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

Abstract

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 İstanbul 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.

Do the Fama and French Five-Factor Model Forecast Well Using Ann?

“…Some notable studies in applying Artificial Neural Networks to asset pricing models are those of (Cao, Leggio, & Schniederjans, 2005;Cao, Parry, & Leggio, 2011;Gokgoz & Sezgin-Alp, 2014). These studies report that the neural networks along with Capital asset pricing model (CAPM) and Fama and French three-factor model (FF3F) provide a valuable linkage of the asset pricing theory with ANN and report significant improvement in the prediction performance of ANN in the long run.…”