Black-Scholes and Monte-Carlo Simulation: Design of a Web-Based Stock Option Pricing Accuracy Comparison Application

Authors

  • Michelle Priyatna Statistics Department, School of Computer Science, Bina Nusantara University, Jakarta, Indonesia
  • Samsul Arifin Statistics Department, School of Computer Science, Bina Nusantara University, Jakarta, Indonesia
  • Devi Fitrianah Computer Science Department, Faculty of Humanities, Bina Nusantara University. Jakarta. Indonesia.

Keywords:

Black-Scloles, Monte Carlo, Stock Options, SOCA, Simulation, Python, Prototype

Abstract

Options are legal contracts that give the right to buy/sell the underlying instrument at a specific price and time. For most types of Options, traditional calculation techniques are very difficult to use due to the complexity of the instruments. For this reason, the research intends to create a calculation system that can determine the price of European Type Stock Options using the Monte Carlo and Black-Scholes simulation methods. In this research, an object-oriented application called SOCA was developed using the prototype method, made website-based using the Python programming language. Based on the evaluation results of the user interface using 8 golden rules and 5 measurable human factors, it is known that the available information and features are in accordance with user needs, the application is also easy to use. The results of the evaluation using the black box show that all functional systems can run well. From the results of the evaluation and testing carried out, it can be concluded that the application has been made according to the needs of its users.

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Published

16.07.2023

How to Cite

Priyatna, M. ., Arifin, S. ., & Fitrianah, D. . (2023). Black-Scholes and Monte-Carlo Simulation: Design of a Web-Based Stock Option Pricing Accuracy Comparison Application. International Journal of Intelligent Systems and Applications in Engineering, 11(3), 480–488. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3202

Issue

Vol. 11 No. 3 (2023)

Section

Research Article

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