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-0.3.2.tar.gz (16.0 MB view details)

Uploaded Source

File details

Details for the file anomalylab-0.3.2.tar.gz.

File metadata

  • Download URL: anomalylab-0.3.2.tar.gz
  • Upload date:
  • Size: 16.0 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.0.1 CPython/3.10.15

File hashes

Hashes for anomalylab-0.3.2.tar.gz
Algorithm Hash digest
SHA256 99a45fd8598cdb24c49cb756fe1871881ab267f3330901bb7ba082f2dea5cffd
MD5 180bcf29bc35d174382eb3bccc12e961
BLAKE2b-256 56e5ec387b7bfaebe501b6121968a2a2fde7da3965dc4057d643003992301b43

See more details on using hashes here.

Supported by

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