This article is investigated the connections between economic growth, trade openness and banking sector depth, using a panel data set including seventeen countries in the Islamic Cooperation Organization (OIC), where participation and conventional banking co-exist, for the period 1990-2016. Using a multivariate framework, it is primarily found that all the variables are not integrated of order one (I). Since the series are not stationary, cross-dependence tests and Westerlund (2007) cointegration analysis are performed to the series and it is determined that the series are cross-dependent and cointegrated. Then, the models are estimated with three estimators by writing the panel as panel ARDL model to determine the long-term and short-term relations. The results of the study indicate a general long-run equilibrium connection between economic growth, trade openness and banking sector depth as well as a short-run connection among these variables. Policy suggestions include those that will increase greater banking sector depth as well as promoted trade openness.

There may not always be actual data available for planning. Predicted data are used especially for future planning. Due to errors in such planning based on prediction, many products enter the reverse logistics network without completing the shelf life. Especially in textile sector, because of fashion, it is the most important point of planning to be able to make accurate estimates in order to avoid unnecessary resource utilization and to provide minimum cost. It is difficult to establish a mathematical model because the prediction problems in real life have multivariate structure and unknown parameters. Most of the studies in literature have been based on time series prediction. But due to changing fashion and demands of consumers, there are significant differences between demand forecasts and real data. So, in the problems with unknown parameters and multivariate structure, Ensemble Machine Learning (EML) methods are preferred recently because they give more accurate results than other prediction methods. Unlike other studies, the product return rate in textile sector has been predicted with the Stacking and Vote algorithms from EML methods in this paper. In this direction, it is aimed to concentrate on the returns of the products sold with the preferences of the customers and to predict the returns more accurately. In this way, the consumer information obtained as a result of the analyzes can provide more accurate planning in avoiding unnecessary production, transportation and storage activities, reducing costs, resource utilization and environmental pollution. In addition, it is one of the main aims of the study to contribute to the literature by determining the parameters that can be used in predicting the return rates. Highest performance results were obtained with Stacking algorithm. The obtained results were given comparatively and the correlation coefficient of 86.07% was reached.

Many textile products are in reverse logistics network due to mistakes made in activities such as sales forecasting, inventor y planning and distribution. In order to reduce resource usage and cost at first step, in addition to producing the correct quantity, these products must be sent to branches, in correct properties (amount, color, size, model…) and transportation planning and stock planning should be done correctly. Statistical methods, artificial intelligence and machine learning methods are used because of the difficulty of establishing mathematical models in multi-parameter and multi-variable problems. In general, all these activities are based on demand forecasts by time series, but there are important differences between these demand predictions and the actual demands because of fashion and consumers' requests change very quickly. Artificial intelligence and machine learning methods provide faster and more accurate results in complex data sets. The difference of this study from other studies is to estimate the product return rates in Reverse Logistics with Machine Learning. In this direction, it is aimed to predict the claims accurately by concentrating on the customers' preferences, their reasons and the replies of the products which are sold to the customers. Thus, the consumer information obtained as a result of these analyzes can provide us with more accurate planning in terms of avoiding unnecessary production, transportation and storage activities, and sending the products with the correct properties; amount, color, size and model, to the branches. Best results (the correlation coefficient value is 82.35% and lowest error metrics) of this study are obtained with M5P algorithms of machine learning techniques

Model karmaşıklığı, modellerin başarısındaki en önemli ölçütlerden birisidir. Bu çalışmada bugüne dek model karmaşıklığının kontrolünde öne çıkan yaklaşımlar başlıklar halinde incelenmiştir. Bunlar Occam'ın usturası, Popper'ın yanlışlanabilirliği ve istatiksel öğrenme teorisidir. Occam'ın usturası ve Popper'ın yanlışlanabilirliği model karmaşıklığının kontrolünde, evet bir felsefi yaklaşım sağlamaktadırlar ve kabul de görmektedirler. Fakat model karmaşıklığının nasıl kontrol edileceği konusunda matematiksel bir formülasyon sağlamamaktadırlar. Fakat istatiksel öğrenme teorisinin (diğer adıyla, VC teorisi) konuya yaklaşımı yalnızca felsefi bir düzeyde kalmamakta, aynı zamanda şimdiye dek geliştirilen modellerde kullanılan ampirik risk minimizasyonu (ARM) ilkesine VC katsayısını ilave ederek yeni bir risk minimizasyonu (yapısal risk minimizasyonu, YRM) ilkesi getirmektedir. Sonuç olarak Vapnik ve Chervonenkis tarafından geliştirilen VC teorisi, bir kontrol modeli olarak, ispatlanmış matematiksel arka planı ve oldukça başarılı olan sonuçları itibariyle, model karmaşıklığının kontrolü konusunda, günümüz çerçevesinde, en tutarlı ve güvenilir bir yaklaşım olarak, model geliştiriciler için iyi bir ilham kaynağı olabilir.

The portfolio optimization is an important research field of the financial sciences. In portfolio optimization problems, it is aimed to create portfolios by giving the best return at a certain risk level from the asset pool or by selecting assets that give the lowest risk at a certain level of return. The diversity of the portfolio gives opportunity to increase the return by minimizing the risk. As a powerful alternative to the mathematical models, heuristics is used widely to solve the portfolio optimization problems. The genetic algorithm (GA) is a technique that is inspired by the biological evolution. While this book considers the heuristics methods for the portfolio optimization problems, this chapter will give the implementing steps of the GA clearly and apply this method to a portfolio optimization problem in a basic example.