{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T14:22:21Z","timestamp":1778077341843,"version":"3.51.4"},"reference-count":68,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2022,12,13]],"date-time":"2022-12-13T00:00:00Z","timestamp":1670889600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,12,13]],"date-time":"2022-12-13T00:00:00Z","timestamp":1670889600000},"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":["Ann Oper Res"],"published-print":{"date-parts":[[2025,9]]},"DOI":"10.1007\/s10479-022-05101-8","type":"journal-article","created":{"date-parts":[[2022,12,13]],"date-time":"2022-12-13T08:03:04Z","timestamp":1670918584000},"page":"745-780","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Improving data efficiency for analyzing global exchange rate fluctuations based on nonlinear causal network-based clustering"],"prefix":"10.1007","volume":"352","author":[{"given":"Insu","family":"Choi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wonje","family":"Yun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8385-9598","authenticated-orcid":false,"given":"Woo Chang","family":"Kim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,12,13]]},"reference":[{"key":"5101_CR1","doi-asserted-by":"crossref","unstructured":"Abbasi, R. A., Javaid, N., Ghuman, M. N. J., Khan, Z. A., Ur Rehman, S., & Amanullah. (2019). Short term load forecasting using xgboost. L. Barolli, M. Takizawa, F. Xhafa, & T. Enokido (Eds.), Web, artificial intelligence and network applications (pp. 1120\u20131131). Cham: Springer International Publishing.","DOI":"10.1007\/978-3-030-15035-8_108"},{"issue":"3","key":"5101_CR2","doi-asserted-by":"crossref","first-page":"1105","DOI":"10.1007\/s10957-011-9968-2","volume":"155","author":"A Ahmadi-Javid","year":"2012","unstructured":"Ahmadi-Javid, A. (2012). Entropic value-at-risk: A new coherent risk measure. Journal of Optimization Theory and Applications, 155(3), 1105\u20131123.","journal-title":"Journal of Optimization Theory and Applications"},{"key":"5101_CR3","unstructured":"Antweiler, W. (2008). Pacific exchange rate service-database retrieval system. Available at http:\/\/fx.sauder.ubc.ca\/data.html."},{"issue":"3","key":"5101_CR4","doi-asserted-by":"crossref","first-page":"945","DOI":"10.1016\/j.ejor.2016.06.052","volume":"256","author":"S Bekiros","year":"2017","unstructured":"Bekiros, S., Nguyen, D. K., Junior, L. S., & Uddin, G. S. (2017). Information diffusion, cluster formation and entropy-based network dynamics in equity and commodity markets. European Journal of Operational Research, 256(3), 945\u2013961.","journal-title":"European Journal of Operational Research"},{"key":"5101_CR5","doi-asserted-by":"crossref","unstructured":"Belhadi, A., Kamble, S. S., Mani, V., Benkhati, I., & Touriki, F. E. (2021). An ensemble machine learning approach for forecasting credit risk of agricultural SMEs\u2019 investments in agriculture 4.0 through supply chain finance. Annals of Operations Research, 1\u201329.","DOI":"10.1007\/s10479-021-04366-9"},{"issue":"2","key":"5101_CR6","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1111\/1467-646X.00056","volume":"11","author":"H Berkman","year":"2000","unstructured":"Berkman, H., Bradbury, M. E., & Ferguson, J. (2000). The accuracy of priceearnings and discounted cash flow methods of IPO equity valuation. Journal of International Financial Management & Accounting, 11(2), 71\u201383.","journal-title":"Journal of International Financial Management & Accounting"},{"key":"5101_CR7","unstructured":"Board of Governors of the Federal Reserve System (2021). H.10 brazil historical rates. Available at https:\/\/www.federalreserve.gov\/releases\/h10\/hist\/dat96 bz.htm (1999\/12\/31)."},{"key":"5101_CR8","doi-asserted-by":"crossref","first-page":"10","DOI":"10.3389\/fphy.2015.00010","volume":"3","author":"P Boba","year":"2015","unstructured":"Boba, P., Bollmann, D., Schoepe, D., Wester, N., Wiesel, J., & Hamacher, K. (2015). Efficient computation and statistical assessment of transfer entropy. Frontiers in Physics, 3, 10.","journal-title":"Frontiers in Physics"},{"key":"5101_CR9","doi-asserted-by":"crossref","unstructured":"Boehmke, B., & Greenwell, B. (2019). Hands-on machine learning with R. Chapman and Hall\/CRC.","DOI":"10.1201\/9780367816377"},{"key":"5101_CR10","unstructured":"Bohdalov\u00e1, M. (2007). A comparison of value-at-risk methods for measurement of the financial risk (p. 10). Faculty of Management: Comenius University, Bratislava, Slovakia."},{"key":"5101_CR11","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.chaos.2017.03.039","volume":"99","author":"G Cao","year":"2017","unstructured":"Cao, G., Zhang, Q., & Li, Q. (2017). Causal relationship between the global foreign exchange market based on complex networks and entropy theory. Chaos, Solitons & Fractals, 99, 36\u201344.","journal-title":"Chaos, Solitons & Fractals"},{"key":"5101_CR12","unstructured":"Central Bank of Kuwait (2021). Foreign currencies exchange rates. Available at https:\/\/www.cbk.gov.kw\/en\/monetary-policy\/market-operations\/exchange-rates."},{"key":"5101_CR13","unstructured":"Central Bank of Sri Lanka (2021). Exchange rates: Central bank of Sri Lanka. Available at https:\/\/www.cbsl.gov.lk\/en\/rates-and-indicators\/exchange-rates."},{"key":"5101_CR14","doi-asserted-by":"crossref","unstructured":"Chen, T., & Guestrin, C. (2016). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining (pp. 785\u2013794).","DOI":"10.1145\/2939672.2939785"},{"key":"5101_CR15","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.datak.2018.08.003","volume":"118","author":"W Chen","year":"2018","unstructured":"Chen, W., Yeo, C. K., Lau, C. T., & Lee, B. S. (2018). Leveraging social media news to predict stock index movement using RNN-boost. Data & Knowledge Engineering, 118, 14\u201324.","journal-title":"Data & Knowledge Engineering"},{"key":"5101_CR16","unstructured":"Chile Banco Central (2021). Exchange rates-observed dollar. Available at https:\/\/si3.bcentral.cl\/Siete\/ES\/Siete\/Cuadro\/CAP_TIPO_CAMBIO\/MN TIPO_CAMBIO4\/DOLAR_OBS_ADO\/TCB_505."},{"issue":"6","key":"5101_CR17","doi-asserted-by":"crossref","first-page":"734","DOI":"10.3390\/e23060734","volume":"23","author":"I Choi","year":"2021","unstructured":"Choi, I., & Kim, W. C. (2021). Detecting and analyzing politically-themed stocks using text mining techniques and transfer entropy|focus on the republic of korea\u2019s case. Entropy, 23(6), 734.","journal-title":"Entropy"},{"key":"5101_CR18","doi-asserted-by":"crossref","DOI":"10.1017\/CBO9780511801389","volume-title":"An introduction to support vector machines and other kernel-based learning methods","author":"N Cristianini","year":"2000","unstructured":"Cristianini, N., Shawe-Taylor, J., et al. (2000). An introduction to support vector machines and other kernel-based learning methods. Cambridge: Cambridge University Press."},{"key":"5101_CR19","doi-asserted-by":"crossref","unstructured":"Dimpfl, T., & Peter, F. J. (2013). Using transfer entropy to measure information flows between financial markets. Studies in Nonlinear Dynamics and Econometrics, 17(1), 85\u2013102.","DOI":"10.1515\/snde-2012-0044"},{"key":"5101_CR20","doi-asserted-by":"crossref","unstructured":"Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 1189\u20131232.","DOI":"10.1214\/aos\/1013203451"},{"key":"5101_CR21","doi-asserted-by":"crossref","first-page":"446","DOI":"10.1016\/j.neucom.2015.03.100","volume":"172","author":"S Galeshchuk","year":"2016","unstructured":"Galeshchuk, S. (2016). Neural networks performance in exchange rate prediction. Neurocomputing, 172, 446\u2013452.","journal-title":"Neurocomputing"},{"key":"5101_CR22","doi-asserted-by":"crossref","unstructured":"Granger, C. W. (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica: Journal of the Econometric Society, 424\u2013438.","DOI":"10.2307\/1912791"},{"key":"5101_CR23","doi-asserted-by":"crossref","unstructured":"Gumus, M., & Kiran, M. S. (2017). Crude oil price forecasting using xgboost. In 2017 international conference on computer science and engineering (UBMK) (pp. 1100\u20131103).","DOI":"10.1109\/UBMK.2017.8093500"},{"key":"5101_CR24","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1007\/s10479-020-03856-6","volume":"308","author":"S Gupta","year":"2022","unstructured":"Gupta, S., Modgil, S., Bhattacharyya, S., & Bose, I. (2022). Artificial intelligence for decision support systems in the field of operations research: Review and future scope of research. Annals of Operations Research, 308, 215\u2013274.","journal-title":"Annals of Operations Research"},{"key":"5101_CR25","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.patrec.2017.11.007","volume":"101","author":"A Hacine-Gharbi","year":"2018","unstructured":"Hacine-Gharbi, A., & Ravier, P. (2018). A binning formula of bi-histogram for joint entropy estimation using mean square error minimization. Pattern Recognition Letters, 101, 21\u201328.","journal-title":"Pattern Recognition Letters"},{"issue":"10","key":"5101_CR26","doi-asserted-by":"crossref","first-page":"1302","DOI":"10.1016\/j.patrec.2012.02.022","volume":"33","author":"A Hacine-Gharbi","year":"2012","unstructured":"Hacine-Gharbi, A., Ravier, P., Harba, R., & Mohamadi, T. (2012). Low bias histogram-based estimation of mutual information for feature selection. Pattern Recognition Letters, 33(10), 1302\u20131308.","journal-title":"Pattern Recognition Letters"},{"key":"5101_CR27","doi-asserted-by":"crossref","unstructured":"Hastie, T., Tibshirani, R., Friedman, J. H., & Friedman, J. H. (2009). The elements of statistical learning: Data mining, inference, and prediction (Vol. 2). Springer.","DOI":"10.1007\/978-0-387-84858-7"},{"key":"5101_CR28","doi-asserted-by":"crossref","unstructured":"He, T., & Droppo, J. (2016). Exploiting LSTM structure in deep neural networks for speech recognition. In 2016 IEEE international conference on acoustics, speech and signal processing (ICASSP) (pp. 5445\u20135449).","DOI":"10.1109\/ICASSP.2016.7472718"},{"key":"5101_CR29","doi-asserted-by":"crossref","unstructured":"Jabeur, S. B., Mefteh-Wali, S., & Viviani, J.-L. (2021). Forecasting gold price with the xgboost algorithm and shap interaction values. Annals of Operations Research, 1\u201321.","DOI":"10.1007\/s10479-021-04187-w"},{"issue":"6","key":"5101_CR30","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s40314-021-01606-3","volume":"40","author":"C Jana","year":"2021","unstructured":"Jana, C. (2021). Multiple attribute group decision-making method based on extended bipolar fuzzy mabac approach. Computational and Applied Mathematics, 40(6), 1\u201317.","journal-title":"Computational and Applied Mathematics"},{"issue":"6","key":"5101_CR31","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s40314-021-01606-3","volume":"40","author":"C Jana","year":"2021","unstructured":"Jana, C. (2021). Multiple attribute group decision-making method based on extended bipolar fuzzy mabac approach. Computational and Applied Mathematics, 40(6), 1\u201317.","journal-title":"Computational and Applied Mathematics"},{"issue":"5","key":"5101_CR32","doi-asserted-by":"crossref","first-page":"3685","DOI":"10.1007\/s10462-020-09936-0","volume":"54","author":"C Jana","year":"2021","unstructured":"Jana, C., Muhiuddin, G., & Pal, M. (2021). Multi-criteria decision making approach based on svtrn dombi aggregation functions. Artificial Intelligence Review, 54(5), 3685\u20133723.","journal-title":"Artificial Intelligence Review"},{"key":"5101_CR33","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2021.104203","volume":"100","author":"C Jana","year":"2021","unstructured":"Jana, C., & Pal, M. (2021). A dynamical hybrid method to design decision making process based on GRA approach for multiple attributes problem. Engineering Applications of Artificial Intelligence, 100, 104203.","journal-title":"Engineering Applications of Artificial Intelligence"},{"key":"5101_CR34","volume":"3","author":"DK Jana","year":"2022","unstructured":"Jana, D. K., Bhunia, P., Adhikary, S. D., & Bej, B. (2022). Optimization of effluents using artificial neural network and support vector regression in detergent industrial wastewater treatment. Cleaner Chemical Engineering, 3, 100039.","journal-title":"Cleaner Chemical Engineering"},{"key":"5101_CR35","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/j.jisa.2018.04.002","volume":"40","author":"DK Jana","year":"2018","unstructured":"Jana, D. K., & Ghosh, R. (2018). Novel interval type-2 fuzzy logic controller for improving risk assessment model of cyber security. Journal of Information Security and Applications, 40, 173\u2013182.","journal-title":"Journal of Information Security and Applications"},{"issue":"11","key":"5101_CR36","doi-asserted-by":"crossref","first-page":"1116","DOI":"10.3390\/e21111116","volume":"21","author":"SM Jang","year":"2019","unstructured":"Jang, S. M., Yi, E., Kim, W. C., & Ahn, K. (2019). Information flow between bitcoin and other investment assets. Entropy, 21(11), 1116.","journal-title":"Entropy"},{"key":"5101_CR37","first-page":"3146","volume":"30","author":"G Ke","year":"2017","unstructured":"Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., & Liu, T.-Y. (2017). Lightgbm: A highly efficient gradient boosting decision tree. Advances in Neural Information Processing Systems, 30, 3146\u20133154.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"5101_CR38","doi-asserted-by":"crossref","DOI":"10.1016\/j.dsp.2019.102581","volume":"95","author":"KH Knuth","year":"2019","unstructured":"Knuth, K. H. (2019). Optimal data-based binning for histograms and histogram-based probability density models. Digital Signal Processing, 95, 102581.","journal-title":"Digital Signal Processing"},{"key":"5101_CR39","unstructured":"Korea Economic Statistics System (2021). The US dollar exchange rate of major currencies. Available at https:\/\/ecos.bok.or.kr\/."},{"key":"5101_CR40","volume-title":"Social network analysis","author":"K-Y Kwak","year":"2017","unstructured":"Kwak, K.-Y. (2017). Social network analysis. Seoul: ChungRam."},{"issue":"12","key":"5101_CR41","doi-asserted-by":"crossref","first-page":"2851","DOI":"10.1016\/j.physa.2008.01.007","volume":"387","author":"O Kwon","year":"2008","unstructured":"Kwon, O., & Yang, J.-S. (2008). Information flow between composite stock index and individual stocks. Physica A: Statistical Mechanics and its Applications, 387(12), 2851\u20132856.","journal-title":"Physica A: Statistical Mechanics and its Applications"},{"key":"5101_CR42","volume-title":"Network analysis methodology","author":"S Lee","year":"2013","unstructured":"Lee, S. (2013). Network analysis methodology. Seoul: NonHyung."},{"key":"5101_CR43","unstructured":"Li, S. Z. (2009). Encyclopedia of biometrics: I-z. (Vol. 2). Springer Science & Business Media."},{"key":"5101_CR44","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1016\/j.physa.2017.05.084","volume":"486","author":"K Lim","year":"2017","unstructured":"Lim, K., Kim, S., & Kim, S. Y. (2017). Information transfer across intra\/interstructure of CDS and stock markets. Physica A: Statistical Mechanics and its Applications, 486, 118\u2013126.","journal-title":"Physica A: Statistical Mechanics and its Applications"},{"issue":"1","key":"5101_CR45","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/S0167-9473(98)00067-X","volume":"30","author":"F Lisi","year":"1999","unstructured":"Lisi, F., & Schiavo, R. A. (1999). A comparison between neural networks and chaotic models for exchange rate prediction. Computational Statistics & Data Analysis, 30(1), 87\u2013102.","journal-title":"Computational Statistics & Data Analysis"},{"issue":"2","key":"5101_CR46","doi-asserted-by":"crossref","first-page":"851","DOI":"10.1007\/s10614-017-9768-3","volume":"53","author":"J Liu","year":"2019","unstructured":"Liu, J., Wu, C., & Li, Y. (2019). Improving financial distress prediction using financial network-based information and GA-based gradient boosting method. Computational Economics, 53(2), 851\u2013872.","journal-title":"Computational Economics"},{"key":"5101_CR47","doi-asserted-by":"crossref","unstructured":"Liu, L., Ye, Y.-t., Xie, Y., & Pu, L. (2010). Serial number extracting and recognizing applied in paper currency sorting system based on RBF network. In 2010 international conference on computational intelligence and software engineering (pp. 1\u20134).","DOI":"10.1109\/CISE.2010.5677049"},{"issue":"2","key":"5101_CR48","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1140\/epjb\/e2002-00379-2","volume":"30","author":"R Marschinski","year":"2002","unstructured":"Marschinski, R., & Kantz, H. (2002). Analysing the information flow between financial time series. The European Physical Journal B-Condensed Matter and Complex Systems, 30(2), 275\u2013281.","journal-title":"The European Physical Journal B-Condensed Matter and Complex Systems"},{"key":"5101_CR49","doi-asserted-by":"crossref","first-page":"984","DOI":"10.1016\/j.physa.2017.09.091","volume":"491","author":"B Mo","year":"2018","unstructured":"Mo, B., Nie, H., & Jiang, Y. (2018). Dynamic linkages among the gold market, us dollar and crude oil market. Physica A: Statistical Mechanics and its Applications, 491, 984\u2013994.","journal-title":"Physica A: Statistical Mechanics and its Applications"},{"key":"5101_CR50","unstructured":"Molnar, C. (2018). A guide for making black box models explainable. Available at https:\/\/christophm.github.io\/interpretable-ml-book."},{"key":"5101_CR51","doi-asserted-by":"crossref","first-page":"647","DOI":"10.1016\/j.procs.2019.01.189","volume":"147","author":"L Ni","year":"2019","unstructured":"Ni, L., Li, Y., Wang, X., Zhang, J., Yu, J., & Qi, C. (2019). Forecasting of forex time series data based on deep learning. Procedia Computer Science, 147, 647\u2013652.","journal-title":"Procedia Computer Science"},{"key":"5101_CR52","unstructured":"Page, L., Brin, S., Motwani, R., & Winograd, T. (1999). The pagerank citation ranking: Bringing order to the web. (Technical Report). Stanford InfoLab."},{"key":"5101_CR53","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., & Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, 2825\u20132830.","journal-title":"Journal of Machine Learning Research"},{"issue":"3","key":"5101_CR54","first-page":"61","volume":"10","author":"J Platt","year":"1999","unstructured":"Platt, J., et al. (1999). Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Advances in Large Margin Classifiers, 10(3), 61\u201374.","journal-title":"Advances in Large Margin Classifiers"},{"key":"5101_CR55","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.physa.2016.03.006","volume":"456","author":"MM Rounaghi","year":"2016","unstructured":"Rounaghi, M. M., & Zadeh, F. N. (2016). Investigation of market efficiency and financial stability between s &p 500 and London stock exchange: Monthly and yearly forecasting of time series stock returns using arma model. Physica A: Statistical Mechanics and its Applications, 456, 10\u201321.","journal-title":"Physica A: Statistical Mechanics and its Applications"},{"key":"5101_CR56","doi-asserted-by":"crossref","unstructured":"Sayavong, L., Wu, Z., & Chalita, S. (2019). Research on stock price prediction method based on convolutional neural network. In 2019 international conference on virtual reality and intelligent systems (ICVRIS) (pp. 173\u2013176).","DOI":"10.1109\/ICVRIS.2019.00050"},{"issue":"2","key":"5101_CR57","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1103\/PhysRevLett.85.461","volume":"85","author":"T Schreiber","year":"2000","unstructured":"Schreiber, T. (2000). Measuring information transfer. Physical Review Letters, 85(2), 461.","journal-title":"Physical Review Letters"},{"key":"5101_CR58","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.chaos.2014.08.007","volume":"68","author":"A Sensoy","year":"2014","unstructured":"Sensoy, A., Sobaci, C., Sensoy, S., & Alali, F. (2014). Effective transfer entropy approach to information flow between exchange rates and stock markets. Chaos, Solitons & Fractals, 68, 180\u2013185.","journal-title":"Chaos, Solitons & Fractals"},{"issue":"1","key":"5101_CR59","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1007\/s10479-021-04183-0","volume":"306","author":"E S\u00e9verin","year":"2021","unstructured":"S\u00e9verin, E., & Veganzones, D. (2021). Can earnings management information improve bankruptcy prediction models? Annals of Operations Research, 306(1), 247\u2013272.","journal-title":"Annals of Operations Research"},{"issue":"3","key":"5101_CR60","doi-asserted-by":"crossref","first-page":"8","DOI":"10.3905\/JAI.2010.12.3.008","volume":"12","author":"AZ Sheikh","year":"2009","unstructured":"Sheikh, A. Z., & Qiao, H. (2009). Non-normality of market returns: A framework for asset allocation decision making. The Journal of Alternative Investments, 12(3), 8\u201335.","journal-title":"The Journal of Alternative Investments"},{"key":"5101_CR61","volume":"32","author":"X Sun","year":"2020","unstructured":"Sun, X., Liu, M., & Sima, Z. (2020). A novel cryptocurrency price trend forecasting model based on lightgbm. Finance Research Letters, 32, 101084.","journal-title":"Finance Research Letters"},{"key":"5101_CR62","unstructured":"Tsai, C.S.-Y. (2011). The real world is not normal. IL, USA: Morningstar Alternative Investments Observer:Chicago."},{"key":"5101_CR63","unstructured":"World Gold Council (2021). Gold price historical data: Gold price history. Available at https:\/\/www.gold.org\/goldhub\/data\/gold-prices (2021\/09)."},{"key":"5101_CR64","doi-asserted-by":"crossref","first-page":"13066","DOI":"10.1109\/ACCESS.2020.2966278","volume":"8","author":"P Yue","year":"2020","unstructured":"Yue, P., Cai, Q., Yan, W., & Zhou, W.-X. (2020). Information flow networks of Chinese stock market sectors. IEEE Access, 8, 13066\u201313077.","journal-title":"IEEE Access"},{"issue":"2","key":"5101_CR65","doi-asserted-by":"crossref","first-page":"194","DOI":"10.3390\/e22020194","volume":"22","author":"P Yue","year":"2020","unstructured":"Yue, P., Fan, Y., Batten, J. A., & Zhou, W.-X. (2020). Information transfer between stock market sectors: A comparison between the USA and China. Entropy, 22(2), 194.","journal-title":"Entropy"},{"key":"5101_CR66","volume":"186","author":"KK Yun","year":"2021","unstructured":"Yun, K. K., Yoon, S. W., & Won, D. (2021). Prediction of stock price direction using a hybrid GA-xgboost algorithm with a three-stage feature engineering process. Expert Systems with Applications, 186, 115716.","journal-title":"Expert Systems with Applications"},{"key":"5101_CR67","doi-asserted-by":"crossref","unstructured":"Zeng, Z., & Khushi, M. (2020). Wavelet denoising and attention-based rnnarima model to predict forex price. In 2020 international joint conference on neural networks (IJCNN) (pp. 1\u20137).","DOI":"10.1109\/IJCNN48605.2020.9206832"},{"key":"5101_CR68","doi-asserted-by":"crossref","unstructured":"Zhukov, M., & Popov, A. (2014). Bin number selection for equidistant mutual information estimaton. In 2014 IEEE 34th international scientific conference on electronics and nanotechnology (ELNANO) (pp. 259\u2013263).","DOI":"10.1109\/ELNANO.2014.6873919"}],"container-title":["Annals of Operations Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10479-022-05101-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10479-022-05101-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10479-022-05101-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T09:56:23Z","timestamp":1758621383000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10479-022-05101-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,13]]},"references-count":68,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2025,9]]}},"alternative-id":["5101"],"URL":"https:\/\/doi.org\/10.1007\/s10479-022-05101-8","relation":{},"ISSN":["0254-5330","1572-9338"],"issn-type":[{"value":"0254-5330","type":"print"},{"value":"1572-9338","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,13]]},"assertion":[{"value":"15 November 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 December 2022","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no relevant financial or non-financial interests to disclose.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}