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International Diversified Portfolio Optimization With Artificial Neural Networks: An Application With Foreign Companies Listed on NYSE
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Author(s): Mehmet Fatih Bayramoglu (Bulent Ecevit University, Turkey)and Cagatay Basarir (Bandirma Onyedi Eylul University, Turkey)
Copyright: 2019
Pages: 23
Source title:
Machine Learning Techniques for Improved Business Analytics
Source Author(s)/Editor(s): Dileep Kumar G. (Adama Science and Technology University, Ethiopia)
DOI: 10.4018/978-1-5225-3534-8.ch009
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Abstract
Investing in developed markets offers investors the opportunity to diversify internationally by investing in foreign firms. In other words, it provides the possibility of reducing systematic risk. For this reason, investors are very interested in developed markets. However, developed are more efficient than emerging markets, so the risk and return can be low in these markets. For this reason, developed market investors often use machine learning techniques to increase their gains while reducing their risks. In this chapter, artificial neural networks which is one of the machine learning techniques have been tested to improve internationally diversified portfolio performance. Also, the results of ANNs were compared with the performances of traditional portfolios and the benchmark portfolio. The portfolios are derived from the data of 16 foreign companies quoted on NYSE by ANNs, and they are invested for 30 trading days. According to the results, portfolio derived by ANNs gained 10.30% return, while traditional portfolios gained 5.98% return.
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