Statistical Analysis Dow Jones Stock Index—Cumulative Return Gap and Finite Difference Method

Kejia Yan, Rakesh Gupta () and Sama Haddad
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Kejia Yan: Department of Accounting, Finance and Economics, Griffith University, Nathan 4111, Australia
Sama Haddad: Griffith Business School, Griffith University, Nathan 4111, Australia

JRFM, 2022, vol. 15, issue 2, 1-44

Abstract: 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.

Keywords: cumulative return; cumulative return gap; cumulative abnormal returns; finite difference; autoregressive model; autoregressive distributed lag model (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2022
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