Market Dynamics and Herding Behaviour

Bank Lending And Herding Behaviour

In recent years, there has been a growing interest in the literature relating to herding behaviour. Through these studies, researchers have explored different aspects of such behaviour and the motivations for the same. However, these studies have focused on herding behaviour in the capital markets, among managers of mutual funds, hedge fund managers and stock analysts. In this part, the researcher has evaluated different studies and investigations on lending and herding behaviour among different banking organisations. Herein, the researcher has presented arguments and discussions relating to lending decisions made by the banks and the reasons for providing similar types of loans and banking services.

Herding can be defined as those situations where banks make similar risk-taking and asset holding decisions. Herding behaviour occurs when banks, that have access to similar information or are under comparable conditions, rationally make similar decisions. There are several reasons that advocate studying the herding behaviour in the banking industry. One of them is the fact that due to the nature of the industry, banks are more likely to herd. Reducing the number of financial organisations, increasing reliance on short-term market instruments lead to banks mimicking each other’s behaviour and decisions. In the following paragraphs, the different aspects of bank lending and herding behaviour have been critically evaluated.

Herding Behaviour

Tran, Nguyen and Lin (2017) investigated the impact of herding behaviour on the quality of credit on bank loans given by banks in Australia. The researchers collected loan data for this study from the Monthly Banking Statistics publication of the Australian Prudential Regulation Authority (APRA). Publications from March 2002 – December 2011 were evaluated in this study. The final sample consisted of 59 domestic, subsidiaries of foreign banks and the foreign bank branches. They found that the herding behaviour of banks vary with the type of loan they give to their customers. They further determined that during the global financial crisis of 2007-08, herding was extremely popular among housing loans given to owners who lived in the homes, due to a phenomenon known as the flight to the quality phenomenon. The herding behaviour is extremely popular in the Big Four Australian banks than the smaller and regionally operating banks. The researcher further stated that the herding behaviour displayed by the banks is countercyclical, as they are inversely related to the GDP growth rate of the country as well as the cost of funding. However, it is directly related to the market risks.

The study by Liu (2014) provides three basic and simple reasons for the hearing. One of them is the information cascade theory; the regulatory arbitrage hypothesis and the reputation or compensation hypothesis. The researcher used LSV and FHW herding measures to commercial banks in the US that operated during the period 1976-2010. One of the key findings of this research was that herding behaviour is very common among banks that provide the same kind of loans at the same time. The researcher further determined that one of the main motivations for herding is the declining bank performance. This finding is reliable in the context of the information asymmetry and the regulatory arbitrage hypothesis.

Chiang and et al., (2013) conducted an examination of herding behaviour displayed by investors in the Pacific Basin equity markets. The results show that herding behaviour largely depends on time. It is present in both rising and falling markets. The authors further found that there is a positive relationship between herding behaviour and stock market performance. However, it is inversely related to volatility in the market. The researchers also suggested that while testing and measuring the herding, its dynamic market must be considered.

Kamada and Miura (2014) investigated the behaviour of long-term rates in Bank of Japan. Here the researchers focused on change in confidence of the traders and the herding behaviour. Kamada and Miura developed a theoretical model and used it in stochastic simulations to present the volatility of bond prices and trading volumes. They suggested that the effective way to understand developments in the long-term interest rates is by analysing the interpretations made by the traders in the market. Dig deeper into Evolution of Financial Technology Impact with our selection of articles.

Theories of bank lending

Over the years there have been many theories and concepts that have thoroughly evaluated the aspect of bank lending. Various researchers have developed theories regarding the subject matter. Following is a critical analysis and evaluation of some of these theories:

The Information Asymmetry Theory

Study of Niinimaki (2015) focused on the asymmetric information on banks, relationship lending and switching costs. According to the authors, the classical theory of relationship banking states that the asymmetric information on borrowers results in a lockout of information by the borrowers. In the study, the authors further stated that a borrower gets tied to a good bank because he does not want to take a risk by accepting the offer of the loan with low-interest rates from other bank which may have poor performance. This means that asymmetry in information forces the customers, i.e. the borrower with only limited options. Due to this reason, they become tied to a particular banking organisation. In such a scenario, the old borrowers are of great importance for the banks because they provide high yields whereas new borrowers may be unprofitable for the organisation. This is even truer because the baking organisations fiercely compete with one another. Therefore they are not in a high risk taking a position. Lin, Prabhala and Viswanathan (2013) further stated that the banks have only short-term relationships with the customers. Therefore it is very difficult for them to obtain detailed information about their clients. This causes an asymmetry in information and affects operations and lending decisions of banks significantly. Even a single mistake can have a detrimental impact on the whole process and may have an adverse impact on the bank itself. The model developed by Demiroglu and James (2010) in their study is based on the assumption that identical borrowers have asymmetric information on the bank’s type. This happens primarily due to reason that operations of banking organisations are more transparent than that of a small business organisation. However, the study by Acharya and Naqvi (2012) showed that banks possess more information about their clients. This is their job to do. They have to ensure that the person they are giving the loan to is capable of replaying them with interest. This is the most effective way for banks to generate revenue and make profits.

Niinimaki (2015) contradicted the findings of Lin, Prabhala and Viswanathan (2013) by stating that in reality borrowers can communicate with one another and share the information they possess on the banks. This is not an ideal situation because it increases the chances of information asymmetry, because in some cases the borrowers may be able to know more about the bank than the bank itself. This can have an adverse impact on the operations of the banking organisation and would get limited in its operational strategies and lending decisions as well.

The Regulatory Arbitrage Theory

Another very popular theory regarding bank lending is that of the regulatory arbitrage theory. Essentially it is an avoidance strategy with the focus on avoiding those regulations that have been exercised or implemented as a result of regulatory inconsistency. Many authors and experts believe that it is a very important theory of bank lending. Herein the authorities should focus on finding the loopholes in the policies regarding the lending behaviour of the banks. Access to such information can help them to take effective decisions that will eventually improve operations and performance of the banks. In this sense, the study conducted by Beltratti and Stulz (2012) rightly states that the arbitrage theory can be of great use for banking organisations and can be used as a tool for long-term sustainability in the industry. Willesson (2017) studied and reviewed 91 different research articles and found that everyone knows about the theory but only implicitly. The understanding of regulatory arbitrage and its motives are still known only on a scattered basis. This means only a very small portion of the people actually know about this theory and its various aspects. However, Borio and Zhu (2012) contradict these findings by stating that many theories that evaluate the opportunity costs related to the use of regulatory arbitrage theory are well known. This, thus, improves the overall quality and effectiveness of the theory by a great margin. Boyer and Kempf (2016) define regulatory arbitrage as a strategic choice which can be characterised as the non-action of an event that increases the possibility of further empirical studies on the said topic.

Borio and Zhu (2012) in their study developed a multi-country economic model of banks. The authors focused on analysing the issues of the impact of international mobility of banks and capital flows on the regulatory framework area of banks’ operations. The researchers further used the agency theory to determine the process of designing a regulatory contract. They even stated that in context of this topic there is information asymmetry, meaning that the public authorities have a disadvantage in terms of information in the process of evaluating the ability of a banking organisation to manage their portfolios efficiently and effectively.

REFERENCES

Acharya, V. and Naqvi, H., 2012. The seeds of a crisis: A theory of bank liquidity and risk taking over the business cycle. Journal of Financial Economics, 106(2), pp.349-366.

Beltratti, A. and Stulz, R.M., 2012. The credit crisis around the globe: Why did some banks perform better?. Journal of Financial Economics, 105(1), pp.1-17.

Borio, C. and Zhu, H., 2012. Capital regulation, risk-taking and monetary policy: a missing link in the transmission mechanism?. Journal of Financial Stability, 8(4), pp.236-251.

Chiang, C. T. and et. al., 2013. Dynamic Herding in Pacific-Basin Markets: Evidence and Implications. Multinational Finance Journal. 17(3/4).pp. 165-200.

Demiroglu, C. and James, C.M., 2010. The information content of bank loan covenants. The Review of Financial Studies, 23(10), pp.3700-3737.

Lin, M., Prabhala, N.R. and Viswanathan, S., 2013. Judging borrowers by the company they keep: Friendship networks and information asymmetry in online peer-to-peer lending. Management Science, 59(1), pp.17-35.

Niinimaki, P. J., 2015. Asymmetric Information, Bank Lending and Implicit Contracts: Differences between Banks. Czech Economic Review. 9(2).pp. 74-90.

Tran, T. V., Nguyen, H. and Lin, T. C., 2017. Herding behaviour in the Australian loan market and its impact on bank loan equity. Accounting and Finance. 57(19).pp. 1149-1176.

Willesson, M., 2017. What is and What is not Regulatory Arbitrage? A Review and Synthesis. Financial Markets, SME Financing and Emerging Economies.