Improving out-of-sample Forecasts of Stock Price Indexes with Forecast Reconciliation and Clustering

George Athanasopoulos (), Rob Hyndman and Raffaele Mattera

No 17/23, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

Abstract: 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.

Keywords: financial time series; hierarchical forecasting; clustering; unsupervised learning; prediction; machine learning; finance (search for similar items in EconPapers)
JEL-codes: C10 C53 (search for similar items in EconPapers)
Pages: 30
Date: 2023
New Economics Papers: this item is included in nep-big, nep-cmp, nep-fmk and nep-for
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Related works:
Journal Article: Improving out-of-sample forecasts of stock price indexes with forecast reconciliation and clustering (2024)
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