{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T13:50:51Z","timestamp":1784209851718,"version":"3.55.0"},"publisher-location":"Singapore","reference-count":53,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819608102","type":"print"},{"value":"9789819608119","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,12,13]],"date-time":"2024-12-13T00:00:00Z","timestamp":1734048000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,13]],"date-time":"2024-12-13T00:00:00Z","timestamp":1734048000000},"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":[[2025]]},"DOI":"10.1007\/978-981-96-0811-9_11","type":"book-chapter","created":{"date-parts":[[2024,12,12]],"date-time":"2024-12-12T17:27:39Z","timestamp":1734024459000},"page":"149-163","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Enhancing Financial Market Predictions: Causality-Driven Feature Selection"],"prefix":"10.1007","author":[{"given":"Wenhao","family":"Liang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhengyang","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weitong","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,12,13]]},"reference":[{"key":"11_CR1","doi-asserted-by":"publisher","unstructured":"Agustini, W.F., Affianti, I.R., Putri, E.R.: Stock price prediction using geometric Brownian motion. J. Phys. Confe. Ser. 974, 012047 (2018). https:\/\/doi.org\/10.1088\/1742-6596\/974\/1\/012047","DOI":"10.1088\/1742-6596\/974\/1\/012047"},{"issue":"3","key":"11_CR2","doi-asserted-by":"publisher","first-page":"1259","DOI":"10.1111\/j.1540-6261.2004.00662.x","volume":"59","author":"W Antweiler","year":"2004","unstructured":"Antweiler, W., Frank, M.Z.: Is all that talk just noise? The information content of internet stock message boards. J. Financ. 59(3), 1259\u20131294 (2004)","journal-title":"J. Financ."},{"key":"11_CR3","doi-asserted-by":"publisher","unstructured":"Baker, M., Wurgler, J.: Investor sentiment in the stock market. J. Econ. Perspect. 21(2), 129\u2013152 (2007). https:\/\/doi.org\/10.1257\/jep.21.2.129, https:\/\/www.aeaweb.org\/articles?id=10.1257\/jep.21.2.129","DOI":"10.1257\/jep.21.2.129"},{"issue":"7","key":"11_CR4","doi-asserted-by":"publisher","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), e0180944 (2017)","journal-title":"PLoS ONE"},{"key":"11_CR5","doi-asserted-by":"crossref","unstructured":"Batra, R., Daudpota, S.M.: Integrating stocktwits with sentiment analysis for better prediction of stock price movement. In: 2018 International Conference on Computing, Mathematics and Engineering Technologies (ICoMET), pp.\u00a01\u20135. IEEE (2018)","DOI":"10.1109\/ICOMET.2018.8346382"},{"issue":"1","key":"11_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1175\/1520-0493(1950)078<0001:VOFEIT>2.0.CO;2","volume":"78","author":"GW Brier","year":"1950","unstructured":"Brier, G.W.: Verification of forecasts expressed in terms of probability. Mon. Weather Rev. 78(1), 1\u20133 (1950)","journal-title":"Mon. Weather Rev."},{"key":"11_CR7","doi-asserted-by":"crossref","unstructured":"Chen, D., Zou, Y., Harimoto, K., Bao, R., Ren, X., Sun, X.: Incorporating fine-grained events in stock movement prediction. arXiv preprint arXiv:1910.05078 (2019)","DOI":"10.18653\/v1\/D19-5105"},{"key":"11_CR8","doi-asserted-by":"crossref","unstructured":"Deng, S., Zhang, N., Zhang, W., Chen, J., Pan, J.Z., Chen, H.: Knowledge-driven stock trend prediction and explanation via temporal convolutional network. In: Companion Proceedings of The 2019 World Wide Web Conference, pp. 678\u2013685 (2019)","DOI":"10.1145\/3308560.3317701"},{"key":"11_CR9","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)"},{"issue":"366","key":"11_CR10","doi-asserted-by":"publisher","first-page":"427","DOI":"10.2307\/2286348","volume":"74","author":"DA Dickey","year":"1979","unstructured":"Dickey, D.A., Fuller, W.A.: Distribution of the estimators for autoregressive time series with a unit root. J. Am. Stat. Assoc. 74(366), 427\u2013431 (1979)","journal-title":"J. Am. Stat. Assoc."},{"key":"11_CR11","unstructured":"Ding, X., Zhang, Y., Liu, T., Duan, J.: Knowledge-driven event embedding for stock prediction. In: Proceedings of coling 2016, the 26th international conference on computational linguistics: Technical Papers, pp. 2133\u20132142 (2016)"},{"key":"11_CR12","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1093\/jjfinec\/nbr005","volume":"10","author":"RF Engle","year":"2012","unstructured":"Engle, R.F., Sokalska, M.E.: Forecasting intraday volatility in the us equity market: multiplicative component garch. J. Financ. Economet. 10, 54\u201383 (2012)","journal-title":"J. Financ. Economet."},{"issue":"2","key":"11_CR13","doi-asserted-by":"publisher","first-page":"383","DOI":"10.2307\/2325486","volume":"25","author":"EF Fama","year":"1970","unstructured":"Fama, E.F.: Efficient capital markets: a review of theory and empirical work. J. Financ. 25(2), 383\u2013417 (1970)","journal-title":"J. Financ."},{"issue":"3","key":"11_CR14","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1002\/(SICI)1099-131X(199604)15:3<229::AID-FOR620>3.0.CO;2-3","volume":"15","author":"PH Franses","year":"1996","unstructured":"Franses, P.H., Van Dijk, D.: Forecasting stock market volatility using (non-linear) Garch models. J. Forecast. 15(3), 229\u2013235 (1996)","journal-title":"J. Forecast."},{"issue":"1","key":"11_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3483596","volume":"55","author":"W Ge","year":"2022","unstructured":"Ge, W., Lalbakhsh, P., Isai, L., Lenskiy, A., Suominen, H.: Neural network-based financial volatility forecasting: a systematic review. ACM Comput. Surv. (CSUR) 55(1), 1\u201330 (2022)","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"11_CR16","first-page":"1583","volume":"35","author":"A Ghosh","year":"2022","unstructured":"Ghosh, A., Schaaf, T., Gormley, M.: Adafocal: calibration-aware adaptive focal loss. Adv. Neural. Inf. Process. Syst. 35, 1583\u20131595 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"issue":"22","key":"11_CR17","doi-asserted-by":"publisher","first-page":"11867","DOI":"10.1007\/s00500-018-03743-0","volume":"23","author":"X-L Gong","year":"2019","unstructured":"Gong, X.-L., Liu, X.-H., Xiong, X., Zhuang, X.-T.: Forecasting stock volatility process using improved least square support vector machine approach. Soft. Comput. 23(22), 11867\u201311881 (2019). https:\/\/doi.org\/10.1007\/s00500-018-03743-0","journal-title":"Soft. Comput."},{"issue":"3","key":"11_CR18","doi-asserted-by":"publisher","first-page":"424","DOI":"10.2307\/1912791","volume":"37","author":"CWJ Granger","year":"1969","unstructured":"Granger, C.W.J.: Investigating causal relations by econometric models and cross-spectral methods. Econometrica 37(3), 424\u2013438 (1969)","journal-title":"Econometrica"},{"key":"11_CR19","unstructured":"Guo, C., Pleiss, G., Sun, Y., Weinberger, K.Q.: On calibration of modern neural networks. In: International Conference on Machine Learning, pp. 1321\u20131330. PMLR (2017)"},{"key":"11_CR20","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)"},{"issue":"1","key":"11_CR21","doi-asserted-by":"publisher","first-page":"13","DOI":"10.3390\/asi4010013","volume":"4","author":"M Jaggi","year":"2021","unstructured":"Jaggi, M., Mandal, P., Narang, S., Naseem, U., Khushi, M.: Text mining of stocktwits data for predicting stock prices. Appl. Syst. Innov. 4(1), 13 (2021)","journal-title":"Appl. Syst. Innov."},{"key":"11_CR22","doi-asserted-by":"publisher","first-page":"9713","DOI":"10.1007\/s00521-019-04504-2","volume":"32","author":"Z Jin","year":"2020","unstructured":"Jin, Z., Yang, Y., Liu, Y.: Stock closing price prediction based on sentiment analysis and LSTM. Neural Comput. Appl. 32, 9713\u20139729 (2020)","journal-title":"Neural Comput. Appl."},{"key":"11_CR23","unstructured":"Joachims, T., et\u00a0al.: A probabilistic analysis of the Rocchio algorithm with TFIDF for text categorization. In: ICML, vol.\u00a097, pp. 143\u2013151. Citeseer (1997)"},{"key":"11_CR24","doi-asserted-by":"crossref","unstructured":"Kim, Y.: Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882 (2014)","DOI":"10.3115\/v1\/D14-1181"},{"key":"11_CR25","doi-asserted-by":"publisher","unstructured":"Lewis, D.: Reuters-21578 Text Categorization Collection. UCI Machine Learning Repository (1997). https:\/\/doi.org\/10.24432\/C52G6M","DOI":"10.24432\/C52G6M"},{"key":"11_CR26","doi-asserted-by":"crossref","unstructured":"Li, J., Bu, H., Wu, J.: Sentiment-aware stock market prediction: a deep learning method. In: 2017 International Conference on Service Systems and Service Management, pp.\u00a01\u20136. IEEE (2017)","DOI":"10.1109\/ICSSSM.2017.7996306"},{"issue":"5","key":"11_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2020.102212","volume":"57","author":"X Li","year":"2020","unstructured":"Li, X., Wu, P., Wang, W.: Incorporating stock prices and news sentiments for stock market prediction: a case of Hong Kong. Inf. Process. Manag. 57(5), 102212 (2020)","journal-title":"Inf. Process. Manag."},{"key":"11_CR28","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1016\/j.knosys.2014.04.022","volume":"69","author":"X Li","year":"2014","unstructured":"Li, X., Xie, H., Chen, L., Wang, J., Deng, X.: News impact on stock price return via sentiment analysis. Knowl.-Based Syst. 69, 14\u201323 (2014)","journal-title":"Knowl.-Based Syst."},{"key":"11_CR29","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., Doll\u00e1r, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980\u20132988 (2017)","DOI":"10.1109\/ICCV.2017.324"},{"issue":"1","key":"11_CR30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13362-019-0066-7","volume":"9","author":"S Liu","year":"2019","unstructured":"Liu, S., Borovykh, A., Grzelak, L.A., Oosterlee, C.W.: A neural network-based framework for financial model calibration. J. Math. Ind. 9(1), 1\u201328 (2019). https:\/\/doi.org\/10.1186\/s13362-019-0066-7","journal-title":"J. Math. Ind."},{"issue":"4","key":"11_CR31","first-page":"782","volume":"65","author":"P Malo","year":"2014","unstructured":"Malo, P., Sinha, A., Korhonen, P., Wallenius, J., Takala, P.: Good debt or bad debt: Detecting semantic orientations in economic texts. J. Am. Soc. Inf. Sci. 65(4), 782\u2013796 (2014)","journal-title":"J. Am. Soc. Inf. Sci."},{"key":"11_CR32","doi-asserted-by":"crossref","unstructured":"Marcucci, J.: Forecasting stock market volatility with regime-switching Garch models. Stud. Nonlinear Dyn. Econ. 9(4) (2005)","DOI":"10.2202\/1558-3708.1145"},{"issue":"1","key":"11_CR33","first-page":"77","volume":"7","author":"H Markowitz","year":"1952","unstructured":"Markowitz, H.: Portfolio selection. J. Financ. 7(1), 77\u201391 (1952)","journal-title":"J. Financ."},{"key":"11_CR34","doi-asserted-by":"crossref","unstructured":"Mohan, S., Mullapudi, S., Sammeta, S., Vijayvergia, P., Anastasiu, D.C.: Stock price prediction using news sentiment analysis. In: 2019 IEEE Fifth International Conference on Big Data Computing Service and Applications (BigDataService), pp. 205\u2013208. IEEE (2019)","DOI":"10.1109\/BigDataService.2019.00035"},{"issue":"1","key":"11_CR35","first-page":"41","volume":"26","author":"AH Murphy","year":"1977","unstructured":"Murphy, A.H., Winkler, R.L.: Reliability of subjective probability forecasts of precipitation and temperature. J. R. Stat. Soc.: Ser. C: Appl. Stat. 26(1), 41\u201347 (1977)","journal-title":"J. R. Stat. Soc.: Ser. C: Appl. Stat."},{"key":"11_CR36","doi-asserted-by":"crossref","unstructured":"Niculescu-Mizil, A., Caruana, R.: Predicting good probabilities with supervised learning. In: Proceedings of the 22nd International Conference on Machine Learning, pp. 625\u2013632 (2005)","DOI":"10.1145\/1102351.1102430"},{"key":"11_CR37","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"355","DOI":"10.1007\/978-3-642-40669-0_31","volume-title":"Progress in Artificial Intelligence","author":"N Oliveira","year":"2013","unstructured":"Oliveira, N., Cortez, P., Areal, N.: On the predictability of stock market behavior using StockTwits sentiment and posting volume. In: Correia, L., Reis, L.P., Cascalho, J. (eds.) EPIA 2013. LNCS (LNAI), vol. 8154, pp. 355\u2013365. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-642-40669-0_31"},{"key":"11_CR38","doi-asserted-by":"crossref","unstructured":"Oliveira, N., Cortez, P., Areal, N.: Automatic creation of stock market lexicons for sentiment analysis using stocktwits data. In: Proceedings of the 18th International Database Engineering & Applications Symposium, pp. 115\u2013123 (2014)","DOI":"10.1145\/2628194.2628235"},{"key":"11_CR39","doi-asserted-by":"crossref","unstructured":"Pei, Y., et al.: Tweetfinsent: a dataset of stock sentiments on twitter. In: Proceedings of the Fourth Workshop on Financial Technology and Natural Language Processing (FinNLP), pp. 37\u201347 (2022)","DOI":"10.18653\/v1\/2022.finnlp-1.5"},{"key":"11_CR40","doi-asserted-by":"crossref","unstructured":"Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532\u20131543 (2014)","DOI":"10.3115\/v1\/D14-1162"},{"issue":"3","key":"11_CR41","first-page":"61","volume":"10","author":"J Platt","year":"1999","unstructured":"Platt, J., et al.: Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. .Adv. Large Margin Classif. 10(3), 61\u201374 (1999)","journal-title":".Adv. Large Margin Classif."},{"issue":"2","key":"11_CR42","doi-asserted-by":"publisher","first-page":"478","DOI":"10.1257\/jel.41.2.478","volume":"41","author":"SH Poon","year":"2003","unstructured":"Poon, S.H., Granger, C.W.J.: Forecasting volatility in financial markets: A review. J. Econ. Literat. 41(2), 478\u2013539 (2003)","journal-title":"J. Econ. Literat."},{"key":"11_CR43","unstructured":"PsychSignal. https:\/\/www.PsychSignal.com"},{"key":"11_CR44","doi-asserted-by":"crossref","unstructured":"Solaiman, S., Rahman, B.O., Arefin, M.F., Ahmed, C.F., Leung, C.K., Madill, E.W.: Unveiling market sentiments: a comprehensive analysis of stock market responses to diverse news events using data mining techniques. In: 2024 18th International Conference on Ubiquitous Information Management and Communication (IMCOM), pp.\u00a01\u20138. IEEE (2024)","DOI":"10.1109\/IMCOM60618.2024.10418389"},{"key":"11_CR45","unstructured":"Tao, L., Dong, M., Xu, C.: Dual focal loss for calibration. arXiv preprint arXiv:2305.13665 (2023)"},{"key":"11_CR46","unstructured":"TradingEconomics. https:\/\/www.tradingeconomics.com"},{"key":"11_CR47","doi-asserted-by":"crossref","unstructured":"Vargas, M.R., De\u00a0Lima, B.S., Evsukoff, A.G.: Deep learning for stock market prediction from financial news articles. In: 2017 IEEE international conference on computational intelligence and virtual environments for measurement systems and applications (CIVEMSA), pp. 60\u201365. IEEE (2017)","DOI":"10.1109\/CIVEMSA.2017.7995302"},{"key":"11_CR48","doi-asserted-by":"crossref","unstructured":"Vui, C.S., Soon, G.K., On, C.K., Alfred, R., Anthony, P.: A review of stock market prediction with artificial neural network (ANN). In: 2013 IEEE International Conference on Control System, Computing and Engineering, pp. 477\u2013482. IEEE (2013)","DOI":"10.1109\/ICCSCE.2013.6720012"},{"key":"11_CR49","unstructured":"Wilder, W.: New Concepts In Technical Trading Systems. Wilder\u2019s own publication (1978). introduction of the momentum concept in stock market analysis"},{"key":"11_CR50","unstructured":"Wolf, T., et\u00a0al.: Transformers: state-of-the-art natural language processing. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pp. 38\u201345 (2020)"},{"key":"11_CR51","unstructured":"Yahoo!Finance\u2019sAPI. https:\/\/pypi.org\/project\/yfinance\/"},{"key":"11_CR52","unstructured":"Zadrozny, B., Elkan, C.: Obtaining calibrated probability estimates from decision trees and Naive Bayesian classifiers. In: ICML, vol.\u00a01, pp. 609\u2013616 (2001)"},{"key":"11_CR53","unstructured":"Zhang, X., Zhao, J., LeCun, Y.: Character-level convolutional networks for text classification. In: Advances in Neural Information Processing Systems, vol. 28 (2015)"}],"container-title":["Lecture Notes in Computer Science","Advanced Data Mining and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-0811-9_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,12]],"date-time":"2024-12-12T18:03:04Z","timestamp":1734026584000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-0811-9_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,13]]},"ISBN":["9789819608102","9789819608119"],"references-count":53,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-0811-9_11","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,13]]},"assertion":[{"value":"13 December 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ADMA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Advanced Data Mining and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Sydney, NSW","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Australia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 December 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 December 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"adma2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/adma2024.github.io\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}