A Comparison Of Linear And Nonlinear Models In Predicting Stock Returns At The Nairobi Stock Exchange
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Date
2009-10Author
Gichana, Isaiah Mboto
Type
ThesisLanguage
enMetadata
Show full item recordAbstract
Empirical literature shows that stock returns could be nonlinear. However, studies on the
nonlinear behavior of stock returns in emerging markets are limited. This study aims at filling
this knowledge gap by comparing linear and nonlinear models in predicting stock returns at
the NSE. The study compared the Random Walk Model, Moving Average Models,
Autoregressive model ARMA models, Autoregressive conditional Heteroskedasticity
(ARCH) models.
The Nairobi stock Exchange index was used as a proxy for stock prices and hence changes in
the NSE index represented stock returns. The sample period consisted of daily observations
of the NSE index. The Akaike Information Criterion (AIC) and the Bayesian Information
Criterion (BIC) were used to select the best fitting model from each type of models. Then the
best fitting model from each type of models were used to predict returns over the sample
period of three months. The mean absolute error (MAE) and the Root mean square error
(RMSE) were used to select the best model. The results indicate that (ARCH (1) performs
better than the other models. Therefore this study concluded that nonlinear models are better
than linear models in predicting stock returns on the NSE. Thus stock returns are nonlinear.
Citation
Masters of Business Administration, University of Nairobi (2009)Publisher
University of Nairobi School of Business