Estimating the Turkish Sectoral Market Returns via Arbitrage Pricing Model under Neural Network Approach

As an alternative to Capital Asset Pricing Model (CAPM), Arbitrage Pricing Theory (APT) is crucial in analyzing practical asset prices. APT provides a kind of multi factor market model which describes the expected returns with respect to macro-economic factors. In multifactor financial modeling, generally the traditional linear models are preferred. However, in the finance literature there are researches indicating the non-stationary and non-linearity of asset prices. For this purpose, in this paper the Artificial Neural Network (ANN) with Feed Forward Back Propagation algorithm has been employed to estimate the expected returns of the main sector indices of the Istanbul Stock Exchange, an emerging stock market, by using their relations with macroeconomic variables over 2003 and 2012 period. The forecasting ability of the ANN model is accessed using in sample and out of sample means square error (MSE) statistics and hypothesis test statistics testing whether there are differences between predicted returns and real returns. The results have revealed that the methodology based on ANN has a significant ability to estimate the different sectors in Turkish stock market with APT approach.

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