2024/03/31 by Nolan Alexander, Alexander, Nolan, William T. Scherer +1 · 1 citation
Decision Sciences · #FOS: Economics and business #Forecasting Techniques and Applications #Portfolio Management (q-fin.PM) #Stock Market Forecasting Methods
paper · doi:10.48550/arxiv.2404.00825
openalex publication_date 2024/03/31 · openalex created_date 2024/04/03 · openalex updated_date 2026/07/28
We propose a novel method to improve estimation of asset returns for portfolio optimization. This approach first performs a monthly directional market forecast using an online decision tree. The decision tree is trained on a novel set of features engineered from portfolio theory: the efficient frontier functional coefficients. Efficient frontiers can be decomposed to their functional form, a square-root second-order polynomial, and the coefficients of this function captures the information of all the constituents that compose the market in the current time period. To make these forecasts actionable, these directional forecasts are integrated to a portfolio optimization framework using expected returns conditional on the market forecast as an estimate for the return vector. This conditional expectation is calculated using the inverse Mills ratio, and the Capital Asset Pricing Model is used to translate the market forecast to individual asset forecasts. This novel method outperforms baseline portfolios, as well as other feature sets including technical indicators and the Fama-French factors. To empirically validate the proposed model, we employ a set of market sector ETFs.