{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T10:37:45Z","timestamp":1742985465502,"version":"3.40.3"},"publisher-location":"Cham","reference-count":105,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030669805"},{"type":"electronic","value":"9783030669812"}],"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.springernature.com\/gp\/researchers\/text-and-data-mining"},{"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.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-66981-2_2","type":"book-chapter","created":{"date-parts":[[2021,1,14]],"date-time":"2021-01-14T14:06:41Z","timestamp":1610633201000},"page":"16-31","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["How Much Does Stock Prediction Improve with Sentiment Analysis?"],"prefix":"10.1007","author":[{"given":"Frederico G.","family":"Monteiro","sequence":"first","affiliation":[]},{"given":"Diogo R.","family":"Ferreira","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,1,15]]},"reference":[{"issue":"1","key":"2_CR1","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1086\/294743","volume":"38","author":"EF Fama","year":"1965","unstructured":"Fama, E.F.: The behavior of stock-market prices. J. Bus. 38(1), 34\u2013105 (1965)","journal-title":"J. Bus."},{"key":"2_CR2","unstructured":"Elliott, R.N.: The Wave Principle. Alanpuri Trading, Rancho Cucamonga (1938)"},{"issue":"2","key":"2_CR3","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. Finan. 25(2), 383\u2013417 (1970)","journal-title":"J. Finan."},{"issue":"9\u201310","key":"2_CR4","first-page":"1487","volume":"23","author":"B LeBaron","year":"1999","unstructured":"LeBaron, B., Arthur, W.B., Palmer, R.: Time series properties of an artificial stock market. JEDC 23(9\u201310), 1487\u20131516 (1999)","journal-title":"JEDC"},{"key":"2_CR5","unstructured":"Emerson, S., Kennedy, R., O\u2019Shea, L., O\u2019Brien, J.: Trends and applications of machine learning in quantitative finance. In: ICEFR 2019, June 2019"},{"issue":"3","key":"2_CR6","doi-asserted-by":"publisher","first-page":"6668","DOI":"10.1016\/j.eswa.2008.08.019","volume":"36","author":"B Vanstone","year":"2009","unstructured":"Vanstone, B., Finnie, G.: An empirical methodology for developing stock market trading systems using artificial neural networks. Expert Syst. Appl. 36(3), 6668\u20136680 (2009)","journal-title":"Expert Syst. Appl."},{"issue":"3","key":"2_CR7","doi-asserted-by":"publisher","first-page":"1186","DOI":"10.1016\/j.eswa.2007.08.038","volume":"35","author":"AJ Hussain","year":"2008","unstructured":"Hussain, A.J., Knowles, A., Lisboa, P.J.G., El-Deredy, W.: Financial time series prediction using polynomial pipelined neural networks. Expert Syst. Appl. 35(3), 1186\u20131199 (2008)","journal-title":"Expert Syst. Appl."},{"key":"2_CR8","volume-title":"Time Series Analysis, Forecasting and Control","author":"GEP Box","year":"1990","unstructured":"Box, G.E.P., Jenkins, G.: Time Series Analysis, Forecasting and Control, 5th edn. Wiley, Hoboken (1990)","edition":"5"},{"issue":"1","key":"2_CR9","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1016\/j.rfe.2013.05.004","volume":"23","author":"J Fang","year":"2014","unstructured":"Fang, J., Jacobsen, B., Qin, Y.: Predictability of the simple technical trading rules: an out-of-sample test. Rev. Fin. Econ. 23(1), 30\u201345 (2014)","journal-title":"Rev. Fin. Econ."},{"issue":"6","key":"2_CR10","doi-asserted-by":"publisher","first-page":"1506","DOI":"10.1109\/TNN.2003.820556","volume":"14","author":"LJ Cao","year":"2003","unstructured":"Cao, L.J., Tay, F.E.H.: Support vector machine with adaptive parameters in financial time series forecasting. IEEE Trans. Neural Netw. 14(6), 1506\u20131518 (2003)","journal-title":"IEEE Trans. Neural Netw."},{"issue":"2","key":"2_CR11","doi-asserted-by":"publisher","first-page":"689","DOI":"10.1016\/j.ejor.2016.10.031","volume":"259","author":"C Krauss","year":"2017","unstructured":"Krauss, C., Do, X.A., Huck, N.: Deep neural networks, gradient-boosted trees, random forests: statistical arbitrage on the S&P 500. EJOR 259(2), 689\u2013702 (2017)","journal-title":"EJOR"},{"key":"2_CR12","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/j.eswa.2018.03.039","volume":"105","author":"LS Malagrino","year":"2018","unstructured":"Malagrino, L.S., Roman, N.T., Monteiro, A.M.: Forecasting stock market index daily direction: a Bayesian network approach. Expert Syst. Appl. 105, 11\u201322 (2018)","journal-title":"Expert Syst. Appl."},{"key":"2_CR13","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1016\/j.eswa.2017.04.030","volume":"83","author":"E Chong","year":"2017","unstructured":"Chong, E., Han, C., Park, F.C.: Deep learning networks for stock market analysis and prediction: methodology, data representations, and case studies. Expert Syst. Appl. 83, 187\u2013205 (2017)","journal-title":"Expert Syst. Appl."},{"issue":"2","key":"2_CR14","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1016\/0925-2312(95)00020-8","volume":"10","author":"N Kohzadi","year":"1996","unstructured":"Kohzadi, N., Boyd, M.S., Kermanshahi, B., Kaastra, I.: A comparison of artificial neural network and time series models for forecasting commodity prices. Neurocomputing 10(2), 169\u2013181 (1996)","journal-title":"Neurocomputing"},{"key":"2_CR15","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1016\/S0925-2312(01)00702-0","volume":"50","author":"GP Zhang","year":"2003","unstructured":"Zhang, G.P.: Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50, 159\u2013175 (2003)","journal-title":"Neurocomputing"},{"key":"2_CR16","doi-asserted-by":"publisher","first-page":"353","DOI":"10.1016\/j.eswa.2018.06.032","volume":"112","author":"SP Chatzis","year":"2018","unstructured":"Chatzis, S.P., Siakoulis, V., Petropoulos, A., Stavroulakis, E., Vlachogiannakis, N.: Forecasting stock market crisis events using deep and statistical machine learning techniques. Expert Syst. Appl. 112, 353\u2013371 (2018)","journal-title":"Expert Syst. Appl."},{"key":"2_CR17","doi-asserted-by":"crossref","unstructured":"Naz\u00e1rio, R.T.F., e Silva, J.L., Sobreiro, V.A., Kimura, H.: A literature review of technical analysis on stock markets. QREF 66, 115\u2013126 (2017)","DOI":"10.1016\/j.qref.2017.01.014"},{"issue":"3","key":"2_CR18","doi-asserted-by":"publisher","first-page":"653","DOI":"10.1109\/TNNLS.2016.2522401","volume":"28","author":"Y Deng","year":"2017","unstructured":"Deng, Y., Bao, F., Kong, Y., Ren, Z., Dai, Q.: Deep direct reinforcement learning for financial signal representation and trading. IEEE Trans. Neural Netw. Learn. Syst. 28(3), 653\u2013664 (2017)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"2_CR19","unstructured":"Si, J., Mukherjee, A., Liu, B., Li, Q., Li, H., Deng, X.: Exploiting topic based twitter sentiment for stock prediction. In: ACL 2013, vol. 2, pp. 24\u201329, August 2013"},{"key":"2_CR20","doi-asserted-by":"crossref","unstructured":"Si, J., Mukherjee, A., Liu, B., Pan, S.J., Li, Q., Li, H.: Exploiting social relations and sentiment for stock prediction. In: EMNLP, pp. 1139\u20131145, October 2014","DOI":"10.3115\/v1\/D14-1120"},{"issue":"3","key":"2_CR21","doi-asserted-by":"publisher","first-page":"62","DOI":"10.4236\/sn.2015.43008","volume":"4","author":"B Dickinson","year":"2015","unstructured":"Dickinson, B., Hu, W.: Sentiment analysis of investor opinions on Twitter. Soc. Netw. 4(3), 62\u201371 (2015)","journal-title":"Soc. Netw."},{"key":"2_CR22","unstructured":"Mittal, A., Goel, A.: Stock prediction using twitter sentiment analysis. Standford University, CS229 (2012)"},{"issue":"2","key":"2_CR23","doi-asserted-by":"publisher","first-page":"654","DOI":"10.1016\/j.ejor.2017.11.054","volume":"270","author":"T Fischer","year":"2018","unstructured":"Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. EJOR 270(2), 654\u2013669 (2018)","journal-title":"EJOR"},{"key":"2_CR24","doi-asserted-by":"publisher","first-page":"228","DOI":"10.1016\/j.eswa.2018.09.005","volume":"117","author":"TK Lee","year":"2019","unstructured":"Lee, T.K., Cho, J.H., Kwon, D.S., Sohn, S.Y.: Global stock market investment strategies based on financial network indicators using machine learning techniques. Expert Syst. Appl. 117, 228\u2013242 (2019)","journal-title":"Expert Syst. Appl."},{"issue":"8","key":"2_CR25","doi-asserted-by":"publisher","first-page":"10389","DOI":"10.1016\/j.eswa.2011.02.068","volume":"38","author":"E Guresen","year":"2011","unstructured":"Guresen, E., Kayakutlu, G., Daim, T.U.: Using artificial neural network models in stock market index prediction. Expert Syst. Appl. 38(8), 10389\u201310397 (2011)","journal-title":"Expert Syst. Appl."},{"key":"2_CR26","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1016\/j.knosys.2018.10.035","volume":"164","author":"H Wang","year":"2019","unstructured":"Wang, H., Lu, S., Zhao, J.: Aggregating multiple types of complex data in stock market prediction: a model-independent framework. Knowl.-Based Syst. 164, 193\u2013204 (2019)","journal-title":"Knowl.-Based Syst."},{"key":"2_CR27","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1016\/j.eswa.2018.03.002","volume":"103","author":"HY Kim","year":"2018","unstructured":"Kim, H.Y., Won, C.H.: Forecasting the volatility of stock price index: a hybrid model integrating LSTM with multiple GARCH-type models. Expert Syst. Appl. 103, 25\u201337 (2018)","journal-title":"Expert Syst. Appl."},{"key":"2_CR28","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1016\/j.knosys.2018.10.034","volume":"164","author":"W Long","year":"2019","unstructured":"Long, W., Lu, Z., Cui, L.: Deep learning-based feature engineering for stock price movement prediction. Knowl.-Based Syst. 164, 163\u2013173 (2019)","journal-title":"Knowl.-Based Syst."},{"key":"2_CR29","doi-asserted-by":"crossref","unstructured":"Selvin, S., Vinayakumar, R., Gopalakrishnan, E.A., Menon, V.K., Soman, K.P.: Stock price prediction using LSTM. In: ICACCI (RNN and CNN-sliding window model), September 2017","DOI":"10.1109\/ICACCI.2017.8126078"},{"key":"2_CR30","unstructured":"Schumaker, R.P., Chen, H.: Textual analysis of stock market prediction using financial news articles. In: AMCIS 2006, vol. 3, pp. 1422\u20131430, December 2006"},{"key":"2_CR31","first-page":"1415","volume":"2014","author":"X Ding","year":"2014","unstructured":"Ding, X., Zhang, Y., Liu, T., Duan, J.: Using structured events to predict stock price movement: an empirical investigation. EMNLP 2014, 1415\u20131425 (2014)","journal-title":"EMNLP"},{"key":"2_CR32","first-page":"60","volume":"2017","author":"MR Vargas","year":"2017","unstructured":"Vargas, M.R., De Lima, B.S.L.P., Evsukoff, A.G.: Deep learning for stock market prediction from financial news articles. CIVEMSA 2017, 60\u201365 (2017)","journal-title":"CIVEMSA"},{"key":"2_CR33","doi-asserted-by":"crossref","unstructured":"Nguyen, T.H., Shirai, K.: Topic modeling based sentiment analysis on social media for stock market prediction. In: ACL-IJCNLP 2015, vol. 1, pp. 1354\u20131364, July 2015","DOI":"10.3115\/v1\/P15-1131"},{"key":"2_CR34","doi-asserted-by":"publisher","first-page":"258","DOI":"10.1016\/j.eswa.2018.06.016","volume":"112","author":"B Weng","year":"2018","unstructured":"Weng, B., Lu, L., Wang, X., Megahed, F.M., Martinez, W.: Predicting short-term stock prices using ensemble methods and online data sources. Expert Syst. Appl. 112, 258\u2013273 (2018)","journal-title":"Expert Syst. Appl."},{"issue":"23","key":"2_CR35","doi-asserted-by":"publisher","first-page":"9192","DOI":"10.1016\/j.eswa.2015.08.008","volume":"42","author":"AA Nasseri","year":"2015","unstructured":"Nasseri, A.A., Tucker, A., de Cesare, S.: Quantifying StockTwits semantic terms\u2019 trading behavior in financial markets: an effective application of decision tree algorithms. Expert Syst. Appl. 42(23), 9192\u20139210 (2015)","journal-title":"Expert Syst. Appl."},{"key":"2_CR36","doi-asserted-by":"crossref","unstructured":"Deng, S., Mitsubuchi, T., Shioda, K., Shimada, T., Sakurai, A.: Combining technical analysis with sentiment analysis for stock price prediction. In: DASC2011, pp. 800\u2013807, December 2011","DOI":"10.1109\/DASC.2011.138"},{"key":"2_CR37","unstructured":"Di Persio, L., Honchar, O.: Artificial neural networks architectures for stock price prediction: comparisons and applications. Int. J. Circuits, Syst. Sig. Process. 10, 403\u2013413 (2016)"},{"key":"2_CR38","doi-asserted-by":"publisher","first-page":"1351","DOI":"10.1016\/j.procs.2018.05.050","volume":"132","author":"M Hiransha","year":"2018","unstructured":"Hiransha, M., Gopalakrishnan, E.A., Menon, V.K., Soman, K.P.: NSE stock market prediction using deep-learning models. Procedia Comput. Sci. 132, 1351\u20131362 (2018)","journal-title":"Procedia Comput. Sci."},{"key":"2_CR39","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":"2_CR40","unstructured":"Li, J., Bu, H., Wu, J.: Sentiment-aware stock market prediction: a deep learning method. In: ICSSSM 2017, June 2017"},{"key":"2_CR41","unstructured":"Kim, S., Kang, M.: Financial series prediction using attention LSTM. arXiv:1902.10877, February 2019"},{"key":"2_CR42","doi-asserted-by":"crossref","unstructured":"Akita, R., Yoshihara, A., Matsubara, T., Uehara, K.: Deep learning for stock prediction using numerical and textual information. In: ICIS 2016, vol. 1, pp. 1\u20136, June 2016","DOI":"10.1109\/ICIS.2016.7550882"},{"key":"2_CR43","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.eswa.2017.12.026","volume":"97","author":"J Zhang","year":"2017","unstructured":"Zhang, J., Cui, S., Xu, Y., Li, Q., Li, T.: A novel data-driven stock price trend prediction system. Expert Syst. Appl. 97, 60\u201369 (2017)","journal-title":"Expert Syst. Appl."},{"key":"2_CR44","doi-asserted-by":"publisher","first-page":"635","DOI":"10.1016\/j.eswa.2018.08.003","volume":"115","author":"FD Paiva","year":"2019","unstructured":"Paiva, F.D., Cardoso, R.T.N., Hanaoka, G.P., Duarte, W.M.: Decision-making for financial trading: a fusion approach of machine learning and portfolio selection. Expert Syst. Appl. 115, 635\u2013655 (2019)","journal-title":"Expert Syst. Appl."},{"key":"2_CR45","first-page":"2327","volume":"2015","author":"X Ding","year":"2015","unstructured":"Ding, X., Zhang, Y., Liu, T., Duan, J.: Deep learning for event-driven stock prediction. IJCAI 2015, 2327\u20132333 (2015)","journal-title":"IJCAI"},{"key":"2_CR46","doi-asserted-by":"crossref","unstructured":"Zhou, F., Zhou, H.M., Yang, Z., Yang, L.: EMD2FNN: a strategy combining empirical mode decomposition and factorization machine based neural network for stock market trend prediction. Expert Syst. Appl. 115, 136\u2013151 (2019)","DOI":"10.1016\/j.eswa.2018.07.065"},{"issue":"1","key":"2_CR47","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.jocs.2010.12.007","volume":"2","author":"J Bollen","year":"2011","unstructured":"Bollen, J., Mao, H., Zeng, X.: Twitter mood predicts the stock market. J. Comput. Sci. 2(1), 1\u20138 (2011)","journal-title":"J. Comput. Sci."},{"key":"2_CR48","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1016\/j.eswa.2018.11.027","volume":"120","author":"B Moews","year":"2019","unstructured":"Moews, B., Herrmann, J.M., Ibikunle, G.: Lagged correlation-based deep learning for directional trend change prediction in financial time series. Expert Syst. Appl. 120, 197\u2013206 (2019)","journal-title":"Expert Syst. Appl."},{"key":"2_CR49","doi-asserted-by":"publisher","first-page":"457","DOI":"10.1016\/j.eswa.2018.07.019","volume":"113","author":"Y Baek","year":"2018","unstructured":"Baek, Y., Kim, H.Y.: ModAugNet: a new forecasting framework for stock market index value with an overfitting prevention LSTM module and a prediction LSTM module. Expert Syst. Appl. 113, 457\u2013480 (2018)","journal-title":"Expert Syst. Appl."},{"key":"2_CR50","unstructured":"Prosky, J., Song, X., Tan, A., Zhao, M.: Sentiment predictability for stocks. arXiv:1712.05785, December 2017"},{"key":"2_CR51","unstructured":"Hollis, T., Viscardi, A., Yi, S.E.: A comparison of LSTMs and attention mechanisms for forecasting financial time series. arXiv:1812.07699, December 2018"},{"key":"2_CR52","doi-asserted-by":"crossref","unstructured":"Day, M.Y., Lee, C.C.: Deep learning for financial sentiment analysis on finance news providers. In: ASONAM, pp. 1127\u20131134, August 2016","DOI":"10.1109\/ASONAM.2016.7752381"},{"key":"2_CR53","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1016\/j.eswa.2017.06.023","volume":"87","author":"S Almahdi","year":"2017","unstructured":"Almahdi, S., Yang, S.Y.: An adaptive portfolio trading system: a risk-return portfolio optimization using recurrent reinforcement learning with expected maximum drawdown. Expert Syst. Appl. 87, 267\u2013279 (2017)","journal-title":"Expert Syst. Appl."},{"key":"2_CR54","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1016\/j.eswa.2018.09.036","volume":"117","author":"G Jeong","year":"2019","unstructured":"Jeong, G., Kim, H.Y.: Improving financial trading decisions using deep Q-learning: predicting the number of shares, action strategies, and transfer learning. Expert Syst. Appl. 117, 125\u2013138 (2019)","journal-title":"Expert Syst. Appl."},{"key":"2_CR55","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1016\/j.cosrev.2017.10.002","volume":"27","author":"M M\u00e4ntyl\u00e4","year":"2018","unstructured":"M\u00e4ntyl\u00e4, M., Graziotin, D., Kuutila, M.: The evolution of sentiment analysis - a review of research topics, venues, and top cited papers. Comput. Sci. Rev. 27, 16\u201332 (2018)","journal-title":"Comput. Sci. Rev."},{"key":"2_CR56","first-page":"984","volume":"2007","author":"A Devitt","year":"2007","unstructured":"Devitt, A., Ahmad, K.: Sentiment polarity identification in financial news: a Cohesion-based approach. ACL 2007, 984\u2013991 (2007)","journal-title":"ACL"},{"key":"2_CR57","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1016\/j.dss.2016.06.020","volume":"90","author":"S Feuerriegel","year":"2016","unstructured":"Feuerriegel, S., Prendinger, H.: News-based trading strategies. Decis. Support Syst. 90, 65\u201374 (2016)","journal-title":"Decis. Support Syst."},{"key":"2_CR58","first-page":"21","volume":"2007","author":"V Sehgal","year":"2007","unstructured":"Sehgal, V., Song, C.: SOPS: stock prediction using web sentiment. ICDM 2007, 21\u201326 (2007)","journal-title":"ICDM"},{"issue":"2","key":"2_CR59","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1109\/MIS.2013.30","volume":"28","author":"E Cambria","year":"2013","unstructured":"Cambria, E., Schuller, B., Xia, Y., Havasi, C.: New avenues in opinion mining and sentiment analysis. IEEE Intell. Syst. 28(2), 15\u201321 (2013)","journal-title":"IEEE Intell. Syst."},{"key":"2_CR60","doi-asserted-by":"crossref","unstructured":"Wuthrich, B., Cho, V., Leung, S., Permunetilleke, D., Sankaran, K., Zhang, J.: Daily stock market forecast from textual web data. In: SMC 1998, vol. 3, pp. 2720\u20132725, October 1998","DOI":"10.1109\/ICSMC.1998.725072"},{"key":"2_CR61","unstructured":"Xiong, R., Nichols, E.P., Shen, Y.: Deep learning stock volatility with google domestic trends. arXiv:1512.04916, December 2015"},{"key":"2_CR62","first-page":"118","volume":"45","author":"S Agarwal","year":"2019","unstructured":"Agarwal, S., Kumar, S., Goel, U.: Stock market response to information diffusion through internet sources: a literature review. IJIM 45, 118\u2013131 (2019)","journal-title":"IJIM"},{"key":"2_CR63","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1016\/j.eswa.2017.03.028","volume":"81","author":"R Ar\u00e9valo","year":"2017","unstructured":"Ar\u00e9valo, R., Garc\u00eda, J., Guijarro, F., Peris, A.: A dynamic trading rule based on filtered flag pattern recognition for stock market price forecasting. Expert Syst. Appl. 81, 177\u2013192 (2017)","journal-title":"Expert Syst. Appl."},{"key":"2_CR64","unstructured":"Hollis, T.: deep learning algorithms applied to blockchain-based financial time series. Technical report, University of Manchester (2018)"},{"key":"2_CR65","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1016\/j.eswa.2019.03.029","volume":"129","author":"E Hoseinzade","year":"2019","unstructured":"Hoseinzade, E., Haratizadeh, S.: CNNpred: CNN-based stock market prediction using a diverse set of variables. Expert Syst. Appl. 129, 273\u2013285 (2019)","journal-title":"Expert Syst. Appl."},{"issue":"4","key":"2_CR66","doi-asserted-by":"publisher","first-page":"2870","DOI":"10.1016\/j.eswa.2007.05.035","volume":"34","author":"CJ Huang","year":"2008","unstructured":"Huang, C.J., Yang, D.X., Chuang, Y.T.: Application of wrapper approach and composite classifier to the stock trend prediction. Expert Syst. Appl. 34(4), 2870\u20132878 (2008)","journal-title":"Expert Syst. Appl."},{"issue":"10","key":"2_CR67","doi-asserted-by":"publisher","first-page":"2513","DOI":"10.1016\/j.cor.2004.03.016","volume":"32","author":"W Huang","year":"2005","unstructured":"Huang, W., Nakamori, Y., Wang, S.Y.: Forecasting stock market movement direction with support vector machine. Comput. Oper. Res. 32(10), 2513\u20132522 (2005)","journal-title":"Comput. Oper. Res."},{"issue":"1","key":"2_CR68","doi-asserted-by":"publisher","first-page":"306","DOI":"10.1016\/j.eswa.2014.08.004","volume":"42","author":"AK Nassirtoussi","year":"2015","unstructured":"Nassirtoussi, A.K., Aghabozorgi, S., Wah, T.Y., Ngo, D.C.L.: Text mining of news-headlines for FOREX market prediction: a multi-layer dimension reduction algorithm with semantics and sentiment. Expert Syst. Appl. 42(1), 306\u2013324 (2015)","journal-title":"Expert Syst. Appl."},{"key":"2_CR69","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1016\/j.is.2016.10.001","volume":"69","author":"B Li","year":"2017","unstructured":"Li, B., Chan, K.C.C., Ou, C., Ruifeng, S.: Discovering public sentiment in social media for predicting stock movement of publicly listed companies. Inf. Syst. 69, 81\u201392 (2017)","journal-title":"Inf. Syst."},{"key":"2_CR70","doi-asserted-by":"crossref","unstructured":"Kar, S., Maharjan, S., Solorio, T.: RiTUAL-UH at SemEval-2017 Task 5: sentiment analysis on financial data using neural networks. In: SemEval-2017, pp. 877\u2013882, August 2017","DOI":"10.18653\/v1\/S17-2150"},{"key":"2_CR71","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1016\/j.eswa.2017.02.041","volume":"79","author":"B Weng","year":"2017","unstructured":"Weng, B., Ahmed, M.A., Megahed, F.M.: Stock market one-day ahead movement prediction using disparate data sources. Expert Syst. Appl. 79, 153\u2013163 (2017)","journal-title":"Expert Syst. Appl."},{"key":"2_CR72","doi-asserted-by":"crossref","unstructured":"Cabanski, T., Romberg, J., Conrad, S.: HHU at SemEval-2017 Task 5: fine-grained sentiment analysis on financial data using machine learning methods. In: SemEval-2017, pp. 832\u2013836, August 2017","DOI":"10.18653\/v1\/S17-2141"},{"issue":"5","key":"2_CR73","doi-asserted-by":"publisher","first-page":"571","DOI":"10.1016\/j.ipm.2009.05.001","volume":"45","author":"RP Schumaker","year":"2009","unstructured":"Schumaker, R.P., Chen, H.: A quantitative stock prediction system based on financial news. Inf. Process. Manage. 45(5), 571\u2013583 (2009)","journal-title":"Inf. Process. Manage."},{"issue":"5","key":"2_CR74","doi-asserted-by":"publisher","first-page":"5311","DOI":"10.1016\/j.eswa.2010.10.027","volume":"38","author":"Y Kara","year":"2011","unstructured":"Kara, Y., Boyacioglu, M.A., Baykan, \u00d6.K.: Predicting direction of stock price index movement using artificial neural networks and support vector machines: the sample of the Istanbul stock exchange. Expert Syst. Appl. 38(5), 5311\u20135319 (2011)","journal-title":"Expert Syst. Appl."},{"issue":"1\u20132","key":"2_CR75","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1016\/S0925-2312(03)00372-2","volume":"55","author":"K Kim","year":"2003","unstructured":"Kim, K.: Financial time series forecasting using support vector machines. Neurocomputing 55(1\u20132), 307\u2013319 (2003)","journal-title":"Neurocomputing"},{"key":"2_CR76","unstructured":"Sen, J., Chaudhuri, T.D.: Decomposition of time series data of stock markets and its implications for prediction - an application for the Indian auto sector. In: ABRMP 2016, January 2016"},{"issue":"5","key":"2_CR77","doi-asserted-by":"publisher","first-page":"805","DOI":"10.1007\/s00500-012-0953-y","volume":"17","author":"G Zhiqiang","year":"2013","unstructured":"Zhiqiang, G., Huaiqing, W., Quan, L.: Financial time series forecasting using LPP and SVM optimized by PSO. Soft. Comput. 17(5), 805\u2013818 (2013)","journal-title":"Soft. Comput."},{"key":"2_CR78","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"424","DOI":"10.1007\/978-3-319-42297-8_40","volume-title":"Intelligent Computing Methodologies","author":"A Ar\u00e9valo","year":"2016","unstructured":"Ar\u00e9valo, A., Ni\u00f1o, J., Hern\u00e1ndez, G., Sandoval, J.: High-frequency trading strategy based on deep neural networks. In: Huang, D.-S., Han, K., Hussain, A. (eds.) ICIC 2016. LNCS (LNAI), vol. 9773, pp. 424\u2013436. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-42297-8_40"},{"issue":"3\u20134","key":"2_CR79","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":"2_CR80","doi-asserted-by":"crossref","unstructured":"Ghosal, D., Bhatnagar, S., Akhtar, M.S., Ekbal, A., Bhattacharyya, P.: IITP at SemEval-2017 Task 5: an ensemble of deep learning and feature based models for financial sentiment analysis. In: SemEval-2017, pp. 899\u2013903, August 2017","DOI":"10.18653\/v1\/S17-2154"},{"key":"2_CR81","doi-asserted-by":"crossref","unstructured":"Pivovarova, L., Escoter, L., Klami, A., Yangarber, R.: HCS at SemEval-2017 Task 5: sentiment detection in business news using convolutional neural networks. In: SemEval-2017, pp. 842\u2013846, August 2017","DOI":"10.18653\/v1\/S17-2143"},{"key":"2_CR82","doi-asserted-by":"crossref","unstructured":"Mansar, Y., Gatti, L., Ferradans, S., Guerini, M., Staiano, J.: Fortia-FBK at SemEval-2017 Task 5: bullish or Bearish? Inferring sentiment towards brands from financial news headlines. arXiv:1704.00939, April 2017","DOI":"10.18653\/v1\/S17-2138"},{"key":"2_CR83","doi-asserted-by":"crossref","unstructured":"Moore, A., Rayson, P.: Lancaster A at SemEval-2017 Task 5: evaluation metrics matter: predicting sentiment from financial news headlines. arXiv:1705.00571, May 2017","DOI":"10.18653\/v1\/S17-2095"},{"key":"2_CR84","doi-asserted-by":"crossref","unstructured":"Nelson, D., Pereira, A., de Oliveira, R.: Stock market\u2019s price movement prediction with LSTM neural networks. IJCNN 2017, May 2017","DOI":"10.1109\/IJCNN.2017.7966019"},{"key":"2_CR85","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1016\/S2212-5671(12)00122-0","volume":"3","author":"F Bertoluzzo","year":"2012","unstructured":"Bertoluzzo, F., Corazza, M.: Testing different reinforcement learning configurations for financial trading: introduction and applications. Procedia Econo. Finan. 3, 68\u201377 (2012)","journal-title":"Procedia Econo. Finan."},{"key":"2_CR86","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.eswa.2018.02.032","volume":"103","author":"PC Pendharkar","year":"2018","unstructured":"Pendharkar, P.C., Cusatis, P.: Trading financial indices with reinforcement learning agents. Expert Syst. Appl. 103, 1\u201313 (2018)","journal-title":"Expert Syst. Appl."},{"issue":"12","key":"2_CR87","doi-asserted-by":"publisher","first-page":"701","DOI":"10.1111\/lnc3.12228","volume":"10","author":"LM Rojas-Barahona","year":"2016","unstructured":"Rojas-Barahona, L.M.: Deep learning for sentiment analysis. Lang. Linguist. Compass 10(12), 701\u2013719 (2016)","journal-title":"Lang. Linguist. Compass"},{"key":"2_CR88","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1016\/j.knosys.2013.09.024","volume":"58","author":"V Loia","year":"2014","unstructured":"Loia, V., Senatore, S.: A fuzzy-oriented sentic analysis to capture the human emotion in Web-based content. Knowl.-Based Syst. 58, 75\u201385 (2014)","journal-title":"Knowl.-Based Syst."},{"key":"2_CR89","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1007\/978-3-030-37720-5_8","volume-title":"Mining Data for Financial Applications","author":"L Barbaglia","year":"2020","unstructured":"Barbaglia, L., Consoli, S., Manzan, S.: Monitoring the business cycle with fine-grained, aspect-based sentiment extraction from news. In: Bitetta, V., Bordino, I., Ferretti, A., Gullo, F., Pascolutti, S., Ponti, G. (eds.) MIDAS 2019. LNCS (LNAI), vol. 11985, pp. 101\u2013106. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-37720-5_8"},{"issue":"1","key":"2_CR90","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1111\/jofi.12109","volume":"69","author":"KR Ahern","year":"2014","unstructured":"Ahern, K.R., Sosyura, D.: Who writes the news? Corporate press releases during merger negotiations. J. Finan. 69(1), 241\u2013291 (2014)","journal-title":"J. Finan."},{"issue":"1","key":"2_CR91","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1016\/j.jfineco.2005.07.011","volume":"82","author":"C Vega","year":"2006","unstructured":"Vega, C.: Stock price reaction to public and private information. J. Financ. Econ. 82(1), 103\u2013133 (2006)","journal-title":"J. Financ. Econ."},{"issue":"2","key":"2_CR92","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1086\/295352","volume":"44","author":"V Niederhoffer","year":"1971","unstructured":"Niederhoffer, V.: The analysis of world events and stock prices. J. Bus. 44(2), 193\u2013219 (1971)","journal-title":"J. Bus."},{"key":"2_CR93","doi-asserted-by":"publisher","first-page":"534","DOI":"10.1016\/j.asoc.2015.07.008","volume":"36","author":"Y Hu","year":"2015","unstructured":"Hu, Y., Liu, K., Zhang, X., Su, L., Ngai, E.W.T., Liu, M.: Application of evolutionary computation for rule discovery in stock algorithmic trading: a literature review. Appl. Soft Comput. 36, 534\u2013551 (2015)","journal-title":"Appl. Soft Comput."},{"issue":"3","key":"2_CR94","doi-asserted-by":"publisher","first-page":"1139","DOI":"10.1111\/j.1540-6261.2007.01232.x","volume":"62","author":"PC Tetlock","year":"2007","unstructured":"Tetlock, P.C.: Giving content to investor sentiment: the role of media in the stock market. J. Finan. 62(3), 1139\u20131168 (2007)","journal-title":"J. Finan."},{"key":"2_CR95","doi-asserted-by":"crossref","unstructured":"Kim, Y.: Convolutional neural networks for sentence classification. arXiv:1408.5882, October 2014","DOI":"10.3115\/v1\/D14-1181"},{"issue":"4","key":"2_CR96","doi-asserted-by":"publisher","first-page":"917","DOI":"10.1007\/s10618-019-00619-1","volume":"33","author":"HI Fawaz","year":"2019","unstructured":"Fawaz, H.I., Forestier, G., Weber, J., Idoumghar, L., Muller, P.: Deep learning for time series classification: a review. Data Min. Knowl. Disc. 33(4), 917\u2013963 (2019)","journal-title":"Data Min. Knowl. Disc."},{"key":"2_CR97","unstructured":"Sutskever, I., Martens, J., Hinton, G.E.: Generating text with recurrent neural networks. In: ICML 2011, pp. 1017\u20131024 (2011)"},{"issue":"2","key":"2_CR98","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1109\/72.279181","volume":"5","author":"Y Bengio","year":"1994","unstructured":"Bengio, Y., Simard, P., Frasconi, P.: Learning long-term dependencies with gradient descent is difficult. IEEE Trans. Neural Netw. 5(2), 157\u2013166 (1994)","journal-title":"IEEE Trans. Neural Netw."},{"issue":"8","key":"2_CR99","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short term memory. Neural Comput. 9(8), 1735\u20131780 (1997)","journal-title":"Neural Comput."},{"key":"2_CR100","doi-asserted-by":"crossref","unstructured":"Cho, K., van Merrienboer, B., Gulcehre, C., Bougares, F., Schwenk, H., Bengio, Y.: Learning Phrase Representations using RNN encoder-decoder for statistical machine translation. arXiv:1406.1078, June 2014","DOI":"10.3115\/v1\/D14-1179"},{"key":"2_CR101","doi-asserted-by":"crossref","unstructured":"Hochreiter, S.: The Vanishing gradient problem during learning recurrent neural nets and problem solutions. In: IJUFKS 1998 6(2), pp. 107\u2013116 (1998)","DOI":"10.1142\/S0218488598000094"},{"key":"2_CR102","unstructured":"Lin, Z., et al.: A structured self-attentive sentence embedding. In: ICLR 2017, March 2017"},{"key":"2_CR103","unstructured":"Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv:1409.0473 (2014)"},{"key":"2_CR104","unstructured":"Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: NIPS 2014, pp. 3104\u20133112 (2014)"},{"key":"2_CR105","unstructured":"Vaswani, A., et al.: Attention is all you need. In: NIPS 2017, pp. 6000\u20136010, December 2017"}],"container-title":["Lecture Notes in Computer Science","Mining Data for Financial Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-66981-2_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,13]],"date-time":"2025-01-13T23:01:58Z","timestamp":1736809318000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-66981-2_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030669805","9783030669812"],"references-count":105,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-66981-2_2","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":"15 January 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MIDAS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Workshop on Mining Data for Financial Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ghent","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Belgium","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"midas2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/midas.portici.enea.it\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"15","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":"8","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":"3","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":"53% - 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":"1","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"The workshop was held virtually due to the COVID-19 pandemic.","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)"}}]}}