A Python package for empirical asset pricing analysis.
Project description
AnomalyLab
Authors
Chen Haiwei, Deng Haotian
Overview
This Python package implements various empirical methods from the book Empirical Asset Pricing: The Cross Section of Stock Returns by Turan G. Bali, Robert F. Engle, and Scott Murray. The package includes functionality for:
- Summary statistics
- Correlation analysis
- Persistence analysis
- Portfolio analysis
- Fama-MacBeth regression (FM regression)
Additionally, we have added several extra features, such as:
- Missing value imputation
- Data normalization
- Leading and lagging variables
- Winsorization/truncation
- Transition matrix calculation
Installation
The package can be installed via:
pip install <anomalylab>
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
anomalylab-0.1.2.tar.gz
(38.6 MB
view details)
Built Distribution
File details
Details for the file anomalylab-0.1.2.tar.gz.
File metadata
- Download URL: anomalylab-0.1.2.tar.gz
- Upload date:
- Size: 38.6 MB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/5.1.1 CPython/3.10.15
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | b7421ae0f132b351339c3af2c225f280ad1f84b086017ae15d166710b4adfdab |
|
| MD5 | 3a09a674289713c9f7edabd2ccf3c0b4 |
|
| BLAKE2b-256 | ec3e7aa0ea0897fe475bb9427d1d6bc68d49b9652cc7b3996e8c4667574f1603 |
File details
Details for the file AnomalyLab-0.1.2-py3-none-any.whl.
File metadata
- Download URL: AnomalyLab-0.1.2-py3-none-any.whl
- Upload date:
- Size: 38.8 MB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/5.1.1 CPython/3.10.15
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | af9043ef237067ecc90fa95d8606115df67b2cb7649a632c2d3c8ee0327657bf |
|
| MD5 | 3172dc439392d5238427237e58c621f4 |
|
| BLAKE2b-256 | 42beb7b80e1cf708e296eb533851d3fdccdf31b1c91929b43f31ff95f2a7f312 |
