In this paper, the Local Global Neural Networks model is proposed within the context of time series models. This formulation encompasses some already existing nonlinear models and also admits the Mixture of Experts approach. We place emphasis on the linear expert case and extensively discuss the...

Testing serial dependence is central to much of time series econometrics. A number of tests that have been developed and used to explore the dependence properties of various processes. This paper builds on recent work on nonparametric tests of independence. We consider a fact that characterises...

In this paper we design a simple trading strategy to exploit the hypothesized distinct informational content of the arithmetic and geometric mean. The rejection of cointegration between the two stock market indicators supports this conjecture. The profits generated by this cheaply replicable...

Building predictive models for genomic mining requires feature selection, as an essential preliminary step to reduce the large number of variable available. Feature selection is a process to select a subset of features which is the most essential for the intended tasks such as classification,...

The problem of forecasting time series is very widely debated. In recent years, machine learning algorithms have been very prolific in this area. This paper describes a systematic approach to building a machine learning predictive model for solving optimization problems in the banking sector. A...

Banking segment is one of the ultimate key segments that support the sustainable economic progress in Jordan. Hence, banks in Jordan are considered as tremendously significant financial establishments that pursue profit by providing various financial services to various customers through dealing...