Gated Neural Networks for Option Pricing: Rationality by Design
Authors: Yongxin Yang, Yu Zheng, Timothy Hospedales
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on S&P 500 index options show that our approach is significantly better than others. |
| Researcher Affiliation | Academia | Yongxin Yang, Yu Zheng, Timothy M. Hospedales EECS, Queen Mary, University of London , Imperial Business School, Imperial College London yongxin.yang@qmul.ac.uk, t.hospedales@qmul.ac.uk, y.zheng12@imperial.ac.uk |
| Pseudocode | No | The paper describes the neural network architecture and mathematical formulas, but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper states in a footnote: '1We release the code of these methods in Github: github.com/arraystream/fft-option-pricing'. This refers to the code for the baseline econometric methods, not the authors' proposed neural network method. |
| Open Datasets | No | The option data for S&P500 index comes from Option Metrics and Bloomberg, which provide historical End-of-Day bid and ask quotes. These are commercial data providers, and no public link or specific citation for public access to the dataset is provided. |
| Dataset Splits | No | The paper states: 'we train a model with five continuous trading days data, and use the following one day for testing.' This describes the training and testing split, but no explicit mention of a separate validation set for hyperparameter tuning is made. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions 'Adam Optimiser (Kingma and Ba 2015)' as the optimizer used, but does not specify its version number or any other software dependencies with their versions. |
| Experiment Setup | Yes | For Single and PSSF, the number of hidden layer neurons is J = 5. The number of pricing models in Multi is I = 9 as MNN has this setting. The number of neurons in hidden layer for the right-branch weighting network of Multi is K = 5. |