- Open Access
© The Author(s). 2018
- Received: 8 September 2018
- Accepted: 12 September 2018
- Published: 20 September 2018
The eleventh issue of Financial Innovation (FIN), Volume 4, No. 3 (2018) presents six papers contributed by 16 authors and co-authors from five countries and areas: Bangladesh, China, India, Pakistan and UK.
The first paper, “Financial frictions and the cash flow – external financing sensitivity: evidence from a panel of Pakistani firms”, by Abdul Rashid and Noshaba Jabeen, uses a large panel of Pakistani non-financial firms over the period 2000–2013 to examine the role of financial constraints in establishing the relationship between cash flow and external financing. The second paper, “Herding behavior in Ramadan and financial crises: the case of the Pakistani stock market”, by Imran Yousaf, Shoaib Ali and Syed Zulfiqar Ali Shah, examines herding behavior in the Pakistani Stock Market under different market conditions, focusing on the Ramadan effect and Crisis period by using data from 2004 to 2014. The third paper, “Beta through the prism of wavelets”, by Aasif Shah, Arif Tali and Qaiser Farooq, shows how wavelet decomposition can provide an easy vehicle to study the systematic risk properties of return series to serve as protocol for different traders who view the market with different time resolutions. The fourth paper, “Modelling trust evolution within small business lending relationships”, by Ying Tang, Andrea Moro, Sandro Sozzo and Zhiyong Li, investigates the intrinsic motivation of trust and it proposes a theoretical model of trust evolution that is based on the notion of ‘trust response’ and ‘trust spiral’. The fifth paper, “Nexus between financial innovation and economic growth in South Asia: evidence from ARDL and nonlinear ARDL approaches”, by Sarat Chandra Nayak and Bijan Bihari Misra, examined the relationship between financial innovation and economic growth in Bangladesh, India, Pakistan, and Sri Lanka for the period Q1 1975 to Q4 2016. The sixth paper, “Estimating stock closing indices using a GA-weighted condensed polynomial neural network”, by Sarat Chandra Nayak and Bijan Bihari Misra, proposes a novel condensed polynomial neural network (CPNN) for the task of forecasting stock closing price indices.
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