Skip to main content

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


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

anomalylab-0.2.0.tar.gz (16.0 MB view details)

Uploaded Source

File details

Details for the file anomalylab-0.2.0.tar.gz.

File metadata

  • Download URL: anomalylab-0.2.0.tar.gz
  • Upload date:
  • Size: 16.0 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.10.15

File hashes

Hashes for anomalylab-0.2.0.tar.gz
Algorithm Hash digest
SHA256 e730430d097011e79805282c1dc7edf6019caa1b8048ab27409f5ca9740ce10f
MD5 49b7a43d49c378ed8d1a9d06668f24c2
BLAKE2b-256 f54082b2d29bb40943eea568a537141dc49b9cf11819fc5238c1fa9dc6ec2d3f

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page