We focus on the stock selection step of the index tracking problem in passive investment management and incorporate constant changes in the dynamics of markets into the decision. We propose an approach, using machine learning techniques, which analyses the performance of the selection methods used in previous market states and identifies the one that gives the optimal tracking portfolio in each period. We apply the proposed procedure using the popular cointegration technique in index tracking and show that it tracks the S&P 500 with a very high level of accuracy. The empirical evidence shows that our proposed approach outperforms cointegration techniques that use a single criterion (e.g., stocks with the maximum market capitalization) in the asset selection.

我们专注于被动投资管理中指数跟踪问题的选股步骤，并将市场动态的不断变化纳入决策。我们提出一种使用机器学习技术的方法，该方法分析了先前市场状态中使用的选择方法的性能，并确定了在每个时期都能提供最佳跟踪组合的方法。我们使用流行的协整技术将建议的过程应用于索引跟踪，并表明它可以非常高的准确性跟踪S＆P 500。经验证据表明，我们提出的方法优于在资产选择中使用单一标准（例如，具有最大市值的股票）的协整技术。