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

Working papers
Authors

Raffaele Mattera, George Athanasopoulos, Rob J Hyndman

Published

31 July 2023

Publication details

Working paper

Links

pdf

 

This paper discusses the use of forecast reconciliation with stock price time series and the corresponding stock index. The individual stock price series may be grouped using known meta-data or other clustering methods. We propose a novel forecasting framework that combines forecast reconciliation and clustering, to lead to better forecasts of both the index and the individual stock price series. The proposed approach is applied to the Dow Jones Industrial Average Index and its component stocks. The results demonstrate empirically that reconciliation improves forecasts of the stock market index and its constituents.