{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,21]],"date-time":"2025-12-21T06:24:02Z","timestamp":1766298242579,"version":"3.40.3"},"publisher-location":"Cham","reference-count":21,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030794620"},{"type":"electronic","value":"9783030794637"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-79463-7_47","type":"book-chapter","created":{"date-parts":[[2021,7,18]],"date-time":"2021-07-18T23:03:07Z","timestamp":1626649387000},"page":"554-563","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A ML-Based Stock Trading Model for\u00a0Profit Predication"],"prefix":"10.1007","author":[{"given":"Jimmy Ming-Tai","family":"Wu","sequence":"first","affiliation":[]},{"given":"Lingyun","family":"Sun","sequence":"additional","affiliation":[]},{"given":"Gautam","family":"Srivastava","sequence":"additional","affiliation":[]},{"given":"Jerry Chun-Wei","family":"Lin","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,7,19]]},"reference":[{"issue":"2","key":"47_CR1","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1016\/S0304-405X(98)00052-X","volume":"51","author":"F Allen","year":"1999","unstructured":"Allen, F., Karjalainen, R.: Using genetic algorithms to find technical trading rules. J. Financ. Econ. 51(2), 245\u2013271 (1999)","journal-title":"J. Financ. Econ."},{"issue":"4","key":"47_CR2","doi-asserted-by":"publisher","first-page":"1645","DOI":"10.1111\/j.1540-6261.2006.00885.x","volume":"61","author":"M Baker","year":"2006","unstructured":"Baker, M., Wurgler, J.: Investor sentiment and the cross-section of stock returns. J. Financ. 61(4), 1645\u20131680 (2006)","journal-title":"J. Financ."},{"issue":"7","key":"47_CR3","doi-asserted-by":"publisher","first-page":"0180944","DOI":"10.1371\/journal.pone.0180944","volume":"12","author":"W Bao","year":"2017","unstructured":"Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. Plos One 12(7), 0180944 (2017)","journal-title":"Plos One"},{"issue":"6","key":"47_CR4","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1002\/for.2585","volume":"38","author":"S Borovkova","year":"2019","unstructured":"Borovkova, S., Tsiamas, I.: An ensemble of lstm neural networks for high-frequency stock market classification. J. Forecast. 38(6), 600\u2013619 (2019)","journal-title":"J. Forecast."},{"issue":"1","key":"47_CR5","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1007\/s10115-019-01353-2","volume":"62","author":"C-H Chen","year":"2019","unstructured":"Chen, C.-H., Lu, C.-Y., Lin, C.-B.: An intelligence approach for group stock portfolio optimization with a trading mechanism. Knowl. Inform. Syst. 62(1), 287\u2013316 (2019). https:\/\/doi.org\/10.1007\/s10115-019-01353-2","journal-title":"Knowl. Inform. Syst."},{"key":"47_CR6","doi-asserted-by":"crossref","unstructured":"Chong, T.T.L., Cao, B., Wong, W.K.: A new principal-component approach to measure the investor sentiment (2014)","DOI":"10.2139\/ssrn.2631910"},{"key":"47_CR7","doi-asserted-by":"publisher","first-page":"883","DOI":"10.1109\/ACCESS.2014.2352261","volume":"2","author":"YH Chou","year":"2014","unstructured":"Chou, Y.H., Kuo, S.Y., Chen, C.Y., Chao, H.C.: A rule-based dynamic decision-making stock trading system based on quantum-inspired tabu search algorithm. IEEE Access 2, 883\u2013896 (2014)","journal-title":"IEEE Access"},{"key":"47_CR8","doi-asserted-by":"crossref","unstructured":"Cowles 3rd, A.: Can stock market forecasters forecast? Econometrica: Journal of the Econometric Society, pp. 309\u2013324 (1933)","DOI":"10.2307\/1907042"},{"key":"47_CR9","doi-asserted-by":"publisher","first-page":"138","DOI":"10.1016\/j.knosys.2017.09.023","volume":"137","author":"H Gunduz","year":"2017","unstructured":"Gunduz, H., Yaslan, Y., Cataltepe, Z.: Intraday prediction of borsa istanbul using convolutional neural networks and feature correlations. Knowl.-Based Syst. 137, 138\u2013148 (2017)","journal-title":"Knowl.-Based Syst."},{"issue":"1","key":"47_CR10","doi-asserted-by":"publisher","first-page":"387","DOI":"10.1093\/rfs\/hhm071","volume":"21","author":"B Han","year":"2008","unstructured":"Han, B.: Investor sentiment and option prices. Rev. Financ. Stud. 21(1), 387\u2013414 (2008)","journal-title":"Rev. Financ. Stud."},{"key":"47_CR11","unstructured":"Hiew, J.Z.G., Huang, X., Mou, H., Li, D., Wu, Q., Xu, Y.: Bert-based financial sentiment index and lstm-based stock return predictability. arXiv preprint arXiv:1906.09024 (2019)"},{"key":"47_CR12","doi-asserted-by":"crossref","unstructured":"Hirabayashi, A., Aranha, C., Iba, H.: Optimization of the trading rule in foreign exchange using genetic algorithm. In: Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation, pp. 1529\u20131536 (2009)","DOI":"10.1145\/1569901.1570106"},{"key":"47_CR13","doi-asserted-by":"crossref","unstructured":"Kim, T., Kim, H.Y.: Forecasting stock prices with a feature fusion lstm-cnn model using different representations of the same data. PloS One 14(2), e0212320 (2019)","DOI":"10.1371\/journal.pone.0212320"},{"issue":"1","key":"47_CR14","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1111\/j.1540-6261.1991.tb03746.x","volume":"46","author":"CM Lee","year":"1991","unstructured":"Lee, C.M., Shleifer, A., Thaler, R.H.: Investor sentiment and the closed-end fund puzzle. J. Finan. 46(1), 75\u2013109 (1991)","journal-title":"J. Finan."},{"key":"47_CR15","unstructured":"Lin, L., Cao, L., Wang, J., Zhang, C.: The applications of genetic algorithms in stock market data mining optimisation. Management Information Systems (2004)"},{"key":"47_CR16","doi-asserted-by":"crossref","unstructured":"Samuelson, P.A.: Lifetime portfolio selection by dynamic stochastic programming. In: Stochastic Optimization Models in Finance, pp. 517\u2013524. Elsevier (1975)","DOI":"10.1016\/B978-0-12-780850-5.50044-7"},{"key":"47_CR17","unstructured":"Schoreels, C., Logan, B., Garibaldi, J.M.: Agent based genetic algorithm employing financial technical analysis for making trading decisions using historical equity market data. In: Proceedings. IEEE\/WIC\/ACM International Conference on Intelligent Agent Technology, 2004. (IAT 2004), pp. 421\u2013424 (2004)"},{"key":"47_CR18","unstructured":"Siripurapu, A.: Convolutional networks for stock trading. Stanford Univ. Dep. Comput. Sci. 1(2), 1\u20136 (2014)"},{"issue":"1","key":"47_CR19","first-page":"48","volume":"1","author":"HH Tsai","year":"2017","unstructured":"Tsai, H.H., Wu, M.E., Wu, W.H.: The information content of implied volatility skew: evidence on Taiwan stock index options. Data Sci. Pattern Recogn. 1(1), 48\u201353 (2017)","journal-title":"Data Sci. Pattern Recogn."},{"key":"47_CR20","doi-asserted-by":"crossref","unstructured":"Wu, J.M.T., Li, Z., Srivastava, G., Tasi, M.H., Lin, J.C.W.: A graph-based convolutional neural network stock price prediction with leading indicators. Pract. Experience Softw. 51(3), 628\u2013644 (2020)","DOI":"10.1002\/spe.2915"},{"issue":"60","key":"47_CR21","doi-asserted-by":"publisher","first-page":"6517","DOI":"10.1080\/00036846.2019.1624920","volume":"51","author":"L Zheng","year":"2019","unstructured":"Zheng, L., Jiang, Y., Long, H.: Exchange rates change, asset-denominated currency difference and stock price fluctuation. Appl. Econ. 51(60), 6517\u20136534 (2019)","journal-title":"Appl. Econ."}],"container-title":["Lecture Notes in Computer Science","Advances and Trends in Artificial Intelligence. From Theory to Practice"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-79463-7_47","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,7,18]],"date-time":"2021-07-18T23:27:50Z","timestamp":1626650870000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-79463-7_47"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030794620","9783030794637"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-79463-7_47","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"19 July 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IEA\/AIE","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kuala Lumpur","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Malaysia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 July 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 July 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"34","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ieaaie2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ieeecomputer.my\/ieaaie2021\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"145","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"87","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"19","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"60% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4.35","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}