{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T14:53:04Z","timestamp":1781016784530,"version":"3.54.1"},"reference-count":47,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2021,12,24]],"date-time":"2021-12-24T00:00:00Z","timestamp":1640304000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>In this paper, the performance of artificial neural networks in option pricing was analyzed and compared with the results obtained from the Black\u2013Scholes\u2013Merton model, based on the historical volatility. The results were compared based on various error metrics calculated separately between three moneyness ratios. The market data-driven approach was taken to train and test the neural network on the real-world options data from 2009 to 2019, quoted on the Warsaw Stock Exchange. The artificial neural network did not provide more accurate option prices, even though its hyperparameters were properly tuned. The Black\u2013Scholes\u2013Merton model turned out to be more precise and robust to various market conditions. In addition, the bias of the forecasts obtained from the neural network differed significantly between moneyness states. This study provides an initial insight into the application of deep learning methods to pricing options in emerging markets with low liquidity and high volatility.<\/jats:p>","DOI":"10.3390\/e24010035","type":"journal-article","created":{"date-parts":[[2021,12,27]],"date-time":"2021-12-27T01:00:54Z","timestamp":1640566854000},"page":"35","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Artificial Neural Networks Performance in WIG20 Index Options Pricing"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1693-1438","authenticated-orcid":false,"given":"Maciej","family":"Wysocki","sequence":"first","affiliation":[{"name":"Quantitative Finance Research Group, Faculty of Economic Sciences, University of Warsaw, Ul. D\u0142uga 44\/50, 00-241 Warsaw, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5227-2014","authenticated-orcid":false,"given":"Robert","family":"\u015alepaczuk","sequence":"additional","affiliation":[{"name":"Quantitative Finance Research Group, Department of Quantitative Finance, Faculty of Economic Sciences, University of Warsaw, Ul. D\u0142uga 44\/50, 00-241 Warsaw, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1007\/BF02478259","article-title":"A logical calculus of the ideas immanent in nervous activity","volume":"5","author":"McCulloch","year":"1943","journal-title":"Bull. Math. Biophys."},{"key":"ref_2","unstructured":"Werbos, P., and John, P. (1974). Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences. 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