PurposeThe paper models the financial interconnectedness and systemic risk of shadow banks using Granger-causal network-based measures and takes the Indian shadow bank crisis of 2018–2019 as a systemic event.Design/methodology/approachThe paper employs pairwise linear Granger-causality tests adjusted for heteroskedasticity and return autocorrelation on a rolling window of weekly returns data of 52 financial institutions from 2016 to 2019 to construct network-based measures and calculate network centrality. The Granger-causal network-based measure ranking of financial institutions in the pre-crisis period (explanatory variable) is rank-regressed with the ranking of financial institutions based on maximum percentage loss suffered by them during the crises period (dependent variable).FindingsThe empirical result demonstrated that the shadow bank complex network during the crisis is denser, more interconnected and more correlated than the tranquil period. The closeness, eigenvector, and PageRank centrality established the systemic risk transmitter and receiver roles of institutions. The financial institutions that are more central and hold prestigious positions due to their incoming links suffered maximum loss. The shadow bank network also showed small-world phenomena similar to social networks. Granger-causal network-based measures have out-of-sample predictive properties and can predict the systemic risk of financial institutions.Research limitations/implicationsThe study considers only the publicly listed financial institutions. Also, the proposed measures are susceptible to the size of the rolling window, frequency of return and significance level of Granger-causality tests.Practical implicationsSupervisors and financial regulators can use the proposed measures to monitor the development of systemic risk and swiftly identify and isolate contagious financial institutions in the event of a crisis. Also, it is helpful to policymakers and researchers of an emerging economy where bilateral exposures' data between financial institutions are often not present in the public domain, plus there is a gap or delay in financial reporting.Originality/valueThe paper is one of the first to study systemic risk of shadow banks using a financial network comprising of commercial banks and mutual funds. It is also the first one to study systemic risk of Indian shadow banks.

PurposeThis paper aims to investigate the intersection between crowdfunding (CF), open innovation (OI) and responsible innovation (RI) and identify the emerging trends and gaps in research and new paths for CF research in the future. In addition, this paper proposes a conceptual framework and propositions.Design/methodology/approachThis paper is structured in line with the systematic literature review protocol. After reading all the titles, keywords and abstracts, 172 papers focused on OI and RI were selected for this research. Finally, 27 papers that are based on dimensions related to responsible OI were selected for the study.FindingsDue to CF's multidisciplinary nature, the scientific literature on the role of CF in endorsing responsible OI for shared value co-creation appears fragmented and redundant. Several emerging trends and gaps of research and new paths for CF research in the future arise regarding research methodology and theoretical perspective.Originality/valueTo the best of the authors' knowledge, this is the first study investigating the intersection between CF OI and RI.

In the recent financial crises, attention has shifted towards "too-central-to-fail" to recognize the sources of systemic risk. The NBFC Crisis of 2018-19 adversely affected other financial institutions and the real economy of India. The NBFCs crisis highlighted the role of smaller institutions in perpetuating and amplifying the crisis. Thus, the present study models the interconnection of NBFCs with the rest of financial institutions using a complex Granger-causality network based on returns data. The PageRank algorithm identifies the central and important nodes and ranks financial institutions in pre-crisis and crisis periods. The financial institutions are also ranked based on the maximum percentage loss suffered during the crises. Using non-parametric rank-based regression, the PageRank ranking of financial institutions in the pre-crises period (explanatory variable) is regressed with the ranking of financial institutions based on maximum percentage loss suffered by them during the crises period (dependent variable) along with Leverage and Size as control variables. We found that PageRank from pre-crisis can significantly identify most financial institutions that suffered loss during NBFCs crises even in the presence of control variables.