Artikel

Statistical analysis Dow Jones Stock Index: Cumulative return gap and finite difference method

This study was motivated by the poor performance of the current models used in stock return forecasting and aimed to improve the accuracy of the existing models in forecasting future stock returns. The current literature largely assumes that the residual term used in the existing model is white noise and, as such, has no valuable information. We exploit the valuable information contained in the residuals of the models in the context of cumulative return and construct a new cumulative return gap (CRG) model to overcome the weaknesses of the traditional cumulative abnormal returns (CAR) and buy-and-hold abnormal returns (BHAR) models. To deal with the residual items of the prediction model and improving the prediction accuracy, we also lead the finite difference (FD) method into the autoregressive (AR) model and autoregressive distributed lag (ARDL) model. The empirical results of the study show that the cumulative return (CR) model is better than the simple return model for stock return prediction. We found that the CRG model can improve prediction accuracy, the term of the residuals from the autoregressive analysis is very important in stock return prediction, and the FD model can improve prediction accuracy.

Sprache
Englisch

Erschienen in
Journal: Journal of Risk and Financial Management ; ISSN: 1911-8074 ; Volume: 15 ; Year: 2022 ; Issue: 2 ; Pages: 1-44 ; Basel: MDPI

Klassifikation
Wirtschaft
Thema
cumulative return
cumulative return gap
cumulative abnormal returns
finite difference
autoregressive model
autoregressive distributed lag model

Ereignis
Geistige Schöpfung
(wer)
Yan, Kejia
Gupta, Rakesh
Haddad, Sama
Ereignis
Veröffentlichung
(wer)
MDPI
(wo)
Basel
(wann)
2022

DOI
doi:10.3390/jrfm15020089
Handle
Letzte Aktualisierung
10.03.2025, 11:45 MEZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Artikel

Beteiligte

  • Yan, Kejia
  • Gupta, Rakesh
  • Haddad, Sama
  • MDPI

Entstanden

  • 2022

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