Improving out-of-sample forecasts of stock price indexes with forecast reconciliation and clustering

 

We propose a novel approach to improving forecasts of stock market indexes by considering common stock prices as hierarchical time series, combining clustering with forecast reconciliation. We propose to group the individual stock price series in various ways including via metadata and using unsupervised learning techniques. The proposed approach is applied to the Dow Jones Industrial Average Index and the Standard & Poor 500 Index and their component stocks, and the results obtained with different grouping approaches are compared. The results demonstrate empirically that the combined use of clustering and reconciliation improves the forecast accuracy of the stock market indexes and their constituents.

Cite

@article{dowhts,
  author  = {Raffaele Mattera and George Athanasopoulos and Rob J Hyndman},
  doi     = {10.1080/14697688.2024.2412687},
  journal = {Quantitative Finance},
  number  = 11,
  pages   = {1641-1667},
  title   = {Improving out-of-sample forecasts of stock price indexes with forecast reconciliation and clustering},
  url     = {https://robjhyndman.com/publications/dow_hts.html},
  volume  = 24,
  year    = 2024
}