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
Project details
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-0.1.6.tar.gz
(15.9 MB
view details)
File details
Details for the file anomalylab-0.1.6.tar.gz
.
File metadata
- Download URL: anomalylab-0.1.6.tar.gz
- Upload date:
- Size: 15.9 MB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/5.1.1 CPython/3.10.15
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 88791869a69c1a808a70ac720bb21170eff822d9e6bcc56f6f8d98bba7dd060c |
|
MD5 | 725f3d6fddd83d447740aa5036ad3c72 |
|
BLAKE2b-256 | 4e69bbd8b2cd8c3d7f17c04854aa31adc1ff82978a7d00d9927805b2f9879ae0 |