Enhancing real estate investment trust return forecasts using machine learning
研究了机器学习模型在预测房地产投资信托基金收益上的表现，发现其优于传统模型，且REITs比股票更可预测，能为投资者带来显著经济收益。
Abstract We extend the emerging literature on machine learning empirical asset pricing by analyzing a comprehensive set of return prediction factors for real estate investment trusts (REITs). We show that machine learning models are superior to traditional ordinary least squares models and find that REIT investors experience significant economic gains when using machine learning forecasts. In particular, we show that REITs are more predictable than stocks and that their higher predictability is stable over time and across industries.