{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T17:22:49Z","timestamp":1782840169060,"version":"3.54.5"},"reference-count":83,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2019,10,18]],"date-time":"2019-10-18T00:00:00Z","timestamp":1571356800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003802","name":"University Research Committee, University of Hong Kong","doi-asserted-by":"publisher","award":["CityU 11300717"],"award-info":[{"award-number":["CityU 11300717"]}],"id":[{"id":"10.13039\/501100003802","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Axioms"],"abstract":"<jats:p>Financial time-series are well known for their non-linearity and non-stationarity nature. The application of conventional econometric models in prediction can incur significant errors. The fast advancement of soft computing techniques provides an alternative approach for estimating and forecasting volatile stock prices. Soft computing approaches exploit tolerance for imprecision, uncertainty, and partial truth to progressively and adaptively solve practical problems. In this study, a comprehensive review of latest soft computing tools is given. Then, examples incorporating a series of machine learning models, including both single and hybrid models, to predict prices of two representative indexes and one stock in Hong Kong\u2019s market are undertaken. The prediction performances of different models are evaluated and compared. The effects of the training sample size and stock patterns (viz. momentum and mean reversion) on model prediction are also investigated. Results indicate that artificial neural network (ANN)-based models yield the highest prediction accuracy. It was also found that the determination of optimal training sample size should take the pattern and volatility of stocks into consideration. Large prediction errors could be incurred when stocks exhibit a transition between mean reversion and momentum trend.<\/jats:p>","DOI":"10.3390\/axioms8040116","type":"journal-article","created":{"date-parts":[[2019,10,18]],"date-time":"2019-10-18T11:24:15Z","timestamp":1571397855000},"page":"116","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["A Study Concerning Soft Computing Approaches for Stock Price Forecasting"],"prefix":"10.3390","volume":"8","author":[{"given":"Chao","family":"Shi","sequence":"first","affiliation":[{"name":"Department of Mathematics, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5051-4710","authenticated-orcid":false,"given":"Xiaosheng","family":"Zhuang","sequence":"additional","affiliation":[{"name":"Department of Mathematics, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,10,18]]},"reference":[{"key":"ref_1","unstructured":"Malkiel, B.G. (1999). A Random Walk down Wall Street: Including a Life-Cycle Guide to Personal Investing, WW Norton & Company. [7th ed.]."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1111\/j.1540-6261.1970.tb00518.x","article-title":"Efficient capital markets: A review of theory and empirical work","volume":"25","author":"Malkiel","year":"1970","journal-title":"J. Financ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1575","DOI":"10.1111\/j.1540-6261.1991.tb04636.x","article-title":"Efficient capital markets: II","volume":"46","author":"Fama","year":"1991","journal-title":"J. Financ."},{"key":"ref_4","first-page":"1","article-title":"Wavelets in economics and finance: Past and future","volume":"6","author":"Ramsey","year":"2002","journal-title":"Stud. Nonlinear Dyn. Econom."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"10696","DOI":"10.1016\/j.eswa.2009.02.043","article-title":"Forecasting stock market short-term trends using a neuro-fuzzy based methodology","volume":"36","author":"Atsalakis","year":"2009","journal-title":"Expert Syst. Appl."},{"key":"ref_6","unstructured":"Chollet, F. (2018). Deep Learning with Python, Manning Publications Co.. [1st ed.]."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"6668","DOI":"10.1016\/j.eswa.2008.08.019","article-title":"An empirical methodology for developing stockmarket trading systems using artificial neural networks","volume":"36","author":"Vanstone","year":"2009","journal-title":"Expert Syst. Appl."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/j.eswa.2016.09.027","article-title":"Forecasting daily stock market return using dimensionality reduction","volume":"67","author":"Zhong","year":"2017","journal-title":"Expert Syst. Appl."},{"key":"ref_9","unstructured":"Fabozzi, F.J., Focardi, S.M., and Kolm, P.N. (2010). Quantitative Equity Investing: Techniques and Strategies, John Wiley & Sons. [1st ed.]."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Cochrane, J.H. (1999). New Facts in Finance, Federal Reserve Bank of Chicago.","DOI":"10.3386\/w7169"},{"key":"ref_11","unstructured":"Devendra, K. (2008). Soft Computing: Techniques and Its Applications in Electrical Engineering, Springer."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1447","DOI":"10.1109\/TIE.2016.2613974","article-title":"A data-driven fuzzy information granulation approach for freight volume forecasting","volume":"64","author":"Yin","year":"2016","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2849","DOI":"10.1109\/TII.2018.2875067","article-title":"Recent advances in key-performance-indicator oriented prognosis and diagnosis with a matlab toolbox: Db-kit","volume":"15","author":"Jiang","year":"2018","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Choudhary, A., Upadhyay, K., and Tripathi, M. (2012, January 19\u201322). Soft computing applications in wind speed and power prediction for wind energy. Proceedings of the 2012 IEEE Fifth Power India Conference, Murthal, India.","DOI":"10.1109\/PowerI.2012.6479588"},{"key":"ref_15","first-page":"987","article-title":"Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation","volume":"50","author":"Engle","year":"1982","journal-title":"Econom. J. Econom. Soc."},{"key":"ref_16","first-page":"45","article-title":"Surveying stock market forecasting techniques-Part I: Conventional methods","volume":"2","author":"Atsalakis","year":"2010","journal-title":"J. Comput. Optim. Econ. Financ."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"De Luca, G., Genton, M.G., and Loperfido, N. (2004). A skew-in-mean GARCH model for financial returns. Skew-Elliptical Distributions and Their Applications: A Journey Beyond Normality, CRC\/Chapman & Hall.","DOI":"10.1201\/9780203492000.ch12"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1214\/ss\/1009213726","article-title":"Statistical modeling: The two cultures","volume":"16","author":"Breiman","year":"2001","journal-title":"Stat. Sci."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1016\/j.eswa.2016.01.018","article-title":"Evaluating machine learning classification for financial trading: An empirical approach","volume":"54","author":"Gerlein","year":"2016","journal-title":"Expert Syst. Appl."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"64","DOI":"10.3905\/jfds.2019.1.064","article-title":"A backtesting protocol in the era of machine learning","volume":"1","author":"Arnott","year":"2019","journal-title":"J. Financ. Data Sci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1080\/14697680600969727","article-title":"Multi-scaling in finance","volume":"7","year":"2007","journal-title":"Quant. Financ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2623","DOI":"10.1016\/j.eneco.2008.05.003","article-title":"Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm","volume":"30","author":"Yu","year":"2008","journal-title":"Energy Econ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"5932","DOI":"10.1016\/j.eswa.2008.07.006","article-title":"Surveying stock market forecasting techniques\u2013Part II: Soft computing methods","volume":"36","author":"Atsalakis","year":"2009","journal-title":"Expert Syst. Appl."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1165","DOI":"10.1007\/s00521-010-0362-z","article-title":"A comparative survey of artificial intelligence applications in finance: Artificial neural networks, expert system and hybrid intelligent systems","volume":"19","author":"Bahrammirzaee","year":"2010","journal-title":"Neural Comput. Appl."},{"key":"ref_25","unstructured":"Terrell, D. (2006). A Multivariate Skew-Garch Model. Advances in Econometrics: Econometric Analysis of Economic and Financial Time Series, Elsevier. Part A (Special volume in honor of Robert Engle and Clive Granger, the 2003 Winners of the Nobel Prize in Economics)."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Hassan, M.R., and Nath, B. (2005, January 8\u201310). Stock market forecasting using hidden Markov model: A new approach. Proceedings of the 5th International Conference on Intelligent Systems Design and Applications (ISDA\u201905), Warsaw, Poland.","DOI":"10.1109\/ISDA.2005.85"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"3439","DOI":"10.1016\/j.neucom.2008.09.029","article-title":"A combination of hidden Markov model and fuzzy model for stock market forecasting","volume":"72","author":"Hassan","year":"2009","journal-title":"Neurocomputing"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/j.eswa.2006.04.007","article-title":"A fusion model of HMM, ANN and GA for stock market forecasting","volume":"33","author":"Hassan","year":"2007","journal-title":"Expert Syst. Appl."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Gupta, A., and Dhingra, B. (2012, January 16\u201318). Stock market prediction using hidden markov models. Proceedings of the 2012 Students Conference on Engineering and Systems, Allahabad, India.","DOI":"10.1109\/SCES.2012.6199099"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1071","DOI":"10.1007\/s10115-018-1315-6","article-title":"Enhancing stock market prediction with extended coupled hidden Markov model over multi-sourced data","volume":"61","author":"Zhang","year":"2018","journal-title":"Knowl. Inf. Syst."},{"key":"ref_31","unstructured":"Schmidt, M. (1996, January 9\u201313). Identifying speaker with support vector networks. Proceedings of the Interface \u201996 Sydney, Sydney, Australia."},{"key":"ref_32","unstructured":"Vapnik, V. (2013). The Nature of Statistical Learning Theory, Springer Science & Business Media. [2nd ed.]."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1016\/S0925-2312(03)00372-2","article-title":"Financial time series forecasting using support vector machines","volume":"55","author":"Kim","year":"2003","journal-title":"Neurocomputing"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1016\/S0305-0483(01)00026-3","article-title":"Application of support vector machines in financial time series forecasting","volume":"29","author":"Tay","year":"2001","journal-title":"Omega"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2513","DOI":"10.1016\/j.cor.2004.03.016","article-title":"Forecasting stock market movement direction with support vector machine","volume":"32","author":"Huang","year":"2005","journal-title":"Comput. Oper. Res."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1016\/j.jfds.2018.04.003","article-title":"Stock price prediction using support vector regression on daily and up to the minute prices","volume":"4","author":"Henrique","year":"2018","journal-title":"J. Financ. Data Sci."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Rustam, Z., and Kintandani, P. (2019). Application of Support Vector Regression in Indonesian Stock Price Prediction with Feature Selection Using Particle Swarm Optimization. Model. Simul. Eng., 2019.","DOI":"10.1155\/2019\/8962717"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"10896","DOI":"10.1016\/j.eswa.2009.02.038","article-title":"Using support vector machine with a hybrid feature selection method to the stock trend prediction","volume":"36","author":"Lee","year":"2009","journal-title":"Expert Syst. Appl."},{"key":"ref_39","first-page":"35","article-title":"Forecasting with artificial neural networks: The state of the art","volume":"14","author":"Zhang","year":"1998","journal-title":"Int. J."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"White, H. (1988, January 24\u201327). Economic prediction using neural networks: The case of IBM daily stock returns. Proceedings of the IEEE 1988 International Conference on Neural Networks, San Diego, CA, USA.","DOI":"10.1109\/ICNN.1988.23959"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Kimoto, T., Asakawa, K., Yoda, M., and Takeoka, M. (1990, January 17\u201321). Stock market prediction system with modular neural networks. Proceedings of the IEEE International Joint Con-ference on Neural Networks, San Diego, CA, USA.","DOI":"10.1109\/IJCNN.1990.137535"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Qiu, M., and Song, Y. (2016). Predicting the direction of stock market index movement using an optimized artificial neural network model. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0155133"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"5311","DOI":"10.1016\/j.eswa.2010.10.027","article-title":"Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul Stock Exchange","volume":"38","author":"Kara","year":"2011","journal-title":"Expert Syst. Appl."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2499","DOI":"10.1016\/j.cor.2004.03.015","article-title":"A comparison between Fama and French\u2019s model and artificial neural networks in predicting the Chinese stock market","volume":"32","author":"Cao","year":"2005","journal-title":"Comput. Oper. Res."},{"key":"ref_45","unstructured":"Song, Y., Zhou, Y., and Han, R. (2018). Neural networks for stock price prediction. arXiv."},{"key":"ref_46","unstructured":"Lawrence, S., Giles, C.L., and Tsoi, A.C. (1997, January 27\u201331). Lessons in neural network training: Overfitting may be harder than expected. Proceedings of the Fourteenth National Conference on Artificial Intelligence and Ninth Innovative Applications of Artificial Intelligence Conference, AAAI 97, IAAI 97, Providence, Rhode Island."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2014\/614342","article-title":"Comparison of ARIMA and artificial neural networks models for stock price prediction","volume":"2014","author":"Adebiyi","year":"2014","journal-title":"J. Appl. Math."},{"key":"ref_48","unstructured":"Han, B. (2018). Framelets and Wavelets: Algorithms, Analysis, and Applications, Birkh\u00e4user. [1st ed.]."},{"key":"ref_49","unstructured":"Mallat, S. (2008). Wavelet Tour of Signal Processing, Academic. [3rd ed.]."},{"key":"ref_50","unstructured":"Chui, C.K. (2016). An Introduction to Wavelets, Academic Press. [1st ed.]."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Daubechies, I. (1992). Ten Lectures on Wavelets, Siam.","DOI":"10.1137\/1.9781611970104"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"2297","DOI":"10.1137\/100785508","article-title":"Matrix extension with symmetry and its application to symmetric orthonormal multiwavelets","volume":"42","author":"Han","year":"2010","journal-title":"Siam J. Math. Anal."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1437","DOI":"10.1137\/15M1048318","article-title":"Digital affine shear transforms: Fast realization and applications in image\/video processing","volume":"9","author":"Zhuang","year":"2016","journal-title":"Siam J. Imaging Sci."},{"key":"ref_54","first-page":"149","article-title":"Applications of wavelet transforms in earthquake, wind and ocean engineering","volume":"21","author":"Gurley","year":"1999","journal-title":"Eng. Struct."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1016\/S0378-4371(00)00456-8","article-title":"Scaling properties of foreign exchange volatility","volume":"289","author":"Whitcher","year":"2001","journal-title":"Phys. A Stat. Mech. Appl."},{"key":"ref_56","first-page":"218","article-title":"Wavelet low-and high-frequency components as features for predicting stock prices with backpropagation neural networks","volume":"26","author":"Lahmiri","year":"2014","journal-title":"J. King Saud Univ. Comput. Inf. Sci."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1191\/0142331206tim177oa","article-title":"Genetic programming with wavelet-based indicators for financial forecasting","volume":"28","author":"Li","year":"2006","journal-title":"Trans. Inst. Meas. Control"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"2510","DOI":"10.1016\/j.asoc.2010.09.007","article-title":"Forecasting stock markets using wavelet transforms and recurrent neural networks: An integrated system based on artificial bee colony algorithm","volume":"11","author":"Hsieh","year":"2011","journal-title":"Appl. Soft Comput."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"14346","DOI":"10.1016\/j.eswa.2011.04.222","article-title":"Forecasting stock indices with back propagation neural network","volume":"38","author":"Wang","year":"2011","journal-title":"Expert Syst. Appl."},{"key":"ref_60","first-page":"35","article-title":"Discrete wavelet transform-based prediction of stock index: A study on National Stock Exchange Fifty index","volume":"28","author":"Jothimani","year":"2015","journal-title":"J. Financ. Manag. Anal."},{"key":"ref_61","first-page":"1","article-title":"Prediction of stock market price using hybrid of wavelet transform and artificial neural network","volume":"9","author":"Chandar","year":"2016","journal-title":"Indian J. Sci. Technol."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/j.procs.2015.04.167","article-title":"Time series forecasting using hybrid ARIMA and ANN models based on DWT decomposition","volume":"48","author":"Khandelwal","year":"2015","journal-title":"Procedia Comput. Sci."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"903","DOI":"10.1098\/rspa.1998.0193","article-title":"The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis","volume":"454","author":"Huang","year":"1998","journal-title":"Proc. R. Soc. Lond. Ser. A Math. Phys. Eng. Sci."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Huang, N.E., and Wu, Z. (2008). A review on Hilbert-Huang transform: Method and its applications to geophysical studies. Rev. Geophys., 46.","DOI":"10.1029\/2007RG000228"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"368","DOI":"10.1016\/j.asoc.2016.01.027","article-title":"A hybrid ANFIS model based on empirical mode decomposition for stock time series forecasting","volume":"42","author":"Wei","year":"2016","journal-title":"Appl. Soft Comput."},{"key":"ref_66","first-page":"1191","article-title":"Empirical mode decomposition of financial data","volume":"3","author":"Drakakis","year":"2008","journal-title":"Int. Math. Forum"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Nava, N., Di Matteo, T., and Aste, T. (2018). Financial time series forecasting using empirical mode decomposition and support vector regression. Risks, 6.","DOI":"10.3390\/risks6010007"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1016\/j.econmod.2013.09.033","article-title":"A novel time-series model based on empirical mode decomposition for forecasting TAIEX","volume":"36","author":"Cheng","year":"2014","journal-title":"Econ. Model."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"2583","DOI":"10.1016\/j.econmod.2012.07.018","article-title":"Empirical mode decomposition\u2013based least squares support vector regression for foreign exchange rate forecasting","volume":"29","author":"Lin","year":"2012","journal-title":"Econ. Model."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1080\/03088839.2013.839512","article-title":"An approach for Baltic Dry Index analysis based on empirical mode decomposition","volume":"41","author":"Zeng","year":"2014","journal-title":"Marit. Policy Manag."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Yu, H., and Liu, H. (2012, January 28\u201329). Improved stock market prediction by combining support vector machine and empirical mode decomposition. Proceedings of the 2012 Fifth International Symposium on Computational Intelligence and Design, Hangzhou, China.","DOI":"10.1109\/ISCID.2012.138"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"847","DOI":"10.1016\/S0378-4266(98)00119-8","article-title":"Skewness in financial returns","volume":"23","author":"Peiro","year":"1999","journal-title":"J. Bank. Financ."},{"key":"ref_73","first-page":"171","article-title":"A survey of forecast error measures","volume":"24","author":"Shcherbakov","year":"2013","journal-title":"World Appl. Sci. J."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"79","DOI":"10.3354\/cr030079","article-title":"Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance","volume":"30","author":"Willmott","year":"2005","journal-title":"Clim. Res."},{"key":"ref_75","unstructured":"Sakamoto, Y., Ishiguro, M., and Kitagawa, G. (1986). Akaike Information Criterion Statistics, KTK Scientific Publishers."},{"key":"ref_76","unstructured":"Martin, J.H., and Jurafsky, D. (2009). Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition, Pearson\/Prentice Hall."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1023\/B:STCO.0000035301.49549.88","article-title":"A tutorial on support vector regression","volume":"14","author":"Smola","year":"2004","journal-title":"Stat. Comput."},{"key":"ref_78","unstructured":"Madge, S., and Bhatt, S. (2015). Predicting Stock Price Direction Using Support Vector Machines, Princeton University. Independent Work Report Spring."},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Sheela, K.G., and Deepa, S.N. (2013). Review on methods to fix number of hidden neurons in neural networks. Math. Probl. Eng., 2013.","DOI":"10.1155\/2013\/425740"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"1084","DOI":"10.1016\/j.eswa.2006.02.005","article-title":"EEG signal classification using wavelet feature extraction and a mixture of expert model","volume":"32","author":"Subasi","year":"2007","journal-title":"Expert Syst. Appl."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"745","DOI":"10.1111\/0022-1082.00225","article-title":"Mean reversion across national stock markets and parametric contrarian investment strategies","volume":"55","author":"Balvers","year":"2000","journal-title":"J. Financ."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1016\/j.jfineco.2012.05.011","article-title":"Size, value, and momentum in international stock returns","volume":"105","author":"Fama","year":"2012","journal-title":"J. Financ. Econ."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1016\/j.physa.2015.05.027","article-title":"Market turning points forecasting using wavelet analysis","volume":"437","author":"Bai","year":"2015","journal-title":"Phys. A Stat. Mech. Appl."}],"container-title":["Axioms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-1680\/8\/4\/116\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:27:31Z","timestamp":1760189251000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-1680\/8\/4\/116"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,10,18]]},"references-count":83,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2019,12]]}},"alternative-id":["axioms8040116"],"URL":"https:\/\/doi.org\/10.3390\/axioms8040116","relation":{},"ISSN":["2075-1680"],"issn-type":[{"value":"2075-1680","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,10,18]]}}}