Forecasting Inflation With the Hedged Random Forest
This paper explores inflation forecasting using a hedged random forest (HRF) model. Extensive empirical analysis demonstrates that this paper’s proposed approach consistently outperforms the standard random forest.
Improved Tracking-Error Management for Active and Passive Investing
Discover how shrinkage estimators and multivariate GARCH models improve tracking-error management for benchmarked portfolios, enhancing risk control and performance
Causal Factor Analysis is a Necessary Condition for Investment Efficiency
Lopez de Prado, Lipton, & Zoonekynd on factor model misspecification leading to investment inefficiency and biased portfolios. Discover why causal factor modeling is essential for sound portfolio optimization
The Hedged Random Forest
Enhance random forest regression with optimized weighting inspired by portfolio selection. Discover a new method for improved forecasting accuracy across datasets.
The Case for Causal Factor Investing
Factor investing models are often misspecified, leading to biased risk premia estimates. Learn why causal inference, not associational methods, is key to accurate factor modeling.
The Three Types of Backtests
Improving backtesting reliability in systematic investing. Resreach into walk-forward testing, resampling, and Monte Carlo simulations to avoid biases and false discoveries
Why Has Factor Investing Failed?: The Role of Specification Errors
Factor investing underperforms due to model specification errors, not just p-hacking. Research into how causal factor investing can improve strategy reliability and profitability.
Lopez de Prado, M., and V. Zoonekynd (2024).
ADIA Lab Research Paper Series, No. 7.
A Geometric Approach to Asset Allocation with Investor Views.
Antonov, Balasubramanian, Lopez de Prado, & A. Lipton explore a geometric approach to portfolio optimization using the Generalized Wasserstein Barycenter, offering more flexibility and rewards than Black-Litterman models.
Overcoming Markowitz's Instability with the Help of the Hierarchical Risk Parity (HRP): Theoretical Evidence.
Professor Lipton, Antonov, and M. Lopez de Prado Compare Markowitz and Hierarchical Risk Parity (HRP) portfolio allocation, & how HRP reduces noise, improves robustness, and optimizes portfolio variance.
Hydrodynamics of Markets: Hidden Links Between Physics and Finance.
Professor Alex Lipton explores the connection between physics and financial engineering through Kelvin waves, solving key models like Black-Scholes, Heston, and volatility swaps.
Data Driven Dimensionality Reduction to Improve Modelling Performance
Chung, Lopez de Prado, H. Simon, and K. Wu explore feature clustering for dimensionality reduction in noisy data using parallel computing framework to optimize hyperparameters for improved model accuracy.
Ranking Empirical Evidence in Finance
This article proposes a hierarchy of empirical evidence in relation to causal claims relating to the use of causal inference in financial economics.
Lopez de Prado, M. (2023) Finance. ADIA Lab Research Paper Series, No. 4.
Connecting the Dots in Trustworthy Artificial Intelligence: From AI Principles, Ethics, and Key Requirements to Responsible AI Systems and Regulation.
Trustworthy Artificial Intelligence (AI) is based on seven technical requirements sustained over three main pillars that should be met throughout the system's entire life cycle: it should be lawful, ethical, and robust
Díaz-Rodríguez, N., J. Del Ser, M. Coeckelbergh, M. Lopez de Prado, E. Herrera-Viedma, and F. Herrera (2023).
Where are the Factors in Factor Investing?
Where are the Factors in Factor Investing?, Lopez de Prado, M. (2023). ADIA Lab Research Paper Series, No. 2.
Causal Factor Investing: Can factor investing become scientific?
Causal Factor Investing: Can factor investing become scientific?
Lopez de Prado, M. (2022)