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[2]. Lo, Gaye-Del & Marcelin, Isaac & Bassène, Théophile & Sène, Babacar (2022) "The Russo-Ukrainian war and financial markets: the role of dependence on Russian commodities," Finance Research Letters, Elsevier, vol. 50(C).
[3]. Frederic S. Mishkin (2016) The Economics of Money, Banking, and Financial Markets (Eleventh Edition), Pearson.
[4]. Lo, G. D., Marcelin, I., Bassène, T., & Sène, B. (2022, December). The Russo-Ukrainian war and financial markets: the role of dependence on Russian commodities. Finance Research Letters, 50, 103194. https://doi.org/10.1016/j.frl.2022.103194
[5]. The Fed. (2022, June 21). The Fed - Monetary Policy: Monetary Policy Report. The Fed - Monetary Policy: Monetary Policy Report. Retrieved November 15, 2022, from https://www.federalreserve.gov/monetarypolicy/2022-06-mpr-summary.htm
[6]. deLisle, J. (2022). Deterrence Dilemmas and Alliance Dynamics: United States Policy on Cross-Strait Issues and the Implications of the War in Ukraine. American Journal of Chinese Studies, 29(2).
[7]. Lehmann, R. The Forecasting Power of the ifo Business Survey. J Bus Cycle Res (2022). https://doi.org/10.1007/s41549-022-00079-5
[8]. Hoekstra, Janny C., and Peter SH Leeflang. "Thriving through turbulence: Lessons from marketing academia and marketing practice." European Management Journal (2022).
[9]. Lo, G. D., Marcelin, I., Bassène, T., & Sène, B. (2022, December). The Russo-Ukrainian war and financial markets: the role of dependence on Russian commodities. Finance Research Letters, 50, 103194. https://doi.org/10.1016/j.frl.2022.103194
[10]. Zhang, T., Yang, K., Ji, S., & Ananiadou, S. (2023). Emotion fusion for mental illness detection from social media: A survey. Information Fusion, 92, 231-246.
[11]. Cui, Y., Jiang, Y., & Gu, H. (2023, January). Novel Sentiment Analysis from Twitter for Stock Change Prediction. In Data Mining and Big Data: 7th International Conference, DMBD 2022, Beijing, China, November 21–24, 2022, Proceedings, Part II (pp. 160-172). Singapore: Springer Nature Singapore.
[12]. Alslaity, A., & Orji, R. (2022). Machine learning techniques for emotion detection and sentiment analysis: current state, challenges, and future directions. Behaviour & Information Technology, 1-26.
[13]. Ruby, D., & About The Author Daniel Ruby Content writer with 10+ years of experience. I write across a range of subjects. (2023, March 1). 58+ twitter statistics for marketers in 2023 (Users & Trends). Demand Sage. Retrieved March 18, 2023, from https://www.demandsage.com/twitter-statistics/#:~:text=Twitter%20has%20around%20450%20million%20monthly%20active%20users%20as%20of%202023
[14]. Tiwari, A. K., Abakah, E. J. A., Bonsu, C. O., Karikari, N. K., & Hammoudeh, S. (2022). The effects of public sentiments and feelings on stock market behavior: Evidence from Australia. Journal of Economic Behavior & Organization, 193, 443-472.
[15]. Sawicki, G. S., Chilvers, M., McNamara, J., Naehrlich, L., Saunders, C., Sermet-Gaudelus, I., ... & Davies, J. C. (2022). A Phase 3, open-label, 96-week trial to study the safety, tolerability, and efficacy of tezacaftor/ivacaftor in children≥ 6 years of age homozygous for F508del or heterozygous for F508del and a residual function CFTR variant. Journal of Cystic Fibrosis, 21(4), 675-683.
[16]. Guzmán, R., & Morales, G. Discursive Strategies and Assessment in Turing test: A developmental analysis of L2 acquisition.
[17]. Fan, J., Campbell, M., & Kingsbury, B. (2011). Artificial intelligence research at IBM. IBM Journal of Research and Development, 55(5), 16-1.
[18]. Torfi, A., Shirvani, R. A., Keneshloo, Y., Tavaf, N., & Fox, E. A. (2020). Natural language processing advancements by deep learning: A survey. arXiv preprint arXiv:2003.01200.
[19]. Chen, S., Wang, C., Chen, Z., Wu, Y., Liu, S., Chen, Z., ... & Wei, F. (2022). Wavlm: Large-scale self-supervised pre-training for full stack speech processing. IEEE Journal of Selected Topics in Signal Processing, 16(6), 1505-1518.
[20]. Friederich, S. (2017). Fine-tuning.
[21]. Jolicoeur, P., & Jolicoeur, P. (1999). Fisher’s linear discriminant function. Introduction to biometry, 303-308.
[22]. Naïve Bayes Classifiers Revisited, Domingos, P., 1997.
[23]. A Few Useful Things to Know About Machine Learning, Pedro Domingos, 2012.
[24]. Gradient-based learning applied to document recognition, LeCun, Y. et al., 1998.
[25]. Long Short-Term Memory, Hochreiter, S. & Schmidhuber, J., 1997.
[26]. Random Forests, Breiman, L., 2001.
[27]. A Logical Calculus of the Ideas Immanent in Nervous Activity, McCulloch, W. S. & Pitts, W., 1943.
[28]. The perceptron: a probabilistic model for information storage and organization in the brain, Rosenblatt, F., 1958.
[29]. Some studies in machine learning using the game of checkers, Samuel, A. L., 1962.
[30]. Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences, Werbos, P. J., 1975.
[31]. Learning internal representations by error propagation, Rumelhart, D. E., Hinton, G. E. & Williams, R. J., 1986.
[32]. A Tutorial on Support Vector Machines for Pattern Recognition, Burges, C. J. C., 1998.
[33]. European Central Bank. (2020, January 8). ECB Monetary policy decisions. European Central Bank. Retrieved March 18, 2023, from https://www.ecb.europa.eu/press/govcdec/mopo/html/index.en.html
[34]. European Central Bank. (n.d.). ECB Latest Business and Consumer Surveys. Economy and Finance. Retrieved March 18, 2023, from https://economy-finance.ec.europa.eu/economic-forecast-and-surveys/business-and-consumer-surveys/latest-business-and-consumer-surveys_en
[35]. Salton, G., & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information Processing & Management, 24(5), 513-523.
[36]. A method of comparing two groups of learning, Cover, T. & Hart, P., 1967
[37]. Pu, Y., Apel, D. B., & Xu, H. (2019). Rockburst prediction in kimberlite with unsupervised learning method and support vector classifier. Tunnelling and Underground Space Technology, 90, 12-18.
[38]. Gibbs, M. N., & MacKay, D. J. (2000). Variational Gaussian process classifiers. IEEE Transactions on Neural Networks, 11(6), 1458-1464.
[39]. Du, W., & Zhan, Z. (2002). Building decision tree classifier on private data.
[40]. Rish, I. (2001, August). An empirical study of the naive Bayes classifier. In IJCAI 2001 workshop on empirical methods in artificial intelligence (Vol. 3, No. 22, pp. 41-46).
[41]. Ghojogh, B., & Crowley, M. (2019). Linear and quadratic discriminant analysis: Tutorial. arXiv preprint arXiv:1906.02590.
[42]. Rojas, R. (2009). AdaBoost and the super bowl of classifiers a tutorial introduction to adaptive boosting. Freie University, Berlin, Tech. Rep.
[43]. Hampshire II, J. B., & Pearlmutter, B. (1991). Equivalence proofs for multi-layer perceptron classifiers and the Bayesian discriminant function. In Connectionist Models (pp. 159-172). Morgan Kaufmann.
[44]. Kulkarni, V. Y., & Sinha, P. K. (2012, July). Pruning of random forest classifiers: A survey and future directions. In 2012 International Conference on Data Science & Engineering (ICDSE) (pp. 64-68). IEEE.
Cite this article
Su,Y.;Xiang,Y.;Lai,Y.;Liu,R. (2023). Implementing NLP techniques and data for Europe financial market forecasting. Applied and Computational Engineering,18,194-207.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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References
[1]. Susan V. Scott & Markos Zachariadis (2012) Origins and development of SWIFT, 1973–2009, Business History, 54:3, 462-482, DOI: 10.1080/00076791.2011.638502
[2]. Lo, Gaye-Del & Marcelin, Isaac & Bassène, Théophile & Sène, Babacar (2022) "The Russo-Ukrainian war and financial markets: the role of dependence on Russian commodities," Finance Research Letters, Elsevier, vol. 50(C).
[3]. Frederic S. Mishkin (2016) The Economics of Money, Banking, and Financial Markets (Eleventh Edition), Pearson.
[4]. Lo, G. D., Marcelin, I., Bassène, T., & Sène, B. (2022, December). The Russo-Ukrainian war and financial markets: the role of dependence on Russian commodities. Finance Research Letters, 50, 103194. https://doi.org/10.1016/j.frl.2022.103194
[5]. The Fed. (2022, June 21). The Fed - Monetary Policy: Monetary Policy Report. The Fed - Monetary Policy: Monetary Policy Report. Retrieved November 15, 2022, from https://www.federalreserve.gov/monetarypolicy/2022-06-mpr-summary.htm
[6]. deLisle, J. (2022). Deterrence Dilemmas and Alliance Dynamics: United States Policy on Cross-Strait Issues and the Implications of the War in Ukraine. American Journal of Chinese Studies, 29(2).
[7]. Lehmann, R. The Forecasting Power of the ifo Business Survey. J Bus Cycle Res (2022). https://doi.org/10.1007/s41549-022-00079-5
[8]. Hoekstra, Janny C., and Peter SH Leeflang. "Thriving through turbulence: Lessons from marketing academia and marketing practice." European Management Journal (2022).
[9]. Lo, G. D., Marcelin, I., Bassène, T., & Sène, B. (2022, December). The Russo-Ukrainian war and financial markets: the role of dependence on Russian commodities. Finance Research Letters, 50, 103194. https://doi.org/10.1016/j.frl.2022.103194
[10]. Zhang, T., Yang, K., Ji, S., & Ananiadou, S. (2023). Emotion fusion for mental illness detection from social media: A survey. Information Fusion, 92, 231-246.
[11]. Cui, Y., Jiang, Y., & Gu, H. (2023, January). Novel Sentiment Analysis from Twitter for Stock Change Prediction. In Data Mining and Big Data: 7th International Conference, DMBD 2022, Beijing, China, November 21–24, 2022, Proceedings, Part II (pp. 160-172). Singapore: Springer Nature Singapore.
[12]. Alslaity, A., & Orji, R. (2022). Machine learning techniques for emotion detection and sentiment analysis: current state, challenges, and future directions. Behaviour & Information Technology, 1-26.
[13]. Ruby, D., & About The Author Daniel Ruby Content writer with 10+ years of experience. I write across a range of subjects. (2023, March 1). 58+ twitter statistics for marketers in 2023 (Users & Trends). Demand Sage. Retrieved March 18, 2023, from https://www.demandsage.com/twitter-statistics/#:~:text=Twitter%20has%20around%20450%20million%20monthly%20active%20users%20as%20of%202023
[14]. Tiwari, A. K., Abakah, E. J. A., Bonsu, C. O., Karikari, N. K., & Hammoudeh, S. (2022). The effects of public sentiments and feelings on stock market behavior: Evidence from Australia. Journal of Economic Behavior & Organization, 193, 443-472.
[15]. Sawicki, G. S., Chilvers, M., McNamara, J., Naehrlich, L., Saunders, C., Sermet-Gaudelus, I., ... & Davies, J. C. (2022). A Phase 3, open-label, 96-week trial to study the safety, tolerability, and efficacy of tezacaftor/ivacaftor in children≥ 6 years of age homozygous for F508del or heterozygous for F508del and a residual function CFTR variant. Journal of Cystic Fibrosis, 21(4), 675-683.
[16]. Guzmán, R., & Morales, G. Discursive Strategies and Assessment in Turing test: A developmental analysis of L2 acquisition.
[17]. Fan, J., Campbell, M., & Kingsbury, B. (2011). Artificial intelligence research at IBM. IBM Journal of Research and Development, 55(5), 16-1.
[18]. Torfi, A., Shirvani, R. A., Keneshloo, Y., Tavaf, N., & Fox, E. A. (2020). Natural language processing advancements by deep learning: A survey. arXiv preprint arXiv:2003.01200.
[19]. Chen, S., Wang, C., Chen, Z., Wu, Y., Liu, S., Chen, Z., ... & Wei, F. (2022). Wavlm: Large-scale self-supervised pre-training for full stack speech processing. IEEE Journal of Selected Topics in Signal Processing, 16(6), 1505-1518.
[20]. Friederich, S. (2017). Fine-tuning.
[21]. Jolicoeur, P., & Jolicoeur, P. (1999). Fisher’s linear discriminant function. Introduction to biometry, 303-308.
[22]. Naïve Bayes Classifiers Revisited, Domingos, P., 1997.
[23]. A Few Useful Things to Know About Machine Learning, Pedro Domingos, 2012.
[24]. Gradient-based learning applied to document recognition, LeCun, Y. et al., 1998.
[25]. Long Short-Term Memory, Hochreiter, S. & Schmidhuber, J., 1997.
[26]. Random Forests, Breiman, L., 2001.
[27]. A Logical Calculus of the Ideas Immanent in Nervous Activity, McCulloch, W. S. & Pitts, W., 1943.
[28]. The perceptron: a probabilistic model for information storage and organization in the brain, Rosenblatt, F., 1958.
[29]. Some studies in machine learning using the game of checkers, Samuel, A. L., 1962.
[30]. Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences, Werbos, P. J., 1975.
[31]. Learning internal representations by error propagation, Rumelhart, D. E., Hinton, G. E. & Williams, R. J., 1986.
[32]. A Tutorial on Support Vector Machines for Pattern Recognition, Burges, C. J. C., 1998.
[33]. European Central Bank. (2020, January 8). ECB Monetary policy decisions. European Central Bank. Retrieved March 18, 2023, from https://www.ecb.europa.eu/press/govcdec/mopo/html/index.en.html
[34]. European Central Bank. (n.d.). ECB Latest Business and Consumer Surveys. Economy and Finance. Retrieved March 18, 2023, from https://economy-finance.ec.europa.eu/economic-forecast-and-surveys/business-and-consumer-surveys/latest-business-and-consumer-surveys_en
[35]. Salton, G., & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information Processing & Management, 24(5), 513-523.
[36]. A method of comparing two groups of learning, Cover, T. & Hart, P., 1967
[37]. Pu, Y., Apel, D. B., & Xu, H. (2019). Rockburst prediction in kimberlite with unsupervised learning method and support vector classifier. Tunnelling and Underground Space Technology, 90, 12-18.
[38]. Gibbs, M. N., & MacKay, D. J. (2000). Variational Gaussian process classifiers. IEEE Transactions on Neural Networks, 11(6), 1458-1464.
[39]. Du, W., & Zhan, Z. (2002). Building decision tree classifier on private data.
[40]. Rish, I. (2001, August). An empirical study of the naive Bayes classifier. In IJCAI 2001 workshop on empirical methods in artificial intelligence (Vol. 3, No. 22, pp. 41-46).
[41]. Ghojogh, B., & Crowley, M. (2019). Linear and quadratic discriminant analysis: Tutorial. arXiv preprint arXiv:1906.02590.
[42]. Rojas, R. (2009). AdaBoost and the super bowl of classifiers a tutorial introduction to adaptive boosting. Freie University, Berlin, Tech. Rep.
[43]. Hampshire II, J. B., & Pearlmutter, B. (1991). Equivalence proofs for multi-layer perceptron classifiers and the Bayesian discriminant function. In Connectionist Models (pp. 159-172). Morgan Kaufmann.
[44]. Kulkarni, V. Y., & Sinha, P. K. (2012, July). Pruning of random forest classifiers: A survey and future directions. In 2012 International Conference on Data Science & Engineering (ICDSE) (pp. 64-68). IEEE.