd7870578e37d675567ca522f.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2010.14194v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Stock Trading Strategies Based on Deep Reinforcement Learning

Yawei Li, Peipei Liu, Ze Wang, Cristian Mateos
<span title="2022-03-01">2022</span> <i title="Hindawi Limited"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/fw4azkpu65d2thmrwfkoawyxse" style="color: black;">Scientific Programming</a> </i> &nbsp;
To solve these problems, this study proposes a new deep reinforcement learning model to implement stock trading, analyzes the stock market through stock data, technical indicators and candlestick charts  ...  The purpose of stock market investment is to obtain more profits. In recent years, an increasing number of researchers have tried to implement stock trading based on machine learning.  ...  market from stock data, technical indicators, and candlestick charts and fuse the features of the different data sources to obtain stock market state features representation and help the agent learn the  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2022/4698656">doi:10.1155/2022/4698656</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/2eoyy2qytjexrkc4roazvs4pbq">fatcat:2eoyy2qytjexrkc4roazvs4pbq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220426081953/https://downloads.hindawi.com/journals/sp/2022/4698656.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/95/b9/95b9d7b5e44fb9cc2a3c70099c53d20a0a76c83d.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2022/4698656"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> hindawi.com </button> </a>

A Reinforcement Learning Based Encoder-Decoder Framework for Learning Stock Trading Rules [article]

Mehran Taghian, Ahmad Asadi, Reza Safabakhsh
<span title="2021-01-08">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
A wide variety of deep reinforcement learning (DRL) models have recently been proposed to learn profitable investment strategies.  ...  In this paper, a novel end-to-end model based on the neural encoder-decoder framework combined with DRL is proposed to learn single instrument trading strategies from a long sequence of raw prices of the  ...  The encoder part is a deep neural network applied to extract deep features from the candlestick chart representations. These features are categorized into the following two groups: 1.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2101.03867v1">arXiv:2101.03867v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/pohr6kvczbc7pm5b3cn7d6rj7a">fatcat:pohr6kvczbc7pm5b3cn7d6rj7a</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210115105458/https://arxiv.org/pdf/2101.03867v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/04/20/042011125edfab7cd275e2035a4edb534bc9b136.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2101.03867v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Improving stock trading decisions based on pattern recognition using machine learning technology

Yaohu Lin, Shancun Liu, Haijun Yang, Harris Wu, Bingbing Jiang, Stefan Cristian Gherghina
<span title="2021-08-06">2021</span> <i title="Public Library of Science (PLoS)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/s3gm7274mfe6fcs7e3jterqlri" style="color: black;">PLoS ONE</a> </i> &nbsp;
PRML, a novel candlestick pattern recognition model using machine learning methods, is proposed to improve stock trading decisions.  ...  An investment strategy is constructed according to the identified candlestick patterns and suitable time window.  ...  Acknowledgments We are truly grateful to an anonymous referee whose comments vastly improved this paper. Author Contributions Conceptualization: Yaohu Lin, Haijun Yang.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1371/journal.pone.0255558">doi:10.1371/journal.pone.0255558</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/34358269">pmid:34358269</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC8345893/">pmcid:PMC8345893</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/6khcc4uqv5azrhxhnexozv6yla">fatcat:6khcc4uqv5azrhxhnexozv6yla</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210812205420/https://storage.googleapis.com/plos-corpus-prod/10.1371/journal.pone.0255558/1/pone.0255558.pdf?X-Goog-Algorithm=GOOG4-RSA-SHA256&amp;X-Goog-Credential=wombat-sa%40plos-prod.iam.gserviceaccount.com%2F20210812%2Fauto%2Fstorage%2Fgoog4_request&amp;X-Goog-Date=20210812T205420Z&amp;X-Goog-Expires=86400&amp;X-Goog-SignedHeaders=host&amp;X-Goog-Signature=4c323296bc506b58e6385bdc45b0094460c234d8af8d940d37a9eb613c00f7a2e0850756c770988509408b7434be71e02bd127d26520b672a4496ed3837e7d5da0930d04e9586294536381cf1585a75ca11ca6f695ea4b2011f2251507e0ba8b9af44e8ffac2d5ff3aea7e1b977765c3af8b000f32f662054da851ea465b10ba32b1b2886553ff34eae9856cff229588ee1247da7aa377d0a09214ee40a94af0423cc91cd1dc0a05eb9fb2eb14d89d9e68a4a08e2342fd11cc272b538abe2de4a2fa3354579389da827245677844ce87b89deeb1b39036f220ac0194cafa2e47e27102abfe5279763dca8ad98801909c4d7a07e23f0159d1a3c8df8e4ca38066" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/ae/37/ae37b1292fc31f241894ca82ca59933e8a883544.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1371/journal.pone.0255558"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> plos.org </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8345893" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

YOLO Object Recognition Algorithm and "Buy-Sell Decision" Model over 2D Candlestick Charts

Serdar Birogul, Gunay Temur, Utku Kose
<span title="">2020</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
INDEX TERMS YOLO, object detection and classification, decision support systems, deep learning, finance, trend decision.  ...  The model is trained by state-of-the-art, real-time object detection system (You Only Look Once) YOLO; for the training, one-year candlestick charts belonging to the stocks traded on Borsa İstanbul (BIST  ...  APPENDIX The authors would like to thank BIST for providing past stock price data used in the study free of charge.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2020.2994282">doi:10.1109/access.2020.2994282</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/2jcqapiry5bgrhwfg3m7ikcpyi">fatcat:2jcqapiry5bgrhwfg3m7ikcpyi</a> </span>
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Stock Price Movement Prediction Using Sentiment Analysis and CandleStick Chart Representation

Trang-Thi Ho, Yennun Huang
<span title="2021-11-29">2021</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/taedaf6aozg7vitz5dpgkojane" style="color: black;">Sensors</a> </i> &nbsp;
People can predict the price movement of stocks by applying machine learning algorithms on information contained in historical data, stock candlestick-chart data, and social-media data.  ...  Finally, we integrated the stock's sentiment features and its candlestick chart to predict the stock price movement over 4-, 6-, 8-, and 10-day time periods.  ...  In 2017, Hu employed a convolutional encoder to learn the candlestick chart patterns to build a decision-making system for the stock market.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/s21237957">doi:10.3390/s21237957</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/34883961">pmid:34883961</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/gnyf6iisvfblhirym6cfho33py">fatcat:gnyf6iisvfblhirym6cfho33py</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220424213123/https://mdpi-res.com/d_attachment/sensors/sensors-21-07957/article_deploy/sensors-21-07957-v2.pdf?version=1638846186" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/93/36/93369b7a6e60b5c8ec2e09dc9ee2021f8b6b415d.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/s21237957"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> mdpi.com </button> </a>

DPP: Deep predictor for price movement from candlestick charts

Chih-Chieh Hung, Ying-Ju Chen, J E. Trinidad Segovia
<span title="2021-06-21">2021</span> <i title="Public Library of Science (PLoS)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/s3gm7274mfe6fcs7e3jterqlri" style="color: black;">PLoS ONE</a> </i> &nbsp;
price movements from a collection of sub-chart representations.  ...  This framework comprises three steps: 1. decomposing a given candlestick chart into sub-charts; 2. using CNN-autoencoder to acquire the best representation of sub-charts; 3. applying RNN to predict the  ...  The encoding and decoding procedures of CAE aim to obtain the best representation of the input-charts by recovering these sub-charts from the output images obtained from deep features.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1371/journal.pone.0252404">doi:10.1371/journal.pone.0252404</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/34153042">pmid:34153042</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/44mdimvaqjfkhp7pltllnognve">fatcat:44mdimvaqjfkhp7pltllnognve</a> </span>
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A Modified Deep Learning Enthused Adversarial Network Model to Predict Financial Fluctuations in Stock Market

<span title="2019-08-30">2019</span> <i title="Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - BEIESP"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/h673cvfolnhl3mnbjxkhtxdtg4" style="color: black;">International Journal of Engineering and Advanced Technology</a> </i> &nbsp;
Predicting financial fluctuations in the real-time stock market is considered to be a major problem due to dynamic changes in financial data.  ...  The main objective of this model is to acquire data from online financial sites and to process the obtained information using adversarial network to generate predictions.  ...  Rosdyana Mangir Irawan Kusuma [6] developed a decision support framework based on the deep neural network based solution along with candlestick charts for the traders to predict and suggest the future  ... 
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Integrating Heuristics and Learning in a Computational Architecture for Cognitive Trading [article]

Remo Pareschi, Federico Zappone
<span title="2021-08-27">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
ways: heuristics and learning.  ...  The successes of Artificial Intelligence in recent years in areas such as image analysis, natural language understanding and strategy games have sparked interest from the world of finance.  ...  This terminology stems from their graphical representation through price changes indicated by Japanese candlesticks which contain more information than simple line charts.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2108.12333v1">arXiv:2108.12333v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/msts3yi2tbgbherpoe72w3mgze">fatcat:msts3yi2tbgbherpoe72w3mgze</a> </span>
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Deep Learning for Financial Applications : A Survey [article]

Ahmet Murat Ozbayoglu, Mehmet Ugur Gudelek, Omer Berat Sezer
<span title="2020-02-09">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Meanwhile, within the Machine Learning (ML) field, Deep Learning (DL) started getting a lot of attention recently, mostly due to its outperformance over the classical models.  ...  In this paper, we tried to provide a state-of-the-art snapshot of the developed DL models for financial applications, as of today.  ...  Belief Network. 4, 8, 15-17, 20, 27, 30, 31 DCNL Deep Co-investment Network Learning. 14 DCNN Deep Convolutional Neural Network. 15 DDPG Deep Deterministic Policy Gradient. 20 Deep-FASP The Financial  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2002.05786v1">arXiv:2002.05786v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/p4ykvxempzajpo66p2z6xaddp4">fatcat:p4ykvxempzajpo66p2z6xaddp4</a> </span>
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