Aye, Goodness ChiomaBalcilar, MehmetGupta, RanganKilimani, NicholasNakumuryango, AmandineRedford, Siobhan2014-07-312014-09Aye, GC, Balcilar, M, Gupta, R, Kilimani, N, Nakumuryango, A & Redford, S 2014, 'Predicting BRICS stock returns using ARFIMA models', Applied Financial Economics, vol. 24, no. 17, pp. 1159-1166.0960-3107 (print)1466-4305 (online)10.1080/09603107.2014.924297http://hdl.handle.net/2263/41029This 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.en© Taylor and Francis. This is an electronic version of an article published in Applied financial economics, vol. 24, no. 17, pp. 1159-1166, 2014. doi : 10.1080/09603107.2014.924297. Applied financial economics is available online at : http://www.tandfonline.com/loi/rafe20.Fractional integrationLong-memoryStock returnsLong-horizon predictionBrazil, Russia, India, China and South Africa (BRICS)Autoregressive fractionally integrated moving average (ARFIMA)ARFIMA modelsBRICS countriesPostprint Article