{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T18:02:34Z","timestamp":1750356154530,"version":"3.40.3"},"publisher-location":"Cham","reference-count":38,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031264214"},{"type":"electronic","value":"9783031264221"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-26422-1_10","type":"book-chapter","created":{"date-parts":[[2023,3,27]],"date-time":"2023-03-27T01:08:37Z","timestamp":1679879317000},"page":"154-169","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Uncertainty Awareness for\u00a0Predicting Noisy Stock Price Movements"],"prefix":"10.1007","author":[{"given":"Yun-Hsuan","family":"Lien","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu-Syuan","family":"Lin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu-Shuen","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,3,18]]},"reference":[{"key":"10_CR1","doi-asserted-by":"crossref","unstructured":"Adebiyi, A.A., Adewumi, A.O., Ayo, C.K.: Comparison of Arima and artificial neural networks models for stock price prediction. J. Appl. Math. 2014, 1\u20137 (2014)","DOI":"10.1155\/2014\/614342"},{"key":"10_CR2","unstructured":"Antor\u00e1n, J., Allingham, J., Hern\u00e1ndez-Lobato, J.M.: Depth uncertainty in neural networks. In: Advances in Neural Information Processing Systems, vol. 33 (2020)"},{"key":"10_CR3","unstructured":"Arpit, D., et al.: A closer look at memorization in deep networks. In: International Conference on Machine Learning, pp. 233\u2013242 (2017)"},{"key":"10_CR4","doi-asserted-by":"crossref","unstructured":"Ding, X., Zhang, Y., Liu, T., Duan, J.: Using structured events to predict stock price movement: an empirical investigation. In: Conference on Empirical Methods in Natural Language Processing, pp. 1415\u20131425 (2014)","DOI":"10.3115\/v1\/D14-1148"},{"issue":"3\u20134","key":"10_CR5","doi-asserted-by":"publisher","first-page":"67","DOI":"10.3233\/AF-170176","volume":"6","author":"M Dixon","year":"2017","unstructured":"Dixon, M., Klabjan, D., Bang, J.H.: Classification-based financial markets prediction using deep neural networks. Algorithmic Finan. 6(3\u20134), 67\u201377 (2017)","journal-title":"Algorithmic Finan."},{"key":"10_CR6","doi-asserted-by":"crossref","unstructured":"Edwards, R.D., Magee, J., Bassetti, W.C.: Technical Analysis of Stock Trends. CRC Press (2018)","DOI":"10.4324\/9781315115719"},{"key":"10_CR7","doi-asserted-by":"crossref","unstructured":"Feng, F., Chen, H., He, X., Ding, J., Sun, M., Chua, T.S.: Enhancing stock movement prediction with adversarial training. In: International Joint Conference on Artificial Intelligence, pp. 5843\u20135849 (2019)","DOI":"10.24963\/ijcai.2019\/810"},{"issue":"2","key":"10_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3309547","volume":"37","author":"F Feng","year":"2019","unstructured":"Feng, F., He, X., Wang, X., Luo, C., Liu, Y., Chua, T.S.: Temporal relational ranking for stock prediction. ACM Trans. Inf. Syst. 37(2), 1\u201330 (2019)","journal-title":"ACM Trans. Inf. Syst."},{"key":"10_CR9","unstructured":"Foong, A.Y., Burt, D.R., Li, Y., Turner, R.E.: Pathologies of factorised Gaussian and MC dropout posteriors in Bayesian neural networks. STAT 1050, 2 (2019)"},{"key":"10_CR10","unstructured":"Gal, Y., Ghahramani, Z.: Dropout as a Bayesian approximation: representing model uncertainty in deep learning. In: International Conference on Machine Learning, pp. 1050\u20131059 (2016)"},{"key":"10_CR11","unstructured":"Goodfellow, I., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. In: International Conference on Learning Representations (2015). https:\/\/arxiv.org\/abs\/1412.6572"},{"key":"10_CR12","unstructured":"Han, B., et al.: Co-teaching: robust training of deep neural networks with extremely noisy labels. In: Advances in Neural Information Processing Systems, pp. 8527\u20138537 (2018)"},{"issue":"1","key":"10_CR13","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1002\/asmb.2209","volume":"33","author":"JB Heaton","year":"2017","unstructured":"Heaton, J.B., Polson, N.G., Witte, J.H.: Deep learning for finance: deep portfolios. Appl. Stoch. Model. Bus. Ind. 33(1), 3\u201312 (2017)","journal-title":"Appl. Stoch. Model. Bus. Ind."},{"key":"10_CR14","unstructured":"Izmailov, P., Maddox, W.J., Kirichenko, P., Garipov, T., Vetrov, D., Wilson, A.G.: Subspace inference for Bayesian deep learning. In: Uncertainty in Artificial Intelligence, pp. 1169\u20131179 (2020)"},{"key":"10_CR15","unstructured":"Jiang, L., Zhou, Z., Leung, T., Li, L.J., Fei-Fei, L.: MentorNet: learning data-driven curriculum for very deep neural networks on corrupted labels. In: International Conference on Machine Learning (2018)"},{"key":"10_CR16","unstructured":"Kurakin, A., Goodfellow, J.I., Bengio, S.: Adversarial machine learning at scale. In: International Conference on Learning Representations (2017)"},{"key":"10_CR17","unstructured":"Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Advances in Neural Information Processing Systems, pp. 6402\u20136413 (2017)"},{"key":"10_CR18","unstructured":"Li, B., Hoi, S.C.: On-line portfolio selection with moving average reversion. arXiv preprint arXiv:1206.4626 (2012)"},{"key":"10_CR19","unstructured":"Li, M., Soltanolkotabi, M., Oymak, S.: Gradient descent with early stopping is provably robust to label noise for overparameterized neural networks. In: International Conference on Artificial Intelligence and Statistics (2020)"},{"key":"10_CR20","unstructured":"Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101 (2017)"},{"key":"10_CR21","unstructured":"Maddox, W., Garipov, T., Izmailov, P., Vetrov, D., Wilson, A.G.: A simple baseline for Bayesian uncertainty in deep learning. arXiv preprint arXiv:1902.02476 (2019)"},{"key":"10_CR22","unstructured":"Malach, E., Shalev-Shwartz, S.: Decoupling \u201cwhen to update\u201d from \u201chow to update\u201d. In: Advances in Neural Information Processing Systems, pp. 960\u2013970 (2017)"},{"issue":"2","key":"10_CR23","doi-asserted-by":"publisher","first-page":"228","DOI":"10.1016\/j.jfineco.2011.11.003","volume":"104","author":"TJ Moskowitz","year":"2012","unstructured":"Moskowitz, T.J., Ooi, Y.H., Pedersen, L.H.: Time series momentum. J. Financ. Econ. 104(2), 228\u2013250 (2012)","journal-title":"J. Financ. Econ."},{"key":"10_CR24","doi-asserted-by":"crossref","unstructured":"Nelson, D.M., Pereira, A.C., de Oliveira, R.A.: Stock market\u2019s price movement prediction with LSTM neural networks. In: International Joint Conference on Neural Networks, pp. 1419\u20131426 (2017)","DOI":"10.1109\/IJCNN.2017.7966019"},{"key":"10_CR25","doi-asserted-by":"crossref","unstructured":"Nguyen, A., Yosinski, J., Clune, J.: Deep neural networks are easily fooled: high confidence predictions for unrecognizable images. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 427\u2013436 (2015)","DOI":"10.1109\/CVPR.2015.7298640"},{"issue":"24","key":"10_CR26","doi-asserted-by":"publisher","first-page":"9603","DOI":"10.1016\/j.eswa.2015.07.052","volume":"42","author":"TH Nguyen","year":"2015","unstructured":"Nguyen, T.H., Shirai, K., Velcin, J.: Sentiment analysis on social media for stock movement prediction. Expert Syst. Appl. 42(24), 9603\u20139611 (2015)","journal-title":"Expert Syst. Appl."},{"issue":"1","key":"10_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/2251-712X-9-1","volume":"9","author":"STA Niaki","year":"2013","unstructured":"Niaki, S.T.A., Hoseinzade, S.: Forecasting s &p 500 index using artificial neural networks and design of experiments. J. Ind. Eng. Int. 9(1), 1 (2013)","journal-title":"J. Ind. Eng. Int."},{"issue":"8","key":"10_CR28","doi-asserted-by":"publisher","first-page":"852","DOI":"10.1002\/for.2543","volume":"37","author":"A Ntakaris","year":"2018","unstructured":"Ntakaris, A., Magris, M., Kanniainen, J., Gabbouj, M., Iosifidis, A.: Benchmark dataset for mid-price forecasting of limit order book data with machine learning methods. J. Forecast. 37(8), 852\u2013866 (2018)","journal-title":"J. Forecast."},{"key":"10_CR29","unstructured":"Ren, M., Zeng, W., Yang, B., Urtasun, R.: Learning to reweight examples for robust deep learning. In: International Conference on Machine Learning (2018)"},{"key":"10_CR30","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"444","DOI":"10.1007\/978-3-030-44584-3_35","volume-title":"Advances in Intelligent Data Analysis XVIII","author":"MH Shaker","year":"2020","unstructured":"Shaker, M.H., H\u00fcllermeier, E.: Aleatoric and epistemic uncertainty with random forests. In: Berthold, M.R., Feelders, A., Krempl, G. (eds.) IDA 2020. LNCS, vol. 12080, pp. 444\u2013456. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-44584-3_35"},{"key":"10_CR31","doi-asserted-by":"crossref","unstructured":"Tsantekidis, A., Passalis, N., Tefas, A., Kanniainen, J., Gabbouj, M., Iosifidis, A.: Using deep learning to detect price change indications in financial markets. In: European Signal Processing Conference, pp. 2511\u20132515 (2017)","DOI":"10.23919\/EUSIPCO.2017.8081663"},{"issue":"4","key":"10_CR32","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1080\/07421222.2001.11045659","volume":"17","author":"S Walczak","year":"2001","unstructured":"Walczak, S.: An empirical analysis of data requirements for financial forecasting with neural networks. J. Manag. Inf. Syst. 17(4), 203\u2013222 (2001)","journal-title":"J. Manag. Inf. Syst."},{"key":"10_CR33","doi-asserted-by":"crossref","unstructured":"Wei, H., Feng, L., Chen, X., An, B.: Combating noisy labels by agreement: a joint training method with co-regularization. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 13726\u201313735 (2020)","DOI":"10.1109\/CVPR42600.2020.01374"},{"key":"10_CR34","doi-asserted-by":"crossref","unstructured":"Xu, Y., Cohen, S.B.: Stock movement prediction from tweets and historical prices. In: Annual Meeting of the Association for Computational Linguistics, pp. 1970\u20131979 (2018)","DOI":"10.18653\/v1\/P18-1183"},{"key":"10_CR35","unstructured":"Yu, X., Han, B., Yao, J., Niu, G., Tsang, I.W., Sugiyama, M.: How does disagreement help generalization against label corruption? arXiv preprint arXiv:1901.04215 (2019)"},{"key":"10_CR36","unstructured":"Zhang, C., Bengio, S., Hardt, M., Recht, B., Vinyals, O.: Understanding deep learning requires rethinking generalization. arXiv preprint arXiv:1611.03530 (2016)"},{"key":"10_CR37","doi-asserted-by":"crossref","unstructured":"Zhang, L., Aggarwal, C., Qi, G.J.: Stock price prediction via discovering multi-frequency trading patterns. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141\u20132149 (2017)","DOI":"10.1145\/3097983.3098117"},{"issue":"11","key":"10_CR38","doi-asserted-by":"publisher","first-page":"3001","DOI":"10.1109\/TSP.2019.2907260","volume":"67","author":"Z Zhang","year":"2019","unstructured":"Zhang, Z., Zohren, S., Roberts, S.: DeepLOB: deep convolutional neural networks for limit order books. IEEE Trans. Signal Process. 67(11), 3001\u20133012 (2019)","journal-title":"IEEE Trans. Signal Process."}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-26422-1_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,2]],"date-time":"2023-05-02T19:10:37Z","timestamp":1683054637000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-26422-1_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031264214","9783031264221"],"references-count":38,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-26422-1_10","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"18 March 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Grenoble","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2022.ecmlpkdd.org\/","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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1060","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":"236","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":"0","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":"22% - 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":"3-4","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":"3-4","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)"}},{"value":"17 demo track papers have been accepted from 28 submissions","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}