CQVAE

KO | EN

gitlite — search

#javascript #hacktoberfest #ai #typescript #llm #go #android #machine-learning #rust #deep-learning #kubernetes #awesome-list #vue #ios

(★ 18)

This resposity is a pre-released verison of Python code used in the paper "Asset pricing via the conditional quantile variational autoencoder".

CQVAE 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# Asset pricing via the conditional quantile variational autoencoder
This resposity is a pre-released verison of Python code used in the paper "[Asset pricing via the conditional quantile variational autoencoder](https://www.researchgate.net/publication/361455269_Asset_pricing_via_the_conditional_quantile_variational_autoencoder)".

If you have any question about this resposity or implement details of paper, please feel free to submit a new issue.
 
## Reproduce the simulation results with certain hyperparameters
```
# CAE
python main.py --model CAE --save-folder logs/CAE --lam 1e-4

# CVAE
python main.py --model CVAE --save-folder logs/CVAE

# CQAE
python main.py --model CQAE --save-folder logs/CQAE --lam 2e-5

# CQVAE
python main.py --model CQVAE --save-folder logs/CQVAE
```

## Reproduce the empirical analysis result
1. Download data from the homepage of Prof. Dacheng Xiu ([homepage link](https://dachxiu.chicagobooth.edu) and [data link](https://dachxiu.chicagobooth.edu/download/datashare.zip)).
2. Preprocess the data by ```python preprocess_data.py```. Please make sure the directories are specified.
3. Train the models by ```python empirical_main.py```. The tuning procedure of hyperparameters and ensemble learning should be done in this step. 
4. Calculate the out-of-sample prediction by using ```python inference_main.py```.

## Citation
Please cite our paper if you feel this code helps.
```
@article{Yang2024CQVAE,
    author = {Xuanling Yang, Zhoufan Zhu, Dong Li and Ke Zhu},
    title = {Asset Pricing via the Conditional Quantile Variational Autoencoder},
    journal = {Journal of Business \& Economic Statistics},
    volume = {42},
    number = {2},
    pages = {681--694},
    year = {2024},
    publisher = {Taylor \& Francis},
    doi = {10.1080/07350015.2023.2223683},
}
```

CQVAE

CQVAE 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

CQVAE

CQVAE 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

Download README