Search results

The purpose of this study is to examine if partner cross-contagion in audit offices is associated with client reporting quality. To this end, the authors test if the presence in an audit office of a partner with a highly aggressive style is associated with the reporting quality of other partners’ clients. Partners with a highly aggressive style are identified by their tendency to approve favorable client reporting. The authors add to the existing literature that provides limited and equivocal evidence on audit office cross-contagion.

Partner style is determined in an estimation period from 2010 to 2014. Aggressive style is identified when partners tend to approve favorable client reporting, which is shown by a positive value for their clients’ median discretionary accruals. Partners are considered to exhibit a highly aggressive style if they have positive median client discretionary accruals within the 90th percentile. Cross-contagion analysis is then conducted in a test period from 2015 to 2019 by determining if the presence in an office of a partner with a highly aggressive style is associated with the reporting quality of other partners’ clients. Two measures of client reporting quality used. These are the accuracy of current-period accruals in predicting period-ahead cash flows and earnings management related to benchmark beating.

This study finds partner cross-contagion of highly aggressive style in Big 4 offices that is associated with lower client reporting quality for non-Metals and Mining industry clients. This cross-contagion only occurs when the contagious partner has a very high level of aggressive style. This study finds Big 4 partners are susceptible to aggressive style cross-contagion regardless of their own idiosyncratic style. The results of this study show more cross-contagion in small Big 4 offices and mitigation of cross-contagion for economically important clients. Cross-contagion in non-Big 4 offices is observed for Metals and Mining industry clients.

By determining style from partners’ past clients’ discretionary accruals, this study extends prior cross-contagion research that relies on restatements to identify style. This study examines several other cross-contagion issues not addressed in prior studies. These include differences in cross-contagion for Big 4 and non-Big 4 offices and for large and small Big 4 offices, partners’ susceptibility to cross-contagion and the influence of client importance.

The purpose of this paper is to analyze the network path and internal mechanism of risks’ cross-contagion between shadow banks and design strategies for preventing risk infection between shadow banks.

Using the complex network theory, analyze the mechanism of risks’ cross-contagion between shadow banks from the credit network, business relationship network (BRN) and social network (SN); the cross-contagion mechanism using the structural equation model on the basis of China’s shadow banks is tested; based on the three risk infection paths, the prevention and control strategies for risk infection using the mathematical models of epidemic diseases are designed.

There are three network risk contagion paths between shadow banks. One, the credit network, risks are infected crossly mainly through debt and equity relationships; two, the BRN, risks are infected crossly mainly through business network and macro policy transmission; three, investor SN, risks are infected crossly mainly through individual SN and fractal relationships. The following three strategies for preventing risk’s cross-contagion between shadow banks: one, the in advance preventing strategy is more effective than the ex post control strategy; two, increasing the risk management coefficient; three, reducing the number of risk-infected submarkets.

The research of this study, especially the strategies for preventing the risks’ cross-contagion, could provide theoretical and practical guidance for regulatory authorities in formulating risk supervision measures.

The 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.

The 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).

The 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.

The 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.

Supervisors 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.

The 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.

This chapter examines crisis propagation mechanisms to the Southeast European exchange-rate markets during the 1998 Russian crisis and the Turkish crisis of 2001. It focuses on whether and how the crises spread to these markets after interdependencies and common external shocks are accounted for. Results for Albania, Bulgaria and Croatia are presented and compared. Understanding the propagation mechanisms of crises to these countries and the reaction of these markets to such shocks in comparison to trading partners and other countries within Europe is an important issue in the context of their potential accession to the European Monetary Union and the adoption of inflation targeting frameworks. It is found that Albania has relatively isolated financial markets in comparison with the other countries in the sample and is not affected by contagion, while Croatia is mostly affected by the crises directly and indirectly.

The purpose of this study is to develop a precise Islamic securities index forecasting model using artificial neural networks (ANNs).

The data of daily closing prices of KMI-30 index span from Aug-2009 to Oct-2019. The data of 2,520 observations are divided into training and test data sets by using the 80:20 ratio, which corresponds to 2016 and 504 observations, respectively. In total, 25 features are used; however, in model selection step, based on maximum accuracy, top ten indicators are selected from several iterations of predictive models.

The results of feature selection show that top five influencing indicators on Islamic index include Bollinger Bands, Williams Accumulation Distribution, Aroon Oscillator, Directional Movement and Forecast Oscillator while Mesa Sine Wave is the least important. The findings show that the model captures much of the trend and some of the undulations of the original series.

The findings of this study may have important implications for investment and risk management by using index-based products.

Numerous studies proved that traditional econometric techniques face significant challenges in out-of-sample predictability due to model uncertainty and parameter instability. Recent studies show an upsurge of interest in machine learning algorithms to improve the prediction accuracy.