Summary of Neural Network Learning Of Black-scholes Equation For Option Pricing, by Daniel De Souza Santos and Tiago Alessandro Espinola Ferreira
Neural Network Learning of Black-Scholes Equation for Option Pricing
by Daniel de Souza Santos, Tiago Alessandro Espinola Ferreira
First submitted to arxiv on: 9 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computational Finance (q-fin.CP); Pricing of Securities (q-fin.PR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach to solving the Black-Scholes Equation, a fundamental model for stock option pricing. By leveraging Neural Networks, researchers successfully trained models using real-world data from Brazilian companies Petrobras and Vale, demonstrating improved accuracy in forecasting short-term call option prices compared to traditional analytical solutions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The study uses Neural Networks to solve the Black-Scholes Equation for a specific real-world stock options time series. Real-world data from the stock options market was used as the initial boundary to solve the equation. The results show that the network can learn to solve the Black-Scholes Equation and make more accurate short-term call option price forecasts. |
Keywords
» Artificial intelligence » Time series
