Abstract
Market efficiency has been analyzed through many studies using different linear methods. However, studies on financial econometrics reveal that financial time series exhibit nonlinear patterns because of various reasons. This paper examines market efficiency at Borsa Istanbul using a smooth transition autoregressive (STAR) type nonlinear model. I develop nonlinear ARCH and STAR models, a linear AR model and random walk model for 10 years’ weekly data and then out-of-sample forecast next 12 weeks’ return. Comparing forecast performance powers, I find that the STAR model outperforms random walk, that is Borsa Istanbul returns are predictable at the given period. The results show that the shareholders may earn abnormal return and identify the direction of the return change for the next week with at least 66% accuracy. Contrary to the linear level studies, these findings show that the Borsa Istanbul is not weak form efficient at nonlinear level within the studied period.
Highlights
The efficient-market hypothesis is one of the most famous financial concepts since it was developed in the 1970s. Fama (1970) defines efficient market as a market where asset prices reflect all available information
Results of the parameter constancy test shows that the parameters in the transition functions of the defined LSTAR model are constant in accordance with the theory
Additional exponential smooth transition autoregressive model (ESTAR) model was estimated for these variables, but the insignificance of parameter coefficients and examination tests indicated that the model is not suitable
Summary
The efficient-market hypothesis is one of the most famous financial concepts since it was developed in the 1970s. Fama (1970) defines efficient market as a market where asset prices reflect all available information. Kim et al (2008) have found widespread evidence of nonlinearity in their study of examining asymmetry and nonlinearities of G-7 stock market returns with LSTAR or ESTAR models Their STAR models outperform the linear autoregressive models in forecasting returns that leads investors in developing investment strategies for those countries. Employing a nonlinear ESTAR unit root test, they found that Borsa Istanbul stock price index series exhibit nonlinear behavior that aligns with random walk process Their findings suggest Borsa Istanbul to be weak-form efficient in the research period. They found the returns were chaotic and unpredictable in two days; and concluded that the market was weak form efficient Since this method is inaccurate in long term forecasting, STAR type or Markov chain models are more appropriate to analyze those nonlinear dynamics.
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