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The aim of this paper is to analyze and test the effects of capital market development on the per-capita GDP growth in Saudi Arabian economy covering the period of 1985-2018. An ARDL, FMOLS and Johansen tests are implemented. The stock market indicators: share price index, capitalization, liquidity, number of share transactions, and number of shares are employed using a log-linear eclectic model designed to fit the availability of data. Capitalization and liquidity came up with negative signs, contrary to the findings of lots of studies in economic literature. However, the share price index, number of shares traded, and the ratio of number of share transactions had the right signs as expected a priori. The findings raise serious questions about the size of the market, the steps and efforts that have been taken to deepen the capital market and their consequences on the function and potency of capital market in fostering per-capita GDP growth. Applying Granger causality test, share price index, market capitalization and number of shares traded do not granger cause per-capita GDP. They are significant at 5 percent level. Capital market authority (CMA) should draw a road map to accelerate deepening the capital market in order to serve economic growth. First published online 23 December 2020

An investigation of the applicability of neural network-based methods in predicting the values of multiple parameters, given the value of a single parameter within a particular problem domain is presented. In this context, the input parameter may be an important source of variation that is related with a complex mapping function to the remaining sources of variation within a multivariate distribution. The definition of the relationship between the variables of a multivariate distribution and a single source of variation allows the estimation of the values of multiple variables given the value of the single variable, addressing in that way an ill-conditioned one-to-many mapping problem. As part of our investigation, two problem domains are considered: predicting the values of individual stock shares, given the value of the general index, and predicting the grades received by high school pupils, given the grade for a single course or the average grade. With our work, the performance of standard neural network-based methods and in particular multilayer perceptrons (MLPs), radial basis functions (RBFs), mixture density networks (MDNs) and a latent variable method, the general topographic mapping (GTM), is compared. According to the results, MLPs and RBFs outperform MDNs and the GTM for these one-to-many mapping problems. © 2010 Springer-Verlag London Limited. 20 6 775 785 Cited By :5