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Predicting BRICS stock returns using ARFIMA models

Goodness C. Aye et al.

Applied Financial Economics2014https://doi.org/10.1080/09603107.2014.924297article
AJG 2ABDC B
Weight
0.67

Abstract

This article examines the existence of long memory in daily stock market returns from Brazil, Russia, India, China and South Africa (BRICS) countries and also attempts to shed light on the efficacy of autoregressive fractionally integrated moving average (ARFIMA) models in predicting stock returns. We present evidence which suggests that ARFIMA models estimated using a variety of estimation procedures yield better forecasting results than the non-ARFIMA (AR, MA, ARMA and GARCH) models with regard to prediction of stock returns. These findings hold consistently for the different countries whose economies differ in size, nature and sophistication.

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https://doi.org/https://doi.org/10.1080/09603107.2014.924297

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@article{goodness2014,
  title        = {{Predicting BRICS stock returns using ARFIMA models}},
  author       = {Goodness C. Aye et al.},
  journal      = {Applied Financial Economics},
  year         = {2014},
  doi          = {https://doi.org/https://doi.org/10.1080/09603107.2014.924297},
}

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Predicting BRICS stock returns using ARFIMA models

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Evidence weight

0.67

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.81 × 0.4 = 0.32
M · momentum0.80 × 0.15 = 0.12
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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