Summary of The Ai Black-scholes: Finance-informed Neural Network, by Amine M. Aboussalah et al.
The AI Black-Scholes: Finance-Informed Neural Network
by Amine M. Aboussalah, Xuanze Li, Cheng Chi, Raj Patel
First submitted to arxiv on: 15 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computational Finance (q-fin.CP); Machine Learning (stat.ML)
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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 The proposed hybrid approach in this paper combines the theoretical rigor and interpretability of principle-driven models with the adaptability of data-driven machine learning techniques to develop a more versatile methodology for pricing options. The framework, called Finance-Informed Neural Network (FINN), integrates strengths from both methodologies to yield improved predictive accuracy while maintaining adherence to core financial principles. FINN is validated across different volatility modeling approaches, including constant volatility and stochastic volatility models. The proposed approach demonstrates enhanced predictive performance and maintains interpretability, making it a promising tool for practitioners. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper combines two types of option pricing methods: principle-driven and data-driven. Principle-driven models are based on mathematical equations that describe the behavior of assets, while data-driven models use machine learning to learn patterns from market data. The new approach combines the strengths of both by using a neural network that is informed by financial principles. This helps to make the model more interpretable and accurate. The researchers tested their approach with different types of volatility models and found it performed well across various market conditions. |
Keywords
» Artificial intelligence » Machine learning » Neural network
