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

Raffaele Mattera, George Athanasopoulos and Rob Hyndman

Quantitative Finance, 2024, vol. 24, issue 11, 1641-1667

Abstract: In this paper, 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 grouping 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 empirically demonstrate that the combined use of clustering and reconciliation improves the forecast accuracy of the stock market indexes and their constituents.

Date: 2024
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Working Paper: Improving out-of-sample Forecasts of Stock Price Indexes with Forecast Reconciliation and Clustering (2023)
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DOI: 10.1080/14697688.2024.2412687

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