{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T16:21:41Z","timestamp":1781713301025,"version":"3.54.5"},"reference-count":140,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2025,5,23]],"date-time":"2025-05-23T00:00:00Z","timestamp":1747958400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,5,23]],"date-time":"2025-05-23T00:00:00Z","timestamp":1747958400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Artif Intell Rev"],"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>The task of pricing options is seen as significant and receives considerable attention due to its potential to generate attractive profits through informed decision-making. Over the past few decades, researchers have extensively investigated both classical and machine-learning techniques for this purpose. Our motivation for undertaking this survey is to provide a comprehensive review and analyze systematically the recent works focusing on non-parametric models for option pricing. The analysis of the articles involves the utilization of several components such as input, output, dataset, assessment metrics, and other relevant factors. Research gaps and challenges are meticulously identified and outlined to serve as guiding insights for future improvements and advancements in the field. We categorize the implementation to assist interested researchers in easily reproducing previous studies as baselines. Based on the findings of this study, it can be inferred that the process of pricing options is a highly complicated task, requiring the consideration of several elements to enhance the accuracy and efficiency of models.<\/jats:p>","DOI":"10.1007\/s10462-025-11249-z","type":"journal-article","created":{"date-parts":[[2025,5,23]],"date-time":"2025-05-23T06:08:59Z","timestamp":1747980539000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Non-parametric insights in option pricing: a systematic review of theory, implementation and future directions"],"prefix":"10.1007","volume":"58","author":[{"given":"Akanksha","family":"Sharma","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chandan","family":"Kumar Verma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,5,23]]},"reference":[{"issue":"10","key":"11249_CR1","doi-asserted-by":"crossref","first-page":"9315","DOI":"10.1016\/j.eswa.2012.02.070","volume":"39","author":"JJ Ahn","year":"2012","unstructured":"Ahn JJ, Kim DH, Oh KJ, Kim TY (2012) Applying option greeks to directional forecasting of implied volatility in the options market: An intelligent approach. Expert Syst Appl 39(10):9315\u20139322","journal-title":"Expert Syst Appl"},{"key":"11249_CR2","unstructured":"Andersson N (2016) Regression-based Monte Carlo for pricing high-dimensional American-style options"},{"issue":"3","key":"11249_CR3","doi-asserted-by":"crossref","first-page":"1415","DOI":"10.1016\/j.ejor.2005.03.081","volume":"185","author":"PC Andreou","year":"2008","unstructured":"Andreou PC, Charalambous C, Martzoukos SH (2008) Pricing and trading european options by combining artificial neural networks and parametric models with implied parameters. Eur J Oper Res 185(3):1415\u20131433","journal-title":"Eur J Oper Res"},{"issue":"5","key":"11249_CR4","doi-asserted-by":"crossref","first-page":"2003","DOI":"10.1111\/j.1540-6261.1997.tb02749.x","volume":"52","author":"G Bakshi","year":"1997","unstructured":"Bakshi G, Cao C, Chen Z (1997) Empirical performance of alternative option pricing models. J Financ 52(5):2003\u20132049","journal-title":"J Financ"},{"issue":"1\u20132","key":"11249_CR5","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1016\/S0304-4076(99)00023-8","volume":"94","author":"G Bakshi","year":"2000","unstructured":"Bakshi G, Cao C, Chen Z (2000) Pricing and hedging long-term options. J Econom 94(1\u20132):277\u2013318","journal-title":"J Econom"},{"issue":"2","key":"11249_CR6","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1111\/j.1540-6261.1987.tb02569.x","volume":"42","author":"G Barone-Adesi","year":"1987","unstructured":"Barone-Adesi G, Whaley RE (1987) Efficient analytic approximation of american option values. J Financ 42(2):301\u2013320","journal-title":"J Financ"},{"issue":"7","key":"11249_CR7","doi-asserted-by":"crossref","first-page":"158","DOI":"10.3390\/jrfm13070158","volume":"13","author":"S Becker","year":"2020","unstructured":"Becker S, Cheridito P, Jentzen A (2020) Pricing and hedging American-style options with deep learning. J Risk Financ Manag 13(7):158","journal-title":"J Risk Financ Manag"},{"issue":"3","key":"11249_CR8","doi-asserted-by":"crossref","first-page":"470","DOI":"10.1017\/S0956792521000073","volume":"32","author":"S Becker","year":"2021","unstructured":"Becker S, Cheridito P, Jentzen A, Welti T (2021) Solving high-dimensional optimal stopping problems using deep learning. Eur J Appl Math 32(3):470\u2013514","journal-title":"Eur J Appl Math"},{"issue":"3","key":"11249_CR9","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1086\/260062","volume":"81","author":"F Black","year":"1973","unstructured":"Black F, Scholes M (1973) The pricing of options and corporate liabilities. J Polit Econ 81(3):637\u2013654","journal-title":"J Polit Econ"},{"key":"11249_CR10","doi-asserted-by":"crossref","unstructured":"Bloch DA (2023) American options: Models and algorithms. Available at SSRN 4532952","DOI":"10.2139\/ssrn.4532952"},{"issue":"1","key":"11249_CR11","first-page":"67","volume":"3","author":"A Bolfake","year":"2023","unstructured":"Bolfake A, Mousavi SN, Mashayekhi S (2023) Deep learning for option pricing under Heston and bates models. J Math Model Financ 3(1):67\u201382","journal-title":"J Math Model Financ"},{"issue":"1","key":"11249_CR12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.2307\/2331019","volume":"23","author":"PP Boyle","year":"1988","unstructured":"Boyle PP (1988) A lattice framework for option pricing with two state variables. J Financ Quant Anal 23(1):1\u201312","journal-title":"J Financ Quant Anal"},{"key":"11249_CR13","volume-title":"Principles of corporate finance","author":"RA Brealey","year":"2020","unstructured":"Brealey RA, Myers SC, Allen F (2020) Principles of corporate finance. Hoa Sen University, McGraw-hill"},{"key":"11249_CR14","unstructured":"Breiman L, Friedman JH, Olshen RA, Stone CJ (1984) Classification and regression trees Belmont. Wadsworth International Group, CA"},{"key":"11249_CR15","unstructured":"Brownlee J (2018) Deep Learning for Time Series Forecasting: Predict the Future with MLPs. CNNs and LSTMs in Python. Machine Learning Mastery, Computers"},{"key":"11249_CR16","doi-asserted-by":"crossref","unstructured":"Can M, Fadda S (2014) A nonparametric approach to pricing options learning networks","DOI":"10.21533\/scjournal.v3i1.18"},{"issue":"3","key":"11249_CR17","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1111\/1467-9965.00020","volume":"13","author":"P Carr","year":"2003","unstructured":"Carr P, Geman H, Madan DB, Yor M (2003) Stochastic volatility for l\u00e9vy processes. Math Financ 13(3):345\u2013382","journal-title":"Math Financ"},{"key":"11249_CR18","volume":"1221","author":"JG Cervera","year":"2019","unstructured":"Cervera JG (2019) Solution of the black-Scholes equation using artificial neural networks. J Phys Conf Ser 1221:012044","journal-title":"J Phys Conf Ser"},{"key":"11249_CR19","unstructured":"Chang E (2022) CNN-LSTM vs ANN: Option pricing theory"},{"issue":"1","key":"11249_CR20","first-page":"123","volume":"8","author":"T-Y Chang","year":"2013","unstructured":"Chang T-Y, Wang Y-H, Yeh H-Y (2013) Forecasting of option prices using a neural network model. J Account Financ Manag Strat 8(1):123","journal-title":"J Account Financ Manag Strat"},{"key":"11249_CR21","doi-asserted-by":"crossref","unstructured":"Chen S-H, Lee W-C (1999) Pricing call warrants with artificial neural networks: the case of the taiwan derivative market. In: IJCNN\u201999. International Joint Conference on Neural Networks. Proceedings (Cat. No. 99CH36339), vol 6, pp 3877\u20133882. IEEE","DOI":"10.1109\/IJCNN.1999.830774"},{"issue":"1","key":"11249_CR22","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1080\/14697688.2020.1788219","volume":"21","author":"Y Chen","year":"2021","unstructured":"Chen Y, Wan JW (2021) Deep neural network framework based on backward stochastic differential equations for pricing and hedging american options in high dimensions. Quant Financ 21(1):45\u201367","journal-title":"Quant Financ"},{"key":"11249_CR23","doi-asserted-by":"crossref","first-page":"103003","DOI":"10.1016\/j.dsp.2021.103003","volume":"112","author":"Y Chen","year":"2021","unstructured":"Chen Y, Yu H, Meng X, Xie X, Hou M, Chevallier J (2021) Numerical solving of the generalized Black-Scholes differential equation using Laguerre neural network. Digit Signal Process 112:103003","journal-title":"Digit Signal Process"},{"key":"11249_CR24","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"},{"issue":"3","key":"11249_CR25","first-page":"513","volume":"25","author":"J Choi","year":"2014","unstructured":"Choi J, Lee JT (2014) An estimation of implied volatility for kospi200 option. J Korean Data Inform Sci Soc 25(3):513\u2013522","journal-title":"J Korean Data Inform Sci Soc"},{"key":"11249_CR26","doi-asserted-by":"crossref","unstructured":"Chou C, Liu J-C, Chen C-T, Huang S-H (2019) Deep learning in model risk neutral distribution for option pricing. In: 2019 IEEE International Conference on Agents (ICA), pp 95\u201398. IEEE","DOI":"10.1109\/AGENTS.2019.8929176"},{"issue":"3","key":"11249_CR27","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/0304-405X(79)90015-1","volume":"7","author":"JC Cox","year":"1979","unstructured":"Cox JC, Ross SA, Rubinstein M (1979) Option pricing: a simplified approach. J Financ Econ 7(3):229\u2013263","journal-title":"J Financ Econ"},{"issue":"4","key":"11249_CR28","first-page":"92","volume":"15","author":"R Culkin","year":"2017","unstructured":"Culkin R, Das SR (2017) Machine learning in finance: the case of deep learning for option pricing. J Invest Manag 15(4):92\u2013100","journal-title":"J Invest Manag"},{"key":"11249_CR29","doi-asserted-by":"crossref","first-page":"4061","DOI":"10.1007\/s00521-016-2303-y","volume":"28","author":"SP Das","year":"2017","unstructured":"Das SP, Padhy S (2017) A new hybrid parametric and machine learning model with homogeneity hint for European-style index option pricing. Neural Comput Appl 28:4061\u20134077","journal-title":"Neural Comput Appl"},{"key":"11249_CR30","unstructured":"Dhandapani VL, Jain S (2024) Optimizing neural networks for Bermudan option pricing: Convergence acceleration, future exposure evaluation and interpolation in counterparty credit risk. Preprint at arXiv:2402.15936"},{"key":"11249_CR31","doi-asserted-by":"crossref","unstructured":"Dixit G, Roy D, Uppal N (2013) Predicting India volatility index: An application of artificial neural network. Int J Comput Appl 70(4)","DOI":"10.5120\/11950-7768"},{"issue":"1","key":"11249_CR32","doi-asserted-by":"crossref","first-page":"1914285","DOI":"10.1080\/23322039.2021.1914285","volume":"9","author":"R Du Plooy","year":"2021","unstructured":"Du Plooy R, Venter PJ (2021) Pricing vanilla options using artificial neural networks: application to the south African market. Cogent Econom Financ 9(1):1914285","journal-title":"Cogent Econom Financ"},{"key":"11249_CR33","doi-asserted-by":"crossref","first-page":"915","DOI":"10.1007\/s10614-020-10070-w","volume":"58","author":"S Eskiizmirliler","year":"2021","unstructured":"Eskiizmirliler S, G\u00fcnel K, Polat R (2021) On the solution of the Black-Scholes equation using feed-forward neural networks. Comput Econ 58:915\u2013941","journal-title":"Comput Econ"},{"issue":"3","key":"11249_CR34","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1016\/j.bir.2020.03.002","volume":"20","author":"S Fadda","year":"2020","unstructured":"Fadda S (2020) Pricing options with dual volatility input to modular neural networks. Borsa Istanbul Rev 20(3):269\u2013278","journal-title":"Borsa Istanbul Rev"},{"key":"11249_CR35","doi-asserted-by":"crossref","unstructured":"Fang Z, George K (2017) Application of machine learning: An analysis of Asian options pricing using neural network. In: 2017 IEEE 14th International Conference on e-Business Engineering (ICEBE), pp 142\u2013149. IEEE","DOI":"10.1109\/ICEBE.2017.30"},{"key":"11249_CR36","unstructured":"Ferraz JDM (2022) Pricing options using the xgboost model. PhD thesis, Instituto Superior de Economia e Gest\u00e3o"},{"key":"11249_CR37","doi-asserted-by":"crossref","first-page":"100190","DOI":"10.1016\/j.cosrev.2019.08.001","volume":"34","author":"DP Gandhmal","year":"2019","unstructured":"Gandhmal DP, Kumar K (2019) Systematic analysis and review of stock market prediction techniques. Comput Sci Rev 34:100190","journal-title":"Comput Sci Rev"},{"key":"11249_CR38","unstructured":"Gan L, Liu W (2023) Option pricing based on the residual neural network. Comput Econom 1\u201321"},{"issue":"3","key":"11249_CR39","doi-asserted-by":"crossref","first-page":"73","DOI":"10.3390\/risks8030073","volume":"8","author":"RM Gaspar","year":"2020","unstructured":"Gaspar RM, Lopes SD, Sequeira B (2020) Neural network pricing of American put options. Risks 8(3):73","journal-title":"Risks"},{"key":"11249_CR40","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.enganabound.2023.02.040","volume":"151","author":"F Gatta","year":"2023","unstructured":"Gatta F, Di Cola VS, Giampaolo F, Piccialli F, Cuomo S (2023) Meshless methods for American option pricing through physics-informed neural networks. Eng Anal Boundary Elem 151:68\u201382","journal-title":"Eng Anal Boundary Elem"},{"issue":"1","key":"11249_CR41","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1109\/TNN.2006.883005","volume":"18","author":"R Gen\u00e7ay","year":"2007","unstructured":"Gen\u00e7ay R, Gibson R (2007) Model risk for European-style stock index options. IEEE Trans Neural Networks 18(1):193\u2013202","journal-title":"IEEE Trans Neural Networks"},{"key":"11249_CR42","doi-asserted-by":"crossref","unstructured":"Ge M, Zhou S, Luo S, Tian B (2021) 3d tensor-based deep learning models for predicting option price. In: 2021 International Conference on Information Science and Communications Technologies (ICISCT), pp 1\u20136. IEEE","DOI":"10.1109\/ICISCT52966.2021.9670100"},{"key":"11249_CR43","first-page":"127355","volume":"432","author":"K Glau","year":"2022","unstructured":"Glau K, Wunderlich L (2022) The deep parametric PDE method and applications to option pricing. Appl Math Comput 432:127355","journal-title":"Appl Math Comput"},{"issue":"02","key":"11249_CR44","doi-asserted-by":"crossref","first-page":"2141001","DOI":"10.1142\/S2424786321410012","volume":"8","author":"A Goswami","year":"2021","unstructured":"Goswami A, Rajani S, Tanksale A (2021) Data-driven option pricing using single and multi-asset supervised learning. Int J Financ Eng 8(02):2141001","journal-title":"Int J Financ Eng"},{"key":"11249_CR45","unstructured":"Goudenege L, Molent A, Zanette A (2022) Computing xva for american basket derivatives by machine learning techniques. Preprint at arXiv:2209.06485"},{"issue":"4","key":"11249_CR46","doi-asserted-by":"crossref","first-page":"626","DOI":"10.1109\/TNN.2008.2011130","volume":"20","author":"N Gradojevic","year":"2009","unstructured":"Gradojevic N, Gen\u00e7ay R, Kukolj D (2009) Option pricing with modular neural networks. IEEE Trans Neural Networks 20(4):626\u2013637","journal-title":"IEEE Trans Neural Networks"},{"key":"11249_CR47","doi-asserted-by":"crossref","unstructured":"Gradojevic N, Kukolj D (2022) Unlocking the black box: Non-parametric option pricing before and during Covid-19. Ann Operat Res 1\u201324","DOI":"10.1007\/s10479-022-04578-7"},{"key":"11249_CR48","unstructured":"Hahn JT (2013) Option pricing using artificial neural networks: an Australian perspective. PhD thesis, Bond University"},{"issue":"2","key":"11249_CR49","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1093\/rfs\/6.2.327","volume":"6","author":"SL Heston","year":"1993","unstructured":"Heston SL (1993) A closed-form solution for options with stochastic volatility with applications to bond and currency options. Rev Financ Stud 6(2):327\u2013343","journal-title":"Rev Financ Stud"},{"key":"11249_CR50","doi-asserted-by":"crossref","unstructured":"Higham DJ (2004) An introduction to financial option valuation: mathematics, stochastics and computation, vol 13. Cambridge University Press, ISBN-9780511800948","DOI":"10.1017\/CBO9780511800948"},{"key":"11249_CR51","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/j.eswa.2019.03.029","volume":"129","author":"E Hoseinzade","year":"2019","unstructured":"Hoseinzade E, Haratizadeh S (2019) CNNPRED: CNN-based stock market prediction using a diverse set of variables. Expert Syst Appl 129:273\u2013285","journal-title":"Expert Syst Appl"},{"key":"11249_CR52","unstructured":"Hoseinzade E, Haratizadeh S, Khoeini A (2019) U-CNNPRED: a universal CNN-based predictor for stock markets. Preprint at arXiv:1911.12540"},{"issue":"3","key":"11249_CR53","doi-asserted-by":"crossref","first-page":"192","DOI":"10.3390\/jrfm16030192","volume":"16","author":"K Hoshisashi","year":"2023","unstructured":"Hoshisashi K, Yamada Y (2023) Pricing multi-asset Bermudan commodity options with stochastic volatility using neural networks. J Risk Financ Manag 16(3):192","journal-title":"J Risk Financ Manag"},{"key":"11249_CR54","unstructured":"Hsu C-M, Fu Y-C, Liu Y-C, Peng C-Y (2015) Forecasting the prices of taiex options by using genetic programming and support vector regression. In: Proceedings of the International MultiConference of Engineers and Computer Scientists, vol 1"},{"issue":"5","key":"11249_CR55","doi-asserted-by":"crossref","first-page":"851","DOI":"10.1080\/14697688.2020.1713393","volume":"20","author":"W Hu","year":"2020","unstructured":"Hu W, Zastawniak T (2020) Pricing high-dimensional American options by kernel ridge regression. Quant Financ 20(5):851\u2013865","journal-title":"Quant Financ"},{"key":"11249_CR56","volume-title":"Options futures and other derivatives","author":"JC Hull","year":"2003","unstructured":"Hull JC (2003) Options futures and other derivatives. Pearson Education India, United States of America"},{"issue":"2","key":"11249_CR57","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1111\/j.1540-6261.1987.tb02568.x","volume":"42","author":"J Hull","year":"1987","unstructured":"Hull J, White A (1987) The pricing of options on assets with stochastic volatilities. J Financ 42(2):281\u2013300","journal-title":"J Financ"},{"issue":"3","key":"11249_CR58","doi-asserted-by":"crossref","first-page":"851","DOI":"10.1111\/j.1540-6261.1994.tb00081.x","volume":"49","author":"JM Hutchinson","year":"1994","unstructured":"Hutchinson JM, Lo AW, Poggio T (1994) A nonparametric approach to pricing and hedging derivative securities via learning networks. J Financ 49(3):851\u2013889","journal-title":"J Financ"},{"key":"11249_CR59","doi-asserted-by":"crossref","unstructured":"\u0130lt\u00fczer Z (2022) Option pricing with neural networks vs. Black-Scholes under different volatility forecasting approaches for bist 30 index options. Borsa Istanbul Rev 22(4):725\u2013742","DOI":"10.1016\/j.bir.2021.12.001"},{"key":"11249_CR60","unstructured":"Itkin A (2019) Deep learning calibration of option pricing models: some pitfalls and solutions. Preprint at arXiv:1906.03507"},{"key":"11249_CR61","doi-asserted-by":"crossref","unstructured":"Iva\u015fcu C-F (2021) Option pricing using machine learning. Exp Syst Appl 163:113799","DOI":"10.1016\/j.eswa.2020.113799"},{"key":"11249_CR62","doi-asserted-by":"crossref","unstructured":"Jacquier A, Malone ER, Oumgari M (2019) Stacked monte Carlo for option pricing. Preprint at arXiv:1903.10795","DOI":"10.2139\/ssrn.3360332"},{"issue":"4","key":"11249_CR63","doi-asserted-by":"crossref","first-page":"587","DOI":"10.1080\/14697688.2018.1490807","volume":"19","author":"H Jang","year":"2019","unstructured":"Jang H, Lee J (2019) Generative Bayesian neural network model for risk-neutral pricing of American index options. Quant Financ 19(4):587\u2013603","journal-title":"Quant Financ"},{"key":"11249_CR64","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.inffus.2020.12.010","volume":"70","author":"JH Jang","year":"2021","unstructured":"Jang JH, Yoon J, Kim J, Gu J, Kim HY (2021) Deepoption: a novel option pricing framework based on deep learning with fused distilled data from multiple parametric methods. Inform Fusion 70:43\u201359","journal-title":"Inform Fusion"},{"key":"11249_CR65","unstructured":"Jayaraman J, Zhu X, Rabaa\u2019i AA (2022) An evaluation of data driven machine learning approaches to option pricing. Proc Northeast Bus Econom Assoc"},{"issue":"10","key":"11249_CR66","doi-asserted-by":"crossref","first-page":"1793","DOI":"10.1080\/00949655.2020.1747463","volume":"90","author":"Y Jerbi","year":"2020","unstructured":"Jerbi Y, Chaabene S (2020) European call price modelling using neural networks in considering volatility as stochastic with comparison to the heston model. J Stat Comput Simul 90(10):1793\u20131810","journal-title":"J Stat Comput Simul"},{"key":"11249_CR67","unstructured":"Jonen C (2011) Efficient pricing of high-dimensional American-style derivatives: a robust regression monte Carlo method. PhD thesis, Universit\u00e4t zu K\u00f6ln"},{"key":"11249_CR68","unstructured":"Ke G, Meng Q, Finley T, Wang T, Chen W, Ma W, Ye Q, Liu T-Y (2017) LIGHTGBM: A highly efficient gradient boosting decision tree. Adv Neural Inform Process Syst 30"},{"key":"11249_CR69","doi-asserted-by":"crossref","unstructured":"Kim YS, Kim H, Choi J (2023) Deep calibration with artificial neural network: a performance comparison on option pricing models. Preprint at arXiv:2303.08760","DOI":"10.2139\/ssrn.4388782"},{"issue":"5","key":"11249_CR70","doi-asserted-by":"crossref","first-page":"2437","DOI":"10.1016\/j.eswa.2013.09.043","volume":"41","author":"W Kristjanpoller","year":"2014","unstructured":"Kristjanpoller W, Fadic A, Minutolo MC (2014) Volatility forecast using hybrid neural network models. Expert Syst Appl 41(5):2437\u20132442","journal-title":"Expert Syst Appl"},{"issue":"6","key":"11249_CR71","first-page":"6","volume":"7","author":"P Lajbcygier","year":"1999","unstructured":"Lajbcygier P (1999) Literature review: the non-parametric models. J Comput Intell Financ 7(6):6\u201318","journal-title":"J Comput Intell Financ"},{"issue":"3","key":"11249_CR72","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1515\/mcma-2021-2091","volume":"27","author":"B Lapeyre","year":"2021","unstructured":"Lapeyre B, Lelong J (2021) Neural network regression for Bermudan option pricing. Monte Carlo Methods Appl 27(3):227\u2013247","journal-title":"Monte Carlo Methods Appl"},{"key":"11249_CR73","doi-asserted-by":"crossref","unstructured":"Levendis A (2023) On the calibration of stochastic volatility models to estimate the real-world measure used in option pricing. ORiON 39(1)","DOI":"10.5784\/39-1-747"},{"key":"11249_CR74","doi-asserted-by":"crossref","unstructured":"Li W (2022) Application of machine learning in option pricing: a review. In: 2022 7th International Conference on Social Sciences and Economic Development (ICSSED 2022), pp 209\u2013214. Atlantis Press","DOI":"10.2991\/aebmr.k.220405.035"},{"issue":"7","key":"11249_CR75","doi-asserted-by":"crossref","first-page":"1324","DOI":"10.3390\/sym14071324","volume":"14","author":"N Li","year":"2022","unstructured":"Li N (2022) An iteration algorithm for American options pricing based on reinforcement learning. Symmetry 14(7):1324","journal-title":"Symmetry"},{"key":"11249_CR76","doi-asserted-by":"crossref","first-page":"115951","DOI":"10.1016\/j.eswa.2021.115951","volume":"187","author":"L Liang","year":"2022","unstructured":"Liang L, Cai X (2022) Time-sequencing European options and pricing with deep learning-analyzing based on interpretable ale method. Expert Syst Appl 187:115951","journal-title":"Expert Syst Appl"},{"issue":"3","key":"11249_CR77","doi-asserted-by":"crossref","first-page":"85","DOI":"10.12660\/rbfin.v19n3.2021.83815","volume":"19","author":"J Lin","year":"2021","unstructured":"Lin J, Almeida C (2021) American option pricing with machine learning: an extension of the longstaff-Schwartz method. Braz Rev Financ 19(3):85\u2013109","journal-title":"Braz Rev Financ"},{"key":"11249_CR78","doi-asserted-by":"crossref","unstructured":"Liu S-B, Chang C-C, Cheng C-Y, et al (2022) Improving option price forecasts using machine learning algorithms with investor sentiment: Evidence from the Taiwan options market","DOI":"10.2139\/ssrn.4207682"},{"issue":"1","key":"11249_CR79","doi-asserted-by":"crossref","first-page":"1","DOI":"10.6339\/JDS.201601_14(1).0001","volume":"14","author":"D Liu","year":"2016","unstructured":"Liu D, Huang S (2016) The performance of hybrid artificial neural network models for option pricing during financial crises. J Data Sci 14(1):1\u201317","journal-title":"J Data Sci"},{"issue":"2","key":"11249_CR80","doi-asserted-by":"crossref","first-page":"180","DOI":"10.3390\/fintech1020014","volume":"1","author":"D Liu","year":"2022","unstructured":"Liu D, Wei A (2022) Regulated LSTM artificial neural networks for option risks. FinTech 1(2):180\u2013190","journal-title":"FinTech"},{"issue":"2","key":"11249_CR81","doi-asserted-by":"crossref","first-page":"314","DOI":"10.3390\/math11020314","volume":"11","author":"Y Liu","year":"2023","unstructured":"Liu Y, Zhang X (2023) Option pricing using LSTM: a perspective of realized skewness. Mathematics 11(2):314","journal-title":"Mathematics"},{"issue":"1","key":"11249_CR82","doi-asserted-by":"crossref","first-page":"16","DOI":"10.3390\/risks7010016","volume":"7","author":"S Liu","year":"2019","unstructured":"Liu S, Oosterlee CW, Bohte SM (2019) Pricing options and computing implied volatilities using neural networks. Risks 7(1):16","journal-title":"Risks"},{"key":"11249_CR83","doi-asserted-by":"crossref","unstructured":"Liu D, Wu Y (2023) Option pricing using deep convolutional neural networks enhanced by technical indicators. In: 2023 IEEE 9th International Conference on Cloud Computing and Intelligent Systems (CCIS), IEEE. pp 143\u2013147","DOI":"10.1109\/CCIS59572.2023.10262865"},{"key":"11249_CR84","doi-asserted-by":"crossref","unstructured":"Luo Q, Jia Z, Li H, Wu Y (2022) Analysis of parametric and non-parametric option pricing models. Heliyon 8(11)","DOI":"10.1016\/j.heliyon.2022.e11388"},{"issue":"1","key":"11249_CR85","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1023\/A:1009703431535","volume":"2","author":"DB Madan","year":"1998","unstructured":"Madan DB, Carr PP, Chang EC (1998) The variance gamma process and option pricing. Rev Financ 2(1):79\u2013105","journal-title":"Rev Financ"},{"key":"11249_CR86","doi-asserted-by":"crossref","first-page":"21","DOI":"10.11648\/j.dmath.20190401.14","volume":"4","author":"B Madhu","year":"2019","unstructured":"Madhu B, Paul AK, Roy R (2019) Performance comparison of various kernels of support vector regression for predicting option price. Int J Discret Math 4:21\u201331","journal-title":"Int J Discret Math"},{"issue":"05","key":"11249_CR87","doi-asserted-by":"crossref","first-page":"78","DOI":"10.4236\/jcc.2021.95006","volume":"9","author":"B Madhu","year":"2021","unstructured":"Madhu B, Rahman MA, Mukherjee A, Islam MZ, Roy R, Ali LE (2021) A comparative study of support vector machine and artificial neural network for option price prediction. J Comput Commun 9(05):78\u201391","journal-title":"J Comput Commun"},{"key":"11249_CR88","doi-asserted-by":"crossref","unstructured":"McGhee W (2020) An artificial neural network representation of the SABR stochastic volatility model. J Comput Financ 25(2)","DOI":"10.21314\/JCF.2021.007"},{"key":"11249_CR89","doi-asserted-by":"crossref","first-page":"113422","DOI":"10.1016\/j.cam.2021.113422","volume":"392","author":"F Mehrdoust","year":"2021","unstructured":"Mehrdoust F, Noorani I, Hamdi A (2021) Calibration of the double Heston model and an analytical formula in pricing American put option. J Comput Appl Math 392:113422","journal-title":"J Comput Appl Math"},{"key":"11249_CR90","doi-asserted-by":"crossref","unstructured":"Merello S, Ratto AP, Oneto L, Cambria E (2019) Ensemble application of transfer learning and sample weighting for stock market prediction. In: 2019 International Joint Conference on Neural Networks (IJCNN), pp 1\u20138. IEEE","DOI":"10.1109\/IJCNN.2019.8851938"},{"issue":"1\u20132","key":"11249_CR91","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/0304-405X(76)90022-2","volume":"3","author":"RC Merton","year":"1976","unstructured":"Merton RC (1976) Option pricing when underlying stock returns are discontinuous. J Financ Econ 3(1\u20132):125\u2013144","journal-title":"J Financ Econ"},{"issue":"4","key":"11249_CR92","first-page":"6","volume":"12","author":"SK Mitra","year":"2012","unstructured":"Mitra SK (2012) An option pricing model that combines neural network approach and black Scholes formula. Global J Comp Sci Technol 12(4):6\u201316","journal-title":"Global J Comp Sci Technol"},{"key":"11249_CR93","doi-asserted-by":"crossref","unstructured":"Montesdeoca L, Niranjan M (2016) Extending the feature set of a data-driven artificial neural network model of pricing financial options. In: 2016 IEEE Symposium Series on Computational Intelligence (SSCI), pp 1\u20136. IEEE","DOI":"10.1109\/SSCI.2016.7850014"},{"key":"11249_CR94","doi-asserted-by":"crossref","unstructured":"Nwankwo C, Umeorah N, Ware T, Dai W (2023) Deep learning and American options via free boundary framework. Comput Econom 1\u201344","DOI":"10.1007\/s10614-023-10459-3"},{"key":"11249_CR95","unstructured":"Palmer S, Gorse D (2017) Pseudo-analytical solutions for stochastic options pricing using monte carlo simulation and breeding pso-trained neural networks. In: ESANN 2017-Proceedings, 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, pp. 365\u2013370. i6doc.com"},{"key":"11249_CR96","doi-asserted-by":"crossref","unstructured":"Paredes MS, Kadry S (2022) Pricing european options with deep learning models. In: 2022 Fifth International Conference of Women in Data Science at Prince Sultan University (WiDS PSU), pp. 106\u2013111. IEEE","DOI":"10.1109\/WiDS-PSU54548.2022.00033"},{"issue":"11","key":"11249_CR97","doi-asserted-by":"crossref","first-page":"5227","DOI":"10.1016\/j.eswa.2014.01.032","volume":"41","author":"H Park","year":"2014","unstructured":"Park H, Kim N, Lee J (2014) Parametric models and non-parametric machine learning models for predicting option prices: empirical comparison study over kospi 200 index options. Expert Syst Appl 41(11):5227\u20135237","journal-title":"Expert Syst Appl"},{"key":"11249_CR98","unstructured":"Pranav B, Hegde V (2021) Volatility forecasting techniques using neural networks: a review. Int J Eng Res Technol 10:748"},{"issue":"12","key":"11249_CR99","doi-asserted-by":"crossref","first-page":"552","DOI":"10.3390\/jrfm15120552","volume":"15","author":"A Prasad","year":"2022","unstructured":"Prasad A, Bakhshi P (2022) Forecasting the direction of daily changes in the India VIX index using machine learning. J Risk Financ Manag 15(12):552","journal-title":"J Risk Financ Manag"},{"key":"11249_CR100","doi-asserted-by":"crossref","first-page":"1340","DOI":"10.1016\/j.procs.2022.01.170","volume":"199","author":"L Qian","year":"2022","unstructured":"Qian L, Zhao J, Ma Y (2022) Option pricing based on Ga-Bp neural network. Procedia Comput Sci 199:1340\u20131354","journal-title":"Procedia Comput Sci"},{"key":"11249_CR101","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1007\/s10489-007-0052-4","volume":"29","author":"C Quek","year":"2008","unstructured":"Quek C, Pasquier M, Kumar N (2008) A novel recurrent neural network-based prediction system for option trading and hedging. Appl Intell 29:138\u2013151","journal-title":"Appl Intell"},{"issue":"3","key":"11249_CR102","first-page":"476","volume":"31","author":"Z Rencai","year":"2022","unstructured":"Rencai Z (2022) Option pricing based on machine learning algorithm. J Syst Manag 31(3):476","journal-title":"J Syst Manag"},{"issue":"5","key":"11249_CR103","first-page":"1093","volume":"34","author":"RJ Rendleman","year":"1979","unstructured":"Rendleman RJ (1979) Two-state option pricing. J Financ 34(5):1093\u20131110","journal-title":"J Financ"},{"key":"11249_CR104","doi-asserted-by":"crossref","first-page":"464","DOI":"10.1016\/j.cam.2019.06.015","volume":"363","author":"P Roul","year":"2020","unstructured":"Roul P, Goura VP (2020) A new higher order compact finite difference method for generalised Black-Scholes partial differential equation: European call option. J Comput Appl Math 363:464\u2013484","journal-title":"J Comput Appl Math"},{"key":"11249_CR105","doi-asserted-by":"crossref","first-page":"112881","DOI":"10.1016\/j.cam.2020.112881","volume":"377","author":"P Roul","year":"2020","unstructured":"Roul P, Goura VP (2020) A sixth order numerical method and its convergence for generalized black-Scholes PDE. J Comput Appl Math 377:112881","journal-title":"J Comput Appl Math"},{"issue":"1","key":"11249_CR106","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1111\/j.1540-6261.1983.tb03636.x","volume":"38","author":"M Rubinstein","year":"1983","unstructured":"Rubinstein M (1983) Displaced diffusion option pricing. J Financ 38(1):213\u2013217","journal-title":"J Financ"},{"key":"11249_CR107","doi-asserted-by":"crossref","unstructured":"Ruf J, Wang W (2019) Neural networks for option pricing and hedging: a literature review. Preprint at arXiv:1911.05620","DOI":"10.2139\/ssrn.3486363"},{"key":"11249_CR108","unstructured":"Rygg ES, Vinje HJ, Wu C (2023) Enhanced option pricing using deep learning: a time-series approach with a combined LSTM-MLP model. Master\u2019s thesis, NTNU"},{"key":"11249_CR109","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1007\/s10614-013-9411-x","volume":"45","author":"G Santamar\u00eda-Bonfil","year":"2015","unstructured":"Santamar\u00eda-Bonfil G, Frausto-Sol\u00eds J, V\u00e1zquez-Rodarte I (2015) Volatility forecasting using support vector regression and a hybrid genetic algorithm. Comput Econ 45:111\u2013133","journal-title":"Comput Econ"},{"issue":"9","key":"11249_CR110","doi-asserted-by":"crossref","first-page":"1736","DOI":"10.1080\/00207160.2021.2011248","volume":"99","author":"M Sarboland","year":"2022","unstructured":"Sarboland M, Aminataei A (2022) On the numerical solution of time fractional Black-Scholes equation. Int J Comput Math 99(9):1736\u20131753","journal-title":"Int J Comput Math"},{"key":"11249_CR111","unstructured":"Scholkopf B, Smola AJ (2018) Learning with kernels: support vector machines, regularization, optimization, and beyond. MIT press, ISBN-0262536579, 9780262536578"},{"issue":"4","key":"11249_CR112","doi-asserted-by":"crossref","first-page":"419","DOI":"10.2307\/2330793","volume":"22","author":"LO Scott","year":"1987","unstructured":"Scott LO (1987) Option pricing when the variance changes randomly: theory, estimation, and an application. J Financ Quant Anal 22(4):419\u2013438","journal-title":"J Financ Quant Anal"},{"issue":"4","key":"11249_CR113","doi-asserted-by":"crossref","first-page":"2790","DOI":"10.1002\/mma.5913","volume":"44","author":"S Shahmorad","year":"2021","unstructured":"Shahmorad S, Kalantari R, Assadzadeh A (2021) Numerical solution of fractional Black-Scholes model of American put option pricing via a nonstandard finite difference method: Stability and convergent analysis. Math Methods Appl Sci 44(4):2790\u20132805","journal-title":"Math Methods Appl Sci"},{"key":"11249_CR114","doi-asserted-by":"crossref","unstructured":"Sharma A, Verma C (2024) Investigating the impact of technical indicators on option price prediction through deep learning models. In: 2024 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT), pp 1\u20135. IEEE","DOI":"10.1109\/CONECCT62155.2024.10677131"},{"key":"11249_CR115","doi-asserted-by":"crossref","unstructured":"Sharma A, Verma CK, Singh P (2024) Enhancing option pricing accuracy in the Indian market: A CNN-BILSTM approach. Comput Econom 1\u201328","DOI":"10.21203\/rs.3.rs-3322968\/v1"},{"key":"11249_CR116","unstructured":"Shen Y (2023) American option pricing using self-attention GRU and Shapley value interpretation. Preprint at arXiv:2310.12500"},{"issue":"1","key":"11249_CR117","first-page":"3444","volume":"9","author":"P Shrivastava","year":"2019","unstructured":"Shrivastava P, Verma C (2019) A hybrid forecasting model for option price prediction using machine learning technique. Int J Innov Technol Exp Eng 9(1):3444\u20133450","journal-title":"Int J Innov Technol Exp Eng"},{"key":"11249_CR118","doi-asserted-by":"publisher","first-page":"3231","DOI":"10.35940\/ijrte.B2683.078219","volume":"8","author":"P Shrivastava","year":"2019","unstructured":"Shrivastava P, Verma C (2019) Prediction of option price using ensemble of machine learning algorithms for Indian stock market. Int J Recent Technol Eng (IJRTE) 8:3231\u20133241. https:\/\/doi.org\/10.35940\/ijrte.B2683.078219","journal-title":"Int J Recent Technol Eng (IJRTE)"},{"key":"11249_CR119","doi-asserted-by":"crossref","first-page":"123979","DOI":"10.1016\/j.eswa.2024.123979","volume":"251","author":"Y Shvimer","year":"2024","unstructured":"Shvimer Y, Zhu S-P (2024) Pricing options with a new hybrid neural network model. Expert Syst Appl 251:123979","journal-title":"Expert Syst Appl"},{"issue":"6","key":"11249_CR120","doi-asserted-by":"crossref","first-page":"419","DOI":"10.1002\/for.2311","volume":"33","author":"C Spreckelsen","year":"2014","unstructured":"Spreckelsen C, Mettenheim H-J, Breitner MH (2014) Real-time pricing and hedging of options on currency futures with artificial neural networks. J Forecast 33(6):419\u2013432","journal-title":"J Forecast"},{"issue":"4","key":"11249_CR121","doi-asserted-by":"crossref","first-page":"384","DOI":"10.3390\/axioms12040384","volume":"12","author":"N Umeorah","year":"2023","unstructured":"Umeorah N, Mashele P, Agbaeze O, Mba JC (2023) Barrier options and Greeks: Modeling with neural networks. Axioms 12(4):384","journal-title":"Axioms"},{"key":"11249_CR122","doi-asserted-by":"crossref","unstructured":"Vaswani P, Mundakkad P, Jayaprakasam K (2022) Financial option pricing using random forest and artificial neural network: A novel approach. In: International Joint Conference on Advances in Computational Intelligence, pp 419\u2013433. Springer","DOI":"10.1007\/978-981-99-1435-7_36"},{"issue":"4","key":"11249_CR123","doi-asserted-by":"crossref","first-page":"105","DOI":"10.21314\/JCF.2001.064","volume":"4","author":"J Vecer","year":"2001","unstructured":"Vecer J (2001) A new PDE approach for pricing arithmetic average Asian options. J Comput Financ 4(4):105\u2013113","journal-title":"J Comput Financ"},{"issue":"6","key":"11249_CR124","first-page":"38","volume":"10","author":"N Verma","year":"2014","unstructured":"Verma N, Srivastava N, Das S (2014) Forecasting the price of call option using support vector regression. IOSR J Math 10(6):38\u201343","journal-title":"IOSR J Math"},{"issue":"4","key":"11249_CR125","doi-asserted-by":"crossref","first-page":"488","DOI":"10.14419\/ijamr.v4i4.5023","volume":"4","author":"N Verma","year":"2015","unstructured":"Verma N, Das S, Srivastava N (2015) Multiple kernel support vector regression for pricing nifty option. Int J Appl Math Res 4(4):488","journal-title":"Int J Appl Math Res"},{"key":"11249_CR126","doi-asserted-by":"crossref","unstructured":"Wang C-W, Wu C-W, Chen P-L (2023) Option pricing using machine learning with intraday data of TAIEX option. In: International Conference on Human-Computer Interaction, pp. 214\u2013224. Springer","DOI":"10.1007\/978-3-031-36049-7_17"},{"issue":"5","key":"11249_CR127","doi-asserted-by":"crossref","first-page":"5025","DOI":"10.1016\/j.eswa.2011.11.038","volume":"39","author":"C-P Wang","year":"2012","unstructured":"Wang C-P, Lin S-H, Huang H-H, Wu P-C (2012) Using neural network for forecasting TXO price under different volatility models. Expert Syst Appl 39(5):5025\u20135032","journal-title":"Expert Syst Appl"},{"key":"11249_CR128","doi-asserted-by":"crossref","unstructured":"Wang M, Zhang Y, Qin C, Liu P, Zhang Q et al (2022) Option pricing model combining ensemble learning methods and network learning structure. Math Probl Eng 2022","DOI":"10.1155\/2022\/2590940"},{"key":"11249_CR129","doi-asserted-by":"crossref","unstructured":"Wei X, Xie Z, Cheng R, Li Q (2020) A CNN based system for predicting the implied volatility and option prices","DOI":"10.24251\/HICSS.2020.176"},{"issue":"1","key":"11249_CR130","doi-asserted-by":"crossref","first-page":"35","DOI":"10.3390\/e24010035","volume":"24","author":"M Wysocki","year":"2021","unstructured":"Wysocki M, \u015alepaczuk R (2021) Artificial neural networks performance in wig20 index options pricing. Entropy 24(1):35","journal-title":"Entropy"},{"key":"11249_CR131","doi-asserted-by":"crossref","unstructured":"Yadav K (2021) Formulation of a rational option pricing model using artificial neural networks. In: SoutheastCon 2021, pp 1\u20138. IEEE","DOI":"10.1109\/SoutheastCon45413.2021.9401835"},{"key":"11249_CR132","doi-asserted-by":"crossref","unstructured":"Yan J-A (2018) Introduction to Stochastic Finance. Springer, Science Press Beijing","DOI":"10.1007\/978-981-13-1657-9"},{"key":"11249_CR133","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1007\/s00186-011-0352-7","volume":"74","author":"Z Yang","year":"2011","unstructured":"Yang Z, Ewald C-O, Menkens O (2011) Pricing and hedging of Asian options: quasi-explicit solutions via Malliavin calculus. Math Methods Oper Res 74:93\u2013120","journal-title":"Math Methods Oper Res"},{"key":"11249_CR134","doi-asserted-by":"crossref","unstructured":"Yang A, Ye Q, Zhai J (2023) Volatility forecasting with hybrid-long short-term memory models: Evidence from the covid-19 period. Int J Financ Econom","DOI":"10.1002\/ijfe.2805"},{"key":"11249_CR135","doi-asserted-by":"crossref","unstructured":"Yang Z, Zhang L, Tao X, Ji Y et al (2022) Heston-ga hybrid option pricing model based on resnet50. Discret Dyn Nat Soc 2022","DOI":"10.1155\/2022\/7274598"},{"key":"11249_CR136","doi-asserted-by":"crossref","unstructured":"Yang Y, Zheng Y, Hospedales T (2017) Gated neural networks for option pricing: Rationality by design. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol 31","DOI":"10.1609\/aaai.v31i1.10505"},{"key":"11249_CR137","doi-asserted-by":"crossref","first-page":"376","DOI":"10.1016\/j.knosys.2018.08.039","volume":"163","author":"Y Zeng","year":"2019","unstructured":"Zeng Y, Klabjan D (2019) Online adaptive machine learning based algorithm for implied volatility surface modeling. Knowl-Based Syst 163:376\u2013391","journal-title":"Knowl-Based Syst"},{"issue":"1","key":"11249_CR138","doi-asserted-by":"crossref","first-page":"36","DOI":"10.3390\/info13010036","volume":"13","author":"K Zhao","year":"2022","unstructured":"Zhao K, Zhang J, Liu Q (2022) Dual-hybrid modeling for option pricing of CSI 300etf. Information 13(1):36","journal-title":"Information"},{"key":"11249_CR139","unstructured":"Zhuang J, Ding D, Lu W, Wu X, Yuan G (2023) A gaussian process based method with deep kernel learning for pricing high-dimensional american options. Preprint at arXiv:2311.07211"},{"issue":"3","key":"11249_CR140","doi-asserted-by":"crossref","first-page":"267","DOI":"10.3934\/DSFE.2023016","volume":"3","author":"H Zouaoui","year":"2023","unstructured":"Zouaoui H, Naas M-N (2023) Option pricing using deep learning approach based on LSTM-GRU neural networks: Case of London stock exchange. Data Sci Financ Econ 3(3):267\u2013284","journal-title":"Data Sci Financ Econ"}],"container-title":["Artificial Intelligence Review"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-025-11249-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10462-025-11249-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-025-11249-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,23]],"date-time":"2025-06-23T10:36:43Z","timestamp":1750675003000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10462-025-11249-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,23]]},"references-count":140,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2025,8]]}},"alternative-id":["11249"],"URL":"https:\/\/doi.org\/10.1007\/s10462-025-11249-z","relation":{},"ISSN":["1573-7462"],"issn-type":[{"value":"1573-7462","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,23]]},"assertion":[{"value":"25 April 2025","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 May 2025","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 declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"252"}}