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The fourteenth issue of Financial Innovation (FIN), Volume 5, No.3 (2019) presents six papers contributed by 16 authors and co-authors from seven countries and areas: China, India, Iran, Nigeria, Pakistan, Palestine and USA.

The first paper, “The impact of bank lending on Palestine economic growth: an econometric analysis of time series data”, by Ibrahim M. Awad and Mohammed S. Al Karaki, examines the impact of bank lending on economic growth in Palestine using Johansen co-integration, Vector Autoregressive Model and Vector Error Correction Model. The second paper, “A dynamic credit risk assessment model with data mining techniques: evidence from Iranian banks”, by Somayeh Moradi and Farimah Mokhatab Rafiei, produces a table of bad customers on a monthly basis and creating a dynamic model based on the table. The third paper, “Indian stock market prediction using artificial neural networks on tick data”, by Dharmaraja Selvamuthu, Vineet Kumar and Abhishek Mishra, uses neural networks based on three different learning algorithms, i.e., Levenberg-Marquardt, Scaled Conjugate Gradient and Bayesian Regularization for stock market prediction based on tick data as well as 15-min data of an Indian company and their results compared. The fourth paper, “Value chain financing and plantain production in Nigeria: an ex-ante approach”, by Mathew Paul Ojo and Adeolu Babatunde Ayanwale, analyzes the potential impact of VCF on plantain production in Nigeria to ascertain the benefits derivable from VCF. The fifth paper, “On the monetary measures of global liquidity”, by Israr Ahmad Shah Hashmi and Arshad Ali Bhatti, onstructs and examines the dynamics of theoretical and atheoretical measures of global liquidity, using monthly data on the components of broad money over the period 2001 M12–2017 M12 for 39 high income countries. The sixth paper, “A statistical learning approach for stock selection in the Chinese stock market”, by Wenbo Wu, Jiaqi Chen, Liang Xu, Qingyun He and Michael L. Tindall, addresses the stock selection process as a statistical learning problem and build cross-sectional forecast models to select individual stocks in the Shanghai Composite Index.

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Correspondence to Gang Kou.

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