International Diversified Portfolio Optimization With Artificial Neural Networks

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.

A Comparative Study on Modelling and Forecasting Tourism Revenues: The Case of Turkey

“…Over the years, ANN architectures and models such as MLP, RBF, Hopfield, Jordan, Elman, Kohonen SOM, which are suitable for use in different fields, have been developed. The most widely used ANN architecture for predictive purposes in the social sciences and tourism forecasting field is the back propagation-feed forward multi-layered perceptron (MLP) model (Wong et al, 2000;Zhang & Qi, 2005;Song & Li, 2008;Moreno et al, 2011;Teixeira & Fernandes, 2012;Bayramoğlu & Başarir, 2018). MLP model is very popular because it is widely applicable in solving business related problems such as prediction, classification and modelling (Smith, 2002).…”