{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T15:56:34Z","timestamp":1780502194868,"version":"3.54.1"},"reference-count":40,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,9,22]],"date-time":"2021-09-22T00:00:00Z","timestamp":1632268800000},"content-version":"vor","delay-in-days":264,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003787","name":"Natural Science Foundation of Hebei Province","doi-asserted-by":"publisher","award":["ZD2018236"],"award-info":[{"award-number":["ZD2018236"]}],"id":[{"id":"10.13039\/501100003787","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100010110","name":"Hebei University of Science and Technology","doi-asserted-by":"publisher","award":["2019-ZDB02"],"award-info":[{"award-number":["2019-ZDB02"]}],"id":[{"id":"10.13039\/100010110","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Complexity"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>As the stock market is an important part of the national economy, more and more investors have begun to pay attention to the methods to improve the return on investment and effectively avoid certain risks. Many factors affect the trend of the stock market, and the relevant information has the nature of time series. This paper proposes a composite model CNN\u2010BiSLSTM to predict the closing price of the stock. Bidirectional special long short\u2010term memory (BiSLSTM) improved on bidirectional long short\u2010term memory (BiLSTM) adds 1\u2009\u2212\u2009tanh(<jats:italic>x<\/jats:italic>) function in the output gate which makes the model better predict the stock price. The model extracts advanced features that influence stock price through convolutional neural network (CNN), and predicts the stock closing price through BiSLSTM after the data processed by CNN. To verify the effectiveness of the model, the historical data of the Shenzhen Component Index from July 1, 1991, to October 30, 2020, are used to train and test the CNN\u2010BiSLSTM. CNN\u2010BiSLSTM is compared with multilayer perceptron (MLP), recurrent neural network (RNN), long short\u2010term memory (LSTM), BiLSTM, CNN\u2010LSTM, and CNN\u2010BiLSTM. The experimental results show that the mean absolute error (MAE), root\u2010mean\u2010squared error (RMSE), and R\u2010square (<jats:italic>R<\/jats:italic><jats:sup>2<\/jats:sup>) evaluation indicators of the CNN\u2010BiSLSTM are all optimal. Therefore, CNN\u2010BiSLSTM can accurately predict the closing price of the Shenzhen Component Index of the next trading day, which can be used as a reference for the majority of investors to effectively avoid certain risks.<\/jats:p>","DOI":"10.1155\/2021\/5360828","type":"journal-article","created":{"date-parts":[[2021,9,22]],"date-time":"2021-09-22T17:38:39Z","timestamp":1632332319000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":55,"title":["A Stock Closing Price Prediction Model Based on CNN\u2010BiSLSTM"],"prefix":"10.1155","volume":"2021","author":[{"given":"Haiyao","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianxuan","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lihui","family":"Cao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yifan","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiuhong","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3829-6540","authenticated-orcid":false,"given":"Jingyang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2021,9,22]]},"reference":[{"key":"e_1_2_10_1_2","doi-asserted-by":"publisher","DOI":"10.1109\/tim.2017.2650718"},{"key":"e_1_2_10_2_2","doi-asserted-by":"publisher","DOI":"10.1155\/2020\/7176598"},{"key":"e_1_2_10_3_2","doi-asserted-by":"publisher","DOI":"10.1007\/s40009-019-00859-1"},{"key":"e_1_2_10_4_2","doi-asserted-by":"crossref","unstructured":"CuiX. ShangW. andJiangF. Stock index forecasting by hidden Markov models with trends recognition Proceedings of the 2019 IEEE International Conference on Big Data (Big Data) 2019 Los Angeles CA USA https:\/\/doi.org\/10.1109\/bigdata47090.2019.9006068.","DOI":"10.1109\/BigData47090.2019.9006068"},{"key":"e_1_2_10_5_2","doi-asserted-by":"publisher","DOI":"10.1111\/jofi.12364"},{"key":"e_1_2_10_6_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-019-04212-x"},{"key":"e_1_2_10_7_2","doi-asserted-by":"publisher","DOI":"10.3233\/AF-170176"},{"key":"e_1_2_10_8_2","doi-asserted-by":"crossref","unstructured":"ShahD. CampbellW. andZulkernineF. A comparative study of LSTM and DNN for stock market forecasting Proceedings of the IEEE International Conference on Big Data 2018 Seattle WA USA IEEE https:\/\/doi.org\/10.1109\/bigdata.2018.8622462 2-s2.0-85062644306.","DOI":"10.1109\/BigData.2018.8622462"},{"key":"e_1_2_10_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.114332"},{"key":"e_1_2_10_10_2","unstructured":"KusumaR. HoT. KaoW. OuY.-Y. andHuaK.-L. Using deep learning neural networks and candlestick chart representation to predict stock market 2019 https:\/\/arxiv.org\/abs\/1903.12258."},{"key":"e_1_2_10_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2017.12.025"},{"key":"e_1_2_10_12_2","doi-asserted-by":"crossref","unstructured":"NelsonD. PereiraA. andOliveiraR. Stock market\u2019s price movement prediction with LSTM neural networks Proceedings of the 2017 International Joint Conference on Neural Networks (IJCNN) 2017 Anchorage AK USA IEEE https:\/\/doi.org\/10.1109\/ijcnn.2017.7966019 2-s2.0-85031022947.","DOI":"10.1109\/IJCNN.2017.7966019"},{"key":"e_1_2_10_13_2","doi-asserted-by":"crossref","unstructured":"NairB. DhariniN. andMohandasV. A stock market trend prediction system using a hybrid decision tree-neuro-fuzzy system Proceedings of the 2010 International Conference on Advances in Recent Technologies in Communication and Computing (ARTCom) 2010 Kottayam India IEEE https:\/\/doi.org\/10.1109\/artcom.2010.75 2-s2.0-78651448991.","DOI":"10.1109\/ARTCom.2010.75"},{"key":"e_1_2_10_14_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2016.07.024"},{"key":"e_1_2_10_15_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2019.03.029"},{"key":"e_1_2_10_16_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2014.01.008"},{"key":"e_1_2_10_17_2","doi-asserted-by":"crossref","unstructured":"HuangB. DingQ. SunG. andLiH. Stock prediction based on Bayesian-LSTM Proceedings of the International Conference on Machine Learning and Computing 2018 Zhuhai China 128\u2013133 https:\/\/doi.org\/10.1145\/3195106.3195170 2-s2.0-85048333108.","DOI":"10.1145\/3195106.3195170"},{"key":"e_1_2_10_18_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2017.09.023"},{"key":"e_1_2_10_19_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2019.01.012"},{"key":"e_1_2_10_20_2","doi-asserted-by":"publisher","DOI":"10.1109\/access.2019.2901842"},{"key":"e_1_2_10_21_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-019-04504-2"},{"key":"e_1_2_10_22_2","doi-asserted-by":"crossref","unstructured":"MehtabS.andSenJ. Stock price prediction using CNN and LSTM-based deep learning models 2020 https:\/\/arxiv.org\/abs\/2010.13891.","DOI":"10.1109\/DASA51403.2020.9317207"},{"key":"e_1_2_10_23_2","article-title":"Deep learning with long short-term memory networks for financial market predictions","volume":"270","author":"Fischer T.","year":"2017","journal-title":"European Journal of Operational Research"},{"key":"e_1_2_10_24_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2018.07.019"},{"key":"e_1_2_10_25_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2020.03.049"},{"key":"e_1_2_10_26_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-020-05535-w"},{"key":"e_1_2_10_27_2","doi-asserted-by":"crossref","unstructured":"HasanI. SettiF. TsesmelisT. BueA. D. GalassoF. andCristaniM. MXLSTM: mixing tracklets and vislets to jointly forecast trajectories and head poses Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition 2018 Salt Lake City UT USA 6067\u20136076 https:\/\/doi.org\/10.1109\/cvpr.2018.00635 2-s2.0-85062825158.","DOI":"10.1109\/CVPR.2018.00635"},{"key":"e_1_2_10_28_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2019.01.256"},{"key":"e_1_2_10_29_2","doi-asserted-by":"crossref","unstructured":"AkitaR. YoshiharaA. MatsubaraT. andUeharaK. Deep learning for stock prediction using numerical and textual information Proceedings of the 2016 IEEE\/ACIS 15th International Conference on Computer and Information Science (ICIS) 2016 Okayama Japan IEEE 945\u2013950 https:\/\/doi.org\/10.1109\/icis.2016.7550882 2-s2.0-84987989524.","DOI":"10.1109\/ICIS.2016.7550882"},{"key":"e_1_2_10_30_2","doi-asserted-by":"publisher","DOI":"10.1155\/2019\/4324878"},{"key":"e_1_2_10_31_2","doi-asserted-by":"publisher","DOI":"10.1155\/2020\/6431712"},{"key":"e_1_2_10_32_2","doi-asserted-by":"publisher","DOI":"10.1155\/2020\/6622927"},{"key":"e_1_2_10_33_2","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"e_1_2_10_34_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.tca.2018.08.024"},{"key":"e_1_2_10_35_2","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_2_10_36_2","doi-asserted-by":"publisher","DOI":"10.1162\/089976600300015015"},{"key":"e_1_2_10_37_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-24797-2"},{"key":"e_1_2_10_38_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.enconman.2012.11.025"},{"key":"e_1_2_10_39_2","doi-asserted-by":"publisher","DOI":"10.1155\/2020\/4754025"},{"key":"e_1_2_10_40_2","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/5511802"}],"container-title":["Complexity"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/complexity\/2021\/5360828.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/complexity\/2021\/5360828.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/2021\/5360828","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,9]],"date-time":"2024-08-09T22:13:49Z","timestamp":1723241629000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1155\/2021\/5360828"}},"subtitle":[],"editor":[{"given":"Kai","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2021,1]]},"references-count":40,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,1]]}},"alternative-id":["10.1155\/2021\/5360828"],"URL":"https:\/\/doi.org\/10.1155\/2021\/5360828","archive":["Portico"],"relation":{},"ISSN":["1076-2787","1099-0526"],"issn-type":[{"value":"1076-2787","type":"print"},{"value":"1099-0526","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1]]},"assertion":[{"value":"2021-08-12","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-09-11","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-09-22","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"5360828"}}