Managing Editor’s Letter

Abstract

Cathy Scott General Manager and Publisher In implementing a strategic asset allocation policy, the forecasting of long-term equity market returns is critically important. Although, historically, several econometric models have been employed for forecasting, more recently machine learning methods have been used for that purpose. In “The Best of Both Worlds: Forecasting US Equity Market Returns Using a Hybrid Machine Learning–Time Series Approach,” Haifeng Wang, Harshdeep Singh Ahluwalia, Roger A. Aliaga-Díaz, and Joseph H. Davis explore machine learning methods to forecast 10-year-ahead US stock returns. To compare the relative performance of machine learning methods, the authors compare the accuracy of these methods to the forecasts of one of the most commonly used regression-based forecasting models by asset managers, the traditional Shiller cyclically adjusted price-to-earnings (CAPE) ratio model. The authors find that machine learning techniques can only modestly improve the forecast accuracy of the regression-based CAPE ratio model. Moreover, they actually result in worse performance than a vector autoregressive model (VAR)–based two-step approach introduced in 2018 by three of the authors of this article. However, when the authors implement a hybrid ML-VAR approach (i.e., VAR-based two-step approach with machine learning techniques allowing for unspecified nonlinear relationships), they find up to 56% improvement in real-time forecast accuracy for 10-year annualized US stock returns. They find the ensemble method consistently offers the best out-of-sample forecast. Machine learning applications in finance have shown benefits over traditional linear models in forecasting stock returns. Edward Leung, Harald Lohre, David Mischlich, Yifei Shea, and Maximilian Stroh quantify these benefits by comparing the forecasting performance of commonly used machine learning algorithms with that of traditional linear methods in their article “The Promises and Pitfalls of Machine Learning for Predicting Stock Returns.” Using well-known equity characteristics, the authors forecast returns for largeand mid-cap stocks from various regional indexes using a gradient boosting machine (GBM) algorithm and standard ordinary least squares (OLS) approaches. In doing so, they shed light on the mechanics and results of the GBM model in order to alleviate its black-box character. While the forecasts from GBM models outperform OLS models based on statistical tests of forecasting performance, the economic gains from such nonlinear models depend on the ability to take the appropriate risks and efficiently implement trades. Jochen Papenbrock, Peter Schwendner, Markus Jaeger, and Stephan Krügel in their article “Matrix Evolutions: Synthetic Correlations and Explainable Machine Learning for Constructing Robust Investment Portfolios” use evolutionary algorithms to simulate correlation matrixes useful for financial market applications. Referring to their novel approach to generate realistic correlation matrixes as “matrix evolutions,” they explain how it can be used for many applications in asset management, such as generating risk scenarios for portfolios, pricing of multi-asset derivatives, backtesting investment strategies, and hedging correlation-dependent investment strategies and financial products. The potential application of matrix evolutions is demonstrated by the authors in a machine learning case study for robust portfolio construction in a multi-asset universe that shows how an explainable machine learning program b y gu es t o n Ju ne 1 5, 2 02 1. C op yr ig ht 2 02 1 Pa ge an t M ed ia L td .