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Working Paper
Machine Learning and the Implementable Efficient Frontier
August 18, 2022
We propose that investment strategies should be evaluated based on their net-of-trading-cost return for each level of risk, which we term the "implementable efficient frontier." While numerous studies use machine learning return forecasts to generate portfolios, their agnosticism toward trading costs leads to excessive reliance on fleeting small-scale characteristics, resulting in poor net returns. We develop a framework that produces a superior frontier by integrating trading-cost-aware portfolio optimization with machine learning
Journal Article
Enhanced Portfolio Optimization
March 2, 2020
We show how to identify the portfolios that cause problems in standard mean-variance optimization (MVO) and develop an enhanced portfolio optimization (EPO) method that addresses the problems. Applying EPO on several realistic datasets, we find significant gains relative to standard benchmarks.
Working Paper
Predicting Returns with Text Data
December 19, 2019
We introduce a new text-mining methodology that extracts sentiment information from news articles to predict asset returns.
Journal Article
Empirical Asset Pricing via Machine Learning
October 17, 2018
We show how the field of machine learning can be used to empirically investigate asset premia including momentum, liquidity, and volatility.