On the predictability of ETF returns with technical predictors
利用全球股票数据训练随机森林模型，用技术指标预测国际股票ETF表现，发现基于排名构建的投资组合月均超额收益达0.76%，且预测在短期和低效市场中更强。
We develop a machine learning framework that uses technical indicators derived from international stock data to forecast the performance of international equity ETFs. To address the limited history of ETFs, we train a random forest model on global stock data and apply it to rank ETFs according to the probability of outperformance. Portfolios formed on this ranking are associated with economically meaningful gross-of-cost return spreads, averaging 0.76% per month ( t -stat = 2.76) and 0.62% after market risk adjustment. Predictability is strongest at short horizons and decays with longer holding periods. Volatility and momentum indicators contribute the most to model performance. In line with limits to arbitrage, predictive strength is more pronounced in less efficient markets and among lower-liquidity ETFs. Results are robust across portfolio construction methods and alternative models, and the signal performs well out-of-sample from 2011 to 2022, with extended evidence up to 2024.