📈This repo contains detailed notes and multiple projects implemented in Python related to AI and Finance. Follow the blog here: https://purvasingh.medium.com

Interactive Brokers Fundamental data for humans

Implements different approaches to tactical and strategic asset allocation

Portfolio Construction Functions under the Basic Mean_Variance Model, the Factor Model and the Black_Litterman Model.

R package for fitting high-dimensional multivariate linear mixed effect models

Work with trained factor models in Python

Repository for the AugmentedPCA Python package.

DRIP Asset Allocation is a collection of model libraries for MPT framework, Black Litterman Strategy Incorporator, Holdings Constraint, and Transaction Costs.

Implementation of the FNETS methodology proposed in Barigozzi, Cho and Owens (2024) for network estimation and forecasting of high-dimensional time series

An R package for Factor Model Asset Pricing

Julia package for simulating and estimating multi-level/hierarchical dynamic factor models (HDFMs).

R codes and dataset for the estimation of the high-dimensional state space model proposed in the paper "A dynamic factor model approach to incorporate Big Data in state space models for official statistics" with Franz Palm, Stephan Smeekes and Jan van den Brakel.

Code for implementing Factor Analysis with BLEssing of dimensionality (FABLE).

A toolkit for asset pricing research

Jupyter notebooks implementing Finance projects

sparseGFM implements sparse generalized factor models for dimension reduction and variable selection in high-dimensional continuous, count, and binary data. Stable release available on CRAN (https://cran.r-project.org/package=sparseGFM); development version hosted on GitHub.

A repo to explore quantitative finance models, libraries and tooling.

Index and Factor Construction with Implied Covariance Process

Implemented a statistical factor model using Asymptotic Principal Component Analysis (APCA) and various weighting strategies to improve the performance of a basket of Italian stocks relative to a benchmark (FTSEMIB)

An empirical analysis of European markets. This thesis compares the perceived dependence of stock and market returns, as measured by the frequency of comovement following Ungeheuer and Weber (2020), with the traditional interpretation of market dependency measured by Sharpe’s beta (1964).