Proceedings of the 18th International Academic Conference, London

A COMPARATIVE STUDY OF STOCK PRICE FORECASTING USING NONLINEAR MODELS

LAWRENCE XABA, NTEBOGANG MOROKE, JOHNSON ARKAAH, CHARLEMAGNE POOE

Abstract:

This study compared the in-sample forecasting accuracy of three forecasting nonlinear models namely: the Smooth Transition Regression (STR) model, the Threshold Autoregressive (TAR) model and the Markov-switching Autoregressive (MS-AR) model. Data used was daily close stock prices of five banks in the South African banking sector and was obtained from the Johannesburg Stock Exchange (JSE). It covered the period from 2010 to 2012 with a total of 563 observations. Nonlinearity and nonstationarity tests used confirmed the validity of the assumptions of the study. The study used model selection criteria, SBC to select the optimal lag order and for the selection of appropriate models. The Mean Square Error (MSE), Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) served as the error measures in evaluating the forecasting ability of the models. The MS-AR models proved to perform well with lower error measures as compared to LSTR and TAR models in most cases.

Keywords: Stock price, nonlinear time series models, error metrics

DOI: 10.20472/IAC.2015.018.138

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