Rewriting the code in "Machine Learning for Factor Investing" in Python
- Updated
Feb 9, 2021 - Jupyter Notebook
Rewriting the code in "Machine Learning for Factor Investing" in Python
Value or Momentum? Comparing Random Forests, Support Vector Machines, and Multi-layer Perceptrons for Financial Time Series Prediction & Tactical Asset Allocation
众人的因子回测框架 stock factor test
Data Science Project: Replication of "Forest Through the Trees: Building Cross-Sections of Stock Returns" - creation of assets to test validity of factor models with Python
Calculate technical factors for stocks in an efficient, maintainable and correct way
Web dashboard to visualize equity factor dynamics using solely publicly available data.
In this study, I empirically and statistically investigate the credibility of common asset pricing beliefs using data from S&P 500® constituents from January 2010–December 2020.
VAR vs. LSTM: Multivariate Forecasting of Factor Returns
Bridging engineering expertise and investment savvy through hands-on tutorials, insightful financial analysis, and collaborative learning in Python and beyond.
Machine Learning for Factor Investing: Python Version
A project to estimate a stock's risk with a linear regression model in Python, using the Fama-French Carhart model and live data from Yahoo Finance.
University Project: constructing portfolios by blending different types of factor portfolios (low-beta, value, and momentum). We investigate different techniques to weight our portfolio and calculating a combined score.
Risk Premia Estimation (FamaMacbeth and Three-pass)
Computing Index Prices and Returns from prices/returns of financial assets
Python code for Swade et al. (2023) "Why Do Equally Weighted Portfolio Beat Value-Weighted Ones?" The Journal of Portfolio Management, 49 (5), 167–187.
Statistical study of information decay in cross-sectional equity factors, focusing on optimal holding horizons and portfolio rebalancing. Replication and extension of “Factor Information Decay: A Global Study” by Emlyn Flint and Rademeyer Vermaak (2021).
Python toolkit for estimating global factor loadings, optimizing factor-tilted portfolios, and minimizing tax drag with region-aware factors and account-level location.
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