Building a Neural Network to Predict the Option Price
( Pp. 190-199)

With the increase in the financial literacy of the population, the scale of the financial market is also expanding: in 2021, the number of private investors who opened brokerage accounts on the Moscow Exchange almost doubled compared to 2020 and at the beginning of 2022 is more than 17 million. One of the most effective tools for reducing market risks are various derivative financial instruments. The aim of the study is to improve the quality and efficiency of estimating the value of an option on the index of the Russian trading system by developing and implementing a specialized information system. To achieve this goal, the following tasks were set and solved in the work: 1) an analysis of the main concepts, tools and algorithms for evaluating options using machine learning methods was carried out; 2) the components of a deep learning model for valuing an option on the RTS Index are determined; 3) a statistical interpretation of the processed data was carried out, 4) a neural network was built for put and call options. Materials and methods. Statistical analysis and neural networks apparatus were used in modeling. Conclusions. A study was made of the statistical characteristics of the underlying asset on the RTS Index futures; an algorithm was developed that uses the fair value of money indicator RUSFAR, calculated by the Moscow Exchange, instead of using zero-coupon rates, which are biased due to the incomplete backing of funds by assets to assess risk-free borrowing rates; the obtained results of the models are interpreted and conclusions are formulated regarding the quality of the obtained models.

Grineva N.V., (2022), BUILDING A NEURAL NETWORK TO PREDICT THE OPTION PRICE. Economic Problems and Legal Practice, 5 => 190-199.

Hutchinson James M., Lo Andrew W. Poggio Tomaso A Nonparametric Approach to Pricing and Hedging Derivative Securities Via Learning Networks // The Journal of Finance. -Boston: Papers and Proceedings Fifty-Fourth Annual Meeting of the American Finance Association, January 3-5, 1994 g. -P. 851-889.
Li Wenda Application of Machine Learning in Option Pricing: A Review // Advances in Economics, Business and Management Research. - b.m. : Proceedings of the 2022 7th International Conference on Social Sciences and Economic Development (ICSSED 2022), 2022. -652 p.
Yangang Chen Wan Justin W. L. Deep Neural Network Framework Based on Backward Stochastic Differential Equations for Pricing and Hedging American Options in High Dimensions. -2019.
Qiang Zhang Yang Dennis Drift-Independent Volatility Estimation Based on High, Low, Open, and Close Prices // The Journal of Business: The University of Chicago Press, No. 3 (July 2000). -https://www.jstor.org/stable/10.1086/209650 seq 1 : T. Vol. 73. -pp. 477-492. -The Journal of Business, Vol. 73, No. 3 (July 2000), pp. 477-492.
Kingma Diederik P. Lei Jimmy Ba ADAM: A METHOD FOR STOCHASTIC OPTIMIZATION. -ICLR: ICLR, 2015.
Goudenege Ludovic, Andrea Molent Zanette Antonino Machine Learning for Pricing American Options in High-Dimensional Markovian and non-Markovian models. -2019.
Culkin Robert Das Sanjiv R. Machine Learning in Finance: The Case of Deep Learning for Option Pricing: Santa Clara University, August 2, 2017.
Ruf Johannes Wang Weiguan. Neural networks for option pricing and hedging: a literature review: Computational Finance (q-fin.CP), 2020.
Yang Andrew Ke Alexander. Option Pricing with Deep Learning: Stanford University, 2019. -P. 230.
Salvador Beatriz, Oosterlee Cornelis W., Meer Remco Xove TIC Conference European and American Options Valuation by Unsupervised Learning with Artificial Neural Networks. -A Coru a : b.n. , 8-9 October 2020.

Financial market, options execution, neural networks, loss function, option value models.