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Abstract: According to the modern portfolio theory, the direction of the relationship between the securities in the portfolio is stated to be effective in reducing the risk. Moreover, securities in high correlation are avoided by taking place in the same portfolio. The models structured by the Bayesian networks are capable of visually illustrate the probabilistic relationship. Also, portfolio returns could be refreshed simultaneously when new information has arrived. The study aims to provide dynamic information through Bayesian networks and to investigate the relationship between macroeconomic indicators and stock returns of Turkish major bank stocks based on the Arbitrage Pricing Model. The dataset includes stock returns of four banks listed in the Borsa Istanbul from June 2001 to January 2017. Besides, macroeconomic variables such as BIST-100 Index, oil prices, inflation, exchange, and interest rate & money supply are gathered for the same period. The results suggest that the Bayesian network models allow dynamics among stock returns could be investigated in more detail. Additionally, it determines that macroeconomic variables would have various impacts on stock returns on bank stocks by comparison of the conventional methods.
Keywords: Arbitrage Pricing Model, Bayesian Networks, Machine Learning, Portfolio Selection Theory, Banking Stocks
JEL: C11, G11, G12
Received : 22 October 2018
Revised : 24 December 2019
Accepted : 18 March 2019
Type : Research
(ProQuest: ... denotes formulae omitted.)
1.Introduction
The purpose of constructing portfolios is to gain returns from financial investments. According to Markowitz, diversification is not singly enough to reduce the risk level of a portfolio. The covariance coefficients between financial assets are also crucial. So, creating a balance between risk and return is essential for investors, when they are constructing their portfolios. In modern portfolio management, the investors aim to gain maximum return for a certain level of risk preference. Also, they take into account the historical movements and those movements are important for the asset return and market return relationship. However, neither modern nor traditional portfolio management theories do not have a systematic update feature. Bayesian network models not only have a systematic update feature but also they have a visualization feature for the linkage between asset returns.
Utilization from Bayesian networks in stock operations is particularly crucial for the circumstances experiencing fast-track changes and exhibiting...