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

Author

Listed:
  • George Athanasopoulos
  • Rob J Hyndman
  • Raffaele Mattera

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.

Suggested Citation

  • George Athanasopoulos & Rob J Hyndman & Raffaele Mattera, 2023. "Improving out-of-sample Forecasts of Stock Price Indexes with Forecast Reconciliation and Clustering," Monash Econometrics and Business Statistics Working Papers 17/23, Monash University, Department of Econometrics and Business Statistics.
    • Handle: RePEc:msh:ebswps:2023-17
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    File URL: https://www.monash.edu/business/ebs/research/publications/ebs/2023/wp17-2023.pdf
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    References listed on IDEAS

    as
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    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    financial time series; hierarchical forecasting; clustering; unsupervised learning; prediction; machine learning; finance;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General

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