Herding behaviour in the Australian loan market and its impact on bank loan quality

We examine the effect of herding behaviour on the credit quality of bank loans in Australia. We find that bank herding varies with different types of loans. It tends to be more prevalent in owner-occupied housing loans and credit cards than other types of loans. During the global financial crisis period, herding in owner-occupied housing loans was most pronounced due to the flight-to-quality phenomenon in the housing sector. Furthermore, we find that the big four banks tend to herd more than smaller and regional banks. Bank herding behaviour is countercyclical, as it is negatively related to real GDP growth and the cost of funding but is positively related to market risk. Regulatory capital requirements may also encourage herding as banks are required to hold less risk-weighted capital for residential loans. Most importantly, bank herding is related to higher impaired assets and therefore lower loan quality. Our findings may have implications for policymakers and bank regulators.

Audit quality, auditor compensation and initial public offering underpricing

We jointly study the impact of audit quality on auditor compensation and initial public offering (IPO) underpricing using a sample of Australian firms going public over the period 1996–2003. We find that quality (Big Four) audit firms earn significantly higher fees than non-Big Four auditors, and audit quality is positively associated with IPO underpricing. The positive relation between audit quality and underpricing is more pronounced for small issues, IPOs underwritten by non-prestigious underwriters, and those that are not backed by venture capitalists. Taken together, our results suggest that quality auditors serve as a signalling device that enhances post-issue market value of equity.

The relationship between exchange rates, interest rates and Australian bank returns

Although there has been significant research on US financial intermediaries' stock returns and sensitivity to interest yields, there has only been limited research on Australian bank stock returns and key macro variables, such as interest rates and exchange rates. The aim of this article is to examine this relationship for four major Australian banks, namely the Australia New Zealand bank (ANZ), the Commonwealth Bank of Australia (CBA), the National Australia Bank (NAB) and the Westpac Banking Corporation (WBC). We use the EGARCH model and examine the relationship using monthly data covering the period 1992 to 2007. The results suggest that for all four banks: (1) there is a similar and statistically significant negative relationship between interest rates and stock returns; and (2) there is evidence of an increase in returns during the period of appreciation of the Australian dollar.

Firms as adaptive organizations : the case of Australian trading banks

The conventional accounting notion of ‘going concern’ — that a firm will continue its business operations in the same manner indefinitely — has underpinned accounting practice for over one hundred years. This idea has provided a rationale for spreading costs over accounting periods and for deferring costs as assets in balance sheets. An alternative idea that is widely regarded as reliable in the literatures of economics and deliberate action is that firms continually adapt to changes in market and economic conditions. That is economic behaviour. The implications of that view of a firm for accounting have been systematically explored by Chambers (1966). While not examining those particular implications, many other accounting theorists have been critical of the conventional accounting idea of 'going concern' and of its impact on accounting practice. The two notions of ‘going concern’ - as static or adaptive enterprises - are examined by referring to the business operations of the four major Australian trading banks over the period 1983-1991. Banks were selected because they are commonly thought to be particularly ‘conservative’ organizations. The period 1983—1991 was chosen because it covers the era of deregulation of the Australian financial system. The evidence adduced by this study indicates that the Australian trading banks have continually adapted their organizational structures and business operations in the light of changes in technology, markets for financial services, government policies and domestic and global economic conditions. Illustrations of adaptive behaviour by banks ate drawn from their normal operating procedures such as the provision of products and services, loan services, acquisitions, sale of property, non-core banking operations and international banking. It is argued on analytical grounds that the cost basis of accounting does not yield financial statements that provide factual and up-to-date information about the financial capacity of firms to pay their debts and to continue trading generally; that is, to be going concerns. At any time, those financial capacities are determined by the amount of money commanded by a firm, including the money's worth of its assets, and by its level of debt. It is concluded on empirical grounds that the Australian trading banks, at least, are adaptive entities.

A study into how Australian banks use social media

Social Media is a term commonly used to describe a group of individual web based services that have grown beyond the provisioning of the capability to connect, network or blog. The popular social networking services have evolved into a ‘platform’ by incorporating a multitude of functionalities through an array of applications to attract millions of users. This has created a favourable environment for businesses to exploit the benefit of having access to millions of social media users by using it as a business support tool. Studies indicate that social media services are being used by businesses for engaging with the general public, enhancing customer interaction, and for crisis communications. Whilst there are many businesses who have adopted social media, others have either rejected the idea or are still unsure about how to proceed. This paper analyses the functionality of selected social media services in order to explore how Australian banks use such services strategically. It reports findings from a longitudinal study of Australian bank use of four popular social media services: Facebook, MySpace, Twitter, and YouTube.

Does the global reporting initiative influence sustainability disclosures in Asia-Pacific banks?

This article examines sustainability disclosures by the major banks in the Asia-Pacific region (the six largest banks from each of four countries: Australia, Japan, China and India) during the period 2005–2012. The findings show sustainability disclosures by banks that participate in the global reporting initiative (GRI) are significantly higher than disclosures by those banks that have not participated in the GRI. Amongst those banks that have participated in the GRI there is a higher rate of disclosure by externally assured banks than by non-externally assured banks. Among the GRI participating banks, there was significant variation of disclosures between countries. Disclosures by Australian banks appeared to be significantly higher than disclosures by banks in any other countries under observation. The findings are discussed from a moral legitimacy perspective. Consistent with this view, the banks under study were responsive to the GRI, which is seen as an influential actor that shapes and reflects the expectations of the broader community. However, the role of the GRI in minimising country differences in disclosure by banks is not significant.

A hybrid approach to clustering in big data

Clustering of big data has received much attention recently. In this paper, we present a new clusiVAT algorithm and compare it with four other popular data clustering algorithms. Three of the four comparison methods are based on the well known, classical batch k-means model. Specifically, we use k-means, single pass k-means, online k-means, and clustering using representatives (CURE) for numerical comparisons. clusiVAT is based on sampling the data, imaging the reordered distance matrix to estimate the number of clusters in the data visually, clustering the samples using a relative of single linkage (SL), and then noniteratively extending the labels to the rest of the data-set using the nearest prototype rule. Previous work has established that clusiVAT produces true SL clusters in compact-separated data. We have performed experiments to show that k-means and its modified algorithms suffer from initialization issues that cause many failures. On the other hand, clusiVAT needs no initialization, and almost always finds partitions that accurately match ground truth labels in labeled data. CURE also finds SL type partitions but is much slower than the other four algorithms. In our experiments, clusiVAT proves to be the fastest and most accurate of the five algorithms; e.g., it recovers 97% of the ground truth labels in the real world KDD-99 cup data (4 292 637 samples in 41 dimensions) in 76 s.

A MapReduce-based nearest neighbor approach for big-data-driven traffic flow prediction

In big-data-driven traffic flow prediction systems, the robustness of prediction performance depends on accuracy and timeliness. This paper presents a new MapReduce-based nearest neighbor (NN) approach for traffic flow prediction using correlation analysis (TFPC) on a Hadoop platform. In particular, we develop a real-time prediction system including two key modules, i.e., offline distributed training (ODT) and online parallel prediction (OPP). Moreover, we build a parallel k-nearest neighbor optimization classifier, which incorporates correlation information among traffic flows into the classification process. Finally, we propose a novel prediction calculation method, combining the current data observed in OPP and the classification results obtained from large-scale historical data in ODT, to generate traffic flow prediction in real time. The empirical study on real-world traffic flow big data using the leave-one-out cross validation method shows that TFPC significantly outperforms four state-of-the-art prediction approaches, i.e., autoregressive integrated moving average, Naïve Bayes, multilayer perceptron neural networks, and NN regression, in terms of accuracy, which can be improved 90.07% in the best case, with an average mean absolute percent error of 5.53%. In addition, it displays excellent speedup, scaleup, and sizeup.