Weekly Quantitative Paper Digest (Feb 14‑Feb 20, 2026)

This article presents concise summaries of three recent arXiv papers covering a high‑performance Python library for systematic financial factor computation, a self‑evolving agent for discovering explainable alpha factors, and an empirical study of the Shanghai‑Hong Kong Stock Connect's impact on A‑H share price premiums under varying market efficiency conditions.

Quantitative Financealpha discoveryarXiv

0 likes · 9 min read

Weekly Quantitative Paper Digest (Feb 14‑Feb 20, 2026)

Model Perspective

Sep 28, 2025 · Fundamentals

Unlock Hidden Patterns: When to Use PCA vs Factor Analysis

This article explains the core ideas, mathematical steps, geometric intuition, and practical differences between Principal Component Analysis and Factor Analysis, guiding readers on when to apply each technique for dimensionality reduction and latent structure discovery in high‑dimensional data.

Data SciencePCAdimensionality reduction

0 likes · 11 min read

Unlock Hidden Patterns: When to Use PCA vs Factor Analysis

Model Perspective

Mar 3, 2023 · Fundamentals

Unlock Hidden Patterns: A Practical Guide to Factor Analysis with Python

Factor analysis, a statistical technique for uncovering underlying common factors among variables, is explained alongside its distinction from PCA, detailed procedural steps, adequacy tests, and a hands‑on Python implementation using the factor_analyzer library with visualizations and factor rotation methods.

Pythondata preprocessingfactor analysis

0 likes · 10 min read

Unlock Hidden Patterns: A Practical Guide to Factor Analysis with Python

Model Perspective

Sep 1, 2022 · Fundamentals

Master Factor Analysis in Python: From Theory to Practical Implementation

This article explains the origins and core concepts of factor analysis, outlines its algorithmic steps, demonstrates how to perform the analysis using Python's factor_analyzer library—including data preparation, adequacy tests, eigenvalue selection, rotation, and visualization—culminating in extracting new latent variables.

Data SciencePythondimensionality reduction

0 likes · 10 min read

Master Factor Analysis in Python: From Theory to Practical Implementation

Meiyou UED

Dec 1, 2015 · Fundamentals

Unlocking Insights: How Exploratory Factor Analysis Simplifies Complex Data

This article introduces exploratory factor analysis as a powerful dimensionality‑reduction method, explains its historical origins, describes its relationship to confirmatory factor analysis, and demonstrates its practical use in consumer‑value research by extracting four interpretable factors.

consumer researchdimensionality reductionexploratory factor analysis

0 likes · 4 min read

Unlocking Insights: How Exploratory Factor Analysis Simplifies Complex Data