factor-models
There are 1 repository under factor-models topic.
purvasingh96 / AI-for-Trading
📈This repo contains detailed notes and multiple projects implemented in Python related to AI and Finance. Follow the blog here: https://purvasingh.medium.com
Language:Jupyter Notebook 398quantbelt / ib_fundamental
Interactive Brokers Fundamental data for humans
Language:Python 94gzj1992 / Portfolio_Construction
Portfolio Construction Functions under the Basic Mean_Variance Model, the Factor Model and the Black_Litterman Model.
Language:Python 41oronimbus / tactical-asset-allocation
Implements different approaches to tactical and strategic asset allocation
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lakshmiDRIP / DROP-Asset-Allocation
DRIP Asset Allocation is a collection of model libraries for MPT framework, Black Litterman Strategy Incorporator, Holdings Constraint, and Transaction Costs.
almgren-chrissasset-allocationblack-littermanconstrained-portfolio-optimizationdripdrip-asset-allocationfactor-modelsidzorekjavamarket-impact-modelsmean-variance-optimizationmulti-period-portfolio-optimizationoptimal-dynamic-portfoliooptimal-executionoptimal-transactionportfolio-constructionprincipal-program-tradesstochastic-liquidity-volatilitytransaction-costLanguage:CSS 10wecarsoniv / augmented-pca
Repository for the AugmentedPCA Python package.
pcaprincipal-component-analysisfactor-modelfactor-modelsdimensionality-reductiondimension-reductionrepresentation-learningmachine-learningadversarial-machine-learningfair-machine-learningaugmented-pcaaugmentedpcaautoencoderautoencoderssupervised-autoencoderadversarial-autoencoderadversarial-autoencoderslinear-modelssupervised-autoencodersLanguage:Python 10a91quaini / intrinsicFRP
An R package for Factor Model Asset Pricing
Language:HTML 8haeran-cho / fnets
Implementation of the FNETS methodology proposed in Barigozzi, Cho and Owens (2024) for network estimation and forecasting of high-dimensional time series
Language:R 8gionikola / DynamicFactorModeling.jl
Julia package for simulating and estimating multi-level/hierarchical dynamic factor models (HDFMs).
Language:Julia 7cateschi / High_dimensional_state_space_model
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.
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zjwang1013 / sparseGFM
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.
Language:R 3rwuebker / quant_modeling
A repo to explore quantitative finance models, libraries and tooling.
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lunageens / PerceivedMarketDependence
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).
Language:Jupyter Notebook 1miindisponi99 / Statistical-Factor-Model
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)
Language:Jupyter Notebook 1- Black-Litterman-Implied-Covariance
samueldecornez62 / Black-Litterman-Implied-Covariance
Index and Factor Construction with Implied Covariance Process
Language:Jupyter Notebook 1 tezzachris / Commodity_Prices_Models
Estimation Commodity Pricing Factor Models
Language:MATLAB 1alexwky / missLASSO
Penalized regression for multiple types of many features with missing data using expectation-maximization (EM) algorithm.
0buddhikaum / InferringLatentStates
This is the code for our publication Inferring Latent States in a Network Influenced by Neighbor Activities: An Undirected Generative Approach, IEEE International Conference on Acoustics, Speech and Signal Processing, New Orleans, LA, 2017
Language:C++ 0PLagat / Asset-Pricing
Estimating CAPM Betas of an equally weighted portfolio of Apple and Google from 2016 to 2021
0chirindaopensource / high_dimensional_matrix_variate_diffusion_index_models
End-to-end Python implementation of Ma et al.'s (2025) matrix-variate diffusion index models for macroeconomic forecasting. Features α-PCA factor extraction, supervised screening, and ILS estimation for high-dimensional forecasting with preserved structural information.
dimension-reductioneconometricsfactor-modelsfinancial-modelinghigh-dimensional-statisticsmatrix-factorizationmonte-carlo-simulationnumpyprincipal-component-analysispythonquantitative-financeresearch-replicationscientific-computingstatistical-computingstatistical-modelingsupervised-learningtime-series-analysistime-series-forecastingdiffusion-indexmacroeconomic-forecastingLanguage:Jupyter NotebookEnd-to-End Python implementation of Massacci et al.'s (2025) novel Randomized Alpha Test for high-dimensional factor models. Features robust OLS estimation, Extreme Value Theory-based inference, Monte Carlo simulation engine, and rolling-window empirical analysis. Handles N>T panels with non-Gaussian, heteroskedastic returns.
asset-pricingeconometricsempirical-researchextreme-value-theoryfactor-modelsfinancefinancial-modelinghigh-dimensionalhypothesis-testingmonte-carlopanel-dataportfolio-analysispythonquantitative-financerandomized-testingresearch-replicationstatistical-computingstatistical-inferencetime-seriesacademic-softwareLanguage:Jupyter Notebookcsciulla / portfolio-risk-simulator
Interactive portfolio risk simulator with Monte Carlo stress testing, factor shocks, and historical crisis replay.
Language:Jupyter Notebookneuroquant / conf2017-factormodel
Approximate Factor Models for Artifact Free Correlation Estimation
Language:TeXprinceoncada / quant-pca-risk
Applies Principal Component Analysis (PCA) to daily returns of 20 US equities (2015–2025) to uncover hidden risk factors. Explores variance explained, scree, loadings, factor returns, covariance reconstruction, and Varimax rotation. Results show 3–5 PCs capture ~75% of portfolio risk.
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