Economic & Macro Investment Analysis
16 Posts Series – Covering All Basics and Advanced Models
Trusted by Economists and Financial Professionals – 16+ Posts
Good materials for conducting economic and financial analysis
— Stephia.T
The macro materials and techniques covered are imperative for ML analysis
— Jack Rick. Q
I really appreciate Harry and his team for this series of sharing
— 이준 (Tiffany)
List of Content
Economic Finance – Post 1
A 5‑Minute Introduction to Economic Analysis with Code
Learn the core steps of economic analysis by turning real investment questions into simple Python models using GDP and unemployment data.
Economic Finance – Post 2
Pulling Real Macroeconomic Data from FRED and the World Bank with Python
Learn a reusable Python workflow for pulling real macroeconomic data from FRED and the World Bank, running visualizations and regressions.
Economic Finance – Post 3
From One X to Many: Your First Multiple Regression in Economic Analysis
Learn how to build a small macro dataset, run your first multiple regression in Python, and interpret “holding other factors constant”.
Economic Finance – Post 4
Are Your Regressions Lying? A Gentle Intro to Regression Diagnostics in Economic Analysis
Learn how to stress‑test your models with regression diagnostics, then step into causal analysis by building a simple DiD design.
Economic Finance – Post 5
Automating Your Economic Data Workflow with Notebooks and Git
Turn your one‑off economic analyses into a clean, automated project pipeline by structuring folders, chaining Jupyter notebooks, and using Git/GitHub.
Economic Finance – Post 6
Better Investment: Where Machine Learning Fits in Economic Analysis
Learn to combine econometrics and machine learning so you can forecast key macro variables, run train/test‑split prediction baselines.
Economic Finance – Post 7
Regularized Regression for Economists: Ridge and Lasso in Practice
Master Ridge and Lasso regression on realistic economic-style data so you can tame multicollinearity, automate variable selection.
Economic Finance – Post 8
Tree‑Based Models and Random Forests for Economic Data
Learn how tree‑based models and random forests capture nonlinear, regime‑like economic relationships, beat simple OLS in prediction.
Economic Finance – Post 9
Forecasting the Economy: Time‑Series and ML for Macro Prediction
Learn to frame macro forecasting as a supervised ML problem, build lagged time‑series features, compare linear models to random forests.
Economic Finance – Post 10
Text, NLP, and AI: Using Unstructured Data in Economic Analysis
Learn how to turn news, central‑bank speeches, and reports into numeric features with NLP and AI so your economic models capture sentiment.
Economic Finance – Post 11
Economic Model: Macro data retrieval and basic economic relationships
Build a complete “hello world” macro model by pulling real GDP and unemployment data from FRED, cleaning and aligning it in Python.
Economic Finance – Post 12
Economic Model: How Macro Indicators Move with the Stock Market (SPY + FRED in Python)
Connect real macro indicators to the stock market by pulling FRED GDP and unemployment data plus SPY prices into Python.
Economic Finance – Post 13
Economic Model: Predicting SPY Returns with Lagged Macroeconomic Features
Turn macro‑driven SPY prediction into a full ML pipeline by building lagged GDP, unemployment, and return features.
Economic Finance – Post 14
Investment Model: Macro Regimes and a Simple Regime‑Based Asset Allocation Strategy
Build and backtest a macro‑regime asset allocation model by classifying the economy into expansion, neutral, and recession with a random forest.
Economic Finance – Post 15
Investment Model: A Simple Mean‑Variance Portfolio with a Macro “Risk‑On / Off” Overlay
Build a simple three‑asset mean‑variance portfolio, then layer on a SPY‑based “risk‑on / risk‑off” rule that cuts equity exposure after weak trends.