Could Herding Be Consequence Of Vietnamese Banks Reform

Risk management become an essential part in the operation of developed banks in the world as it contributes to reduce risk in financial market from which minimize the loss from crisis and turbulence. However, from the view of herding behavior, a tighter risk management regulation may worsen the downturn by quickly contagion in national-wide as well as global scope (Persaud (2000) (1)). In Vietnam, risk management system is in initial process; nevertheless, potential risks in Vietnamese market have proved the necessity of more prudent risk management tools such as VaR model or credit scoring model. However, the use of VaR may cause so-called risk management herding, for which solutions should be addressed.


1. The problem

1.1. Poor risk management as a source of crisis

History has witnessed a large number of financial crash and turbulence in the world banking system and many of them left very serious consequences because of their quickly widespread and contagion to worldwide scope. As could be seen in Table 1, the root of these crises is various, such as non-performing loan, illiquidity or changes in real interest rate; however, one of the most important reasons is the inefficiency and laxity of risk management system in banks and financial institutions at the time of the downturn. Mike Batty (2008) indicates that “an overarching theme of the credit crisis is a failure of risk management?. According to Batty (2008), credit crisis is the result of the inadequacy in risk management system and failure in risk management techniques. Browel (2001) also point out that financial crises or banking crises in Europe in 1990s decade implies common characteristics containing external circumstance and poor risk management practices.

1.2. Risk management herding: 1987 crash and 1998 LTCM crisis

In 1990s decade, herding was very common phenomenon in which market participants with the same risk management policy have taken the same reactions to the risk would make markets to be more volatile and vulnerable (Avinash Persaud (2000)(1)). Therefore, once crisis occurs, it could be quickly contagious from one market to another and push financial system into the deep hole. 1987 crash and 1998 LTCM crisis are two typical examples for the turbulences which were in connection with so-called risk management herding. 1987 crash has happened because of both internal and external causes in which the economists usually see the external reasons as the main causes: program trading strategy, portfolio insurance, derivatives markets and lack of liquidity (Edward (1988)). However, from another view about the reasons for crash based on information avalanches, internal causes like the herding behavior of market participants in buying and selling stock is emphasized. On Monday, 19th October, 1987, which is known as double “Black Monday?, The Dow Jones Index sharply fell 22.6 percent (508points) in a single day, the largest one day plunge in the history of stock market. In the weeks or even months before the crash, investors’ confidence has become weak because they gradually realized the downward trend of stock prices [2] and the reason for this, based on Shiller (1987)’s survey, could be the herding behavior between market participants: similar risk management policy (using the same VaR model) leading to similar reaction to the change of the economy, a large number of investors sell stocks may lead to more and more other investors selling their stocks.

Another crisis which is concerned to risk management herding is the collapse of Long-term Capital Management in 1998. LTCM crisis is related to the decline in liquidity condition which is explained by three factors: the born of EMU (European Economic and Monetary Union) in January 1999; the increase in the different ways of transaction such as ATS (electronic alternative trading system) and ECNs (electronic communication networks); and the third factor is herding behavior between investors (Avinash Persaud (2000)(2)). The reason for this, according to Persaud (2000)(2), is changes in factors that affect international capital flows, from which these factors could affect market liquidity. When international capital flows depend on institutional market participants who desire to get higher return than the others, this can boost liquidity of the market. However, herding behavior prevents investors from doing so because many investors’ decisions now are identical: they enter and leave the market at the same time. With tighter risk management system, herding has even caused more volatility when herding investors reach their DEAR (daily earning at risk) limit at the same time, they sell stocks at the same time and drive the crisis to be further. Persaud (2000)(1) analyses this issue based on a cycle of DEAR limit. At first, there is some bad news related to a particular stock, increasing volatility in this stock’s market. Banks who have a large amount of this stock in their portfolio can find that their DEARs are reached together, thus they sell their stocks at the same time to decrease DEAR level. This would increase market volatility and correlations because of decline in stocks’ prices. Market volatility, in turn, leads to more banks’ DEAR limit are reached and create contagion in financial market. In other words, crisis contagion phenomenon in this case could be explained (as Nishimura and Kiyohiko G. (2009)) that in boom, decrease in risk also pushes down capital requirement thus decreases lending. In contrast, when the economy in downturn, bank lending increases due to the rise in downside risk leading to bigger capital requirement. From this, capital adequacy requirement regulated in Basel II is criticized in causing so-call “procyclicality? problem [1] , when it combines with the herding behavior of investors. First, regulations in Basel II could increase the volatility of the market, and with a minimum capital requirement, the reserve may not satisfy the liquidity demand in bad times. Second, the fair-value accounting in which reference value would not present the value of real market may trigger the problem. Third, herding behavior of investor also amplify procyclicality effect and crisis would be contagious in a wide and unpredicted scope.

1.3. Reasons for herding

It can be seen from the issues above that herding can cause negative impacts to finance system in time of crisis because it can cause the contagion effect of the crises. In fact, Stephanie Kremer (2010) states two kind of herding: intentional and unintentional. Unintentional herding is usually driven by fundamentals such as the same information set, the same investment style or the same risk management model. Intentional herding is the imitation of market participants in buying and selling the same stocks, and this imitation is not related to private information or prior belief. Unintentional herding can be a good outcome while intentional herding is seen as an inefficient phenomenon which can cause awful effect to financial market. However, in some case, such as the case indicated by Persaud (2000), unintentional herding which is driven by using the same VaR model also triggers the bad effect of banking crash and financial crisis (Kremer (2010). Therefore, the question why market participants herd should be addressed. Avinash Persaud (2000) indicated three explanations for herding: Firstly, because we are living in the world of uncertainty, the best way to discover information of the others is imitating them. The second reason is investors are usually rewarded by relative performance, so if an investor strays so far from the others, he may not rewarded. The indicated third reason is investors are easier to be fired if they made their own decision and wrong than if they are wrong but in a group. Because of these reasons, herding becomes very common and difficult to control. The changes in risk management regulations aiming to more transparency, stronger prudential and more market sensitive risk management system may even trigger the effects of herding behavior (Persaud (2000)).

2. Aim of this paper

This paper aims to analyze risk management system in Vietnamese banks as well as herding behavior in Vietnamese banks regulatory and financial market to suggest some solutions for controlling this behavior phenomenon. The following of this paper concludes Chapter II to Chapter V, in which Chapter II indicates the literature review related to risk management, herding behavior in institutional investors and regulatory responses. In this Chapter, I discuss the use of VaR as a risk management tool in banks, its drawback in encouraging herding behavior in financial market and also, I consider the literature about the solutions for this phenomenon.

In Chapter III, I present the application of international risk management tools in Vietnam banks and potential herding behavior in Vietnamese banks’ risk management system. To do that, I summary the risk management system in Vietnamese banks as well as potential risks they could face in the near future, from which I prove the necessity of enhanced risk management tools like VaR model and credit score model. These models on the one hand could create a tighter risk management system, which, on the other hand can lead to contagion in financial crisis.

Chapter IV is my conclusions about Vietnamese risk management system, the danger of herding and some recommendations to improve liquidity as well as to avoid herding in this market.

Chapter V indicates the drawbacks of this paper.


Introduction of Value at Risk

In the late 1980s, the concept of VaR (Value at risk) was first used by major financial institutions to measure their portfolio’s risk (Linsmeier and Pearson (1996)). However, the current VaR as a market risk measure was just born since 1993 when the report of G-30 about the born of off-balance-sheet products was published. The born of these new products has addressed a need of smart risk management for them (Alexander J. Mc Neil, Rudiger Frey, Paul Embrechsts (2005)) and with the release of RiskMetrics in 1994, it has created a strong incentive in using VaR as a popular risk management technique in banks and financial firms since that time (Linsmeier and Pearson (1996)). As the definition from, “VaR is the largest loss likely to be suffered on a portfolio position over a holding period (usually 1 to 10 days) with a given probability (confidence level)?. Therefore, VaR could be considered as the maximum potential lost of a portfolio in specific time period with a given confidence interval. For example if the 95% value at risk of a portfolio is GBP1milllion in weekly period, the probability for total loss of this portfolio exceeds GBP1million for the next week is just only 5% (Natasa Kozu (2010)). From the calculation of VaR, banks and financial firms could manage their capital requirement by retaining enough money provision of market downward trend so as to avoid market risk. Because VaR is easy to understand and apply and it is also a single measurement for risk which is accepted widely (Natasa Kozu (2010)), a VaR model was allowed by Basel Committee in 1995 for banks to measure their capital requirement in case of market risk, based on the parameters from the committee (Linsmeier and Pearson (1996)). This is indicated in Basel Accord I which requires banks to calculate regulatory capital (minimum capital charge) and remain the minimum capital in line with potential loss of banks. However, the publication of Basel Accord II framework has required more market sensitive risk management, more prudential principles to guarantee that banks have enough capital to support their risk and stronger market discipline by increasing transparency of banks’ publications (Neil, Frey & Embrechsts (2005)). Although Basel II and VaR have their own merits, the use of VaR also has some drawback such as it cannot measure risk in extreme situations and may bring the wrong results with the less frequently traded stocks (Natasa Kozu (2010)). In addition, the release of Basel Accord II with three pillars above may increase the effect of risk management herding in crisis period which is considered as “procyclical effects of financial regulation? (Alexander J. Mc Neil, Rudiger Frey, Paul Embrechsts (2005)) in which minimum capital requirement increasing in recession and decreasing in boom may cause the liquidity of market decline.

Literature for risk management herding solutions

Avinash Persaud (2000) has suggested the solutions for herding behavior in financial market which, as he indicated, could create systemic risks. The problem that policy makers face is the tighter risk management regulation focusing on market-sensitivity, prudence and transparency can increase the effect of herding behavior, therefore it should be reconsidered in a new way. The requirement of setting an extra capital aside for systemic risk of regulators in particular market where herding exists just only make the problem become more vicious when there is negative information (why). The solutions here are encouraging banks and other financial institutions to allocate collateral in case of systemic risk, or to buy liquidity options from central bank but in good times, not in awful times. In addition, because there are investment inflows that herds less than the others, regulators should facilitate those inflows, including foreign direct investment, equity portfolio and hedge funds rather than bond flows and…..Transparency in the long run is a good action; however, governments should not expose the information with too high frequency. In particular, information should be published or released monthly of weekly, but not daily. Small markets even find it even better not to release their reserves weekly.

Peter Haiss (2009) considered the solutions for risk management herding through both sides: regulatory side and banking side. In regulatory side, he suggests to change from actor-based approach to product-based and to consider macroprudential factors in making regulation. ?????In banking side, Haiss (2009) suggests banks to set up conflict mechanism in the management systems (devils advocate-approaches or dialectical inquiry for example) and to control the reward systems to resist herding.


Overview of risk management in Vietnamese banks and financial system

In Vietnam, risk management systems is in improvement process toward an international standard risk management system (Loi, Hoang Tien (2004)); however, currently, Vietnam is just still apply the Basel I which has revealed many shortcomings in the past decade (Ha, Tran Manh (2009)). The requirement of minimum capital has just concentrated on credit risk, while market risk is also a important source of financial turbulence and downturns. In Vietnam, risk management system is mainly based on Decision no. 457/2005/QD/NHNN of State Bank of Vietnam (with Basel I fundamental) in which regulates capital adequacy ratio (CAR), the ratios of payment ability, credit limitations which are mainly credit-risk related regulations. The market risk is still not focused while financial crisis is happening in global scope. The collapse of Northern Rock in 2007 due to the change in interest rate when LIBOR (London Interbank Offered rate) reached its highest level at 7% in July 2007 and the loss of American commercial banks in 1990s prove the essential of management of market risk. In 2008, when interest rate and exchange rate in Vietnam proved its strong volatility [1] , few commercial banks have a forecast systems which could predict the potential loss that may occur from which brings suitable solutions in case of crisis (Ha, Tran Manh (2009)).

Nevertheless, risk management in banks and financial institutions in Vietnam is moving toward the Basel II with new standards aiming to assure healthy Vietnamese financial system (Loi, Hoang Tien (2004)). The four state-owned banks of Vietnam (Bank for foreign trade - Vietcombank, Bank for Investment and Development - BIDV, Bank for Industry and Trade – Vietinbank, and Agriculture and Rural Development Bank - Agribank) have been advised by the State Bank of Vietnam to build credit manuals which contain the international standard in risk management [1] . Along with credit manuals, the four state-owned commercial banks also carry out other activities such as asset-liability management or insider audit which contributes to recognize potential risks in banks operation although there is still a big gap between the standards and the implementation (eStandardsforum (2008)). According to Technical Assistance Report of ADB (Asian Development Bank) 2008, ADB will assist Vietnam in improving their account and auditing system as well as support this country in drawing a draft of new law related to independent audit and strengthening standards for SMEs (Small and Medium-sized Enterprises).

Credit information becomes also more transparent through the establishment of Credit Information Centre (CIC) which supplies information of the borrowers to banks and financial institutions. This may decrease the risk of the lending in the future: once banks and creditors have enough information about borrowers, effect of asymmetric information will reduce and therefore put down the risk that creditors may face in the future. Before 2004, the CIC provided entry to information for credit firms only but from 2004 onward, the CIC already facilitated the access to information for all parties of all economic sectors and received the fee. This has improved the transparency in financial market as indicated in pillar 3 of Basel Accord II. However, improvement of transparency in Vietnam financial systems has been facing with many difficulties, especially in case of non-performing loans (NPLs) and non-performing assets (NPA) of state-owned firms which are still considered as state secrets. NPLs in Vietnam has been estimated by Fitch Ratings [2] that it accounts for about 13% of Vietnam banks’ total loan at late 2008, however, this is still an underestimated percentage of NPLs (Champleboux, Kurdi, Bultez & Gain (2010)). Cleaning NPLs and NPA is currently the target of AMC (Asset Management Company) which belongs to commercial banks and DATC (the national Debts and Assets Trading Company) which belongs to Vietnam Ministry of Finance. In Vietnam there currently are ten commercial banks who have established their AMCs which concentrate on mortgage asset transferred from the banks to recover NPLs of these banks. The DATC concentrates on State-owned Enterprises (SOEs) in which it focuses on resolving the long out-standing NPLs and NPA of these SOEs. Although AMCs and DATC have been established in order to resolve NPLs and NPA which have been overdue for a long time, the targets of these companies still contain conflict: on the one side, they aims to clear NPLs and NPA but on the other side, they also need to gain profit by dealing with transaction related to NPLs (Loi, Hoang Tien (2004)). In Vietnam Best Practice Report 2008 (Estandardforum (2008)), it is indicated that there is not enough information available to conclude that Vietnam banking and financial system is compliance with Code of Good Practices of IMF on Transparency in Monetary Policy. (Table) Vietnam has already joined the General Data Dissemination System of IMF from 2003 but its entry to Special Data Dissemination System of this organization, which has stricter regulations, is not yet approved because this country has not yet met the entry requirements. The main reason is data published in Vietnam lacks reliability, opportuneness and coverage, especially in case of national accounts. Moreover, difficulties in cooperation between governmental offices who has responsibility of data dissemination also leads to the failure of Vietnam in the road to IMF’s Special Data Dissemination System (Estandardforum (2008). About fiscal policy transparency, a report published in 1999 of IMF and World Bank has indicated that Vietnam does not meet the requirement of the Code of Good Practices of IMF on transparency in Fiscal Policy; however, the next reports of IMF also indicated that Vietnam has a good improvement in transparency in fiscal policy presenting in State Budge Law (2002) and altered budget categorization method. National budget was being published annually and national accounting standards were being revised and integrated financial control system was introduced aiming to enhance fiscal report and treasury management. Nevertheless, Vietnam OBI (Open Budget Index) 2006 related to the openness and transparency in budget just reached 2%. The reason for it is among seven documents related to budget which must be public, Vietnam just satisfied only one and even did not allow the public access to the detailed content of that document (Estandardforum (2008)).

Besides, regulation about risk management practices in Vietnam also gives more power to banks and financial institutions (Loi, Hoang Tien (2004)). In particular, Vietnam state-owned commercial banks are allowed to reject the projects that cannot be commercially vital, even in the case of big projects of state-owned huge companies. In Vietnam, there was situation in which state-owned commercial banks understand that a project does not have potential commercial benefit but they still must approve that project because it is from big state-owned company. This is called as lending based on relationship between banks and clients (Dinh and Kleimeier (2006)) in which approval for lending is not only depends on quantitative characteristics of borrowers like income or collateral but also based on qualitative factors such as fame or borrowers’ position in the community. Therefore, the big state-owned companies usually have advantage in applying for a loan from state-owned commercial banks even if their projects do not have bright prospect about success ability which contributes to raise the risk in banking lending operation. Giving more power to SOCBs (State-owned commercial banks) is one in many steps of reform in banking system and would also help to reduce risk in financial and banking operation.

Potential risk and the need of risk management in Vietnam financial system

Vietnam financial system contains many potential risks

Downgrade of Vietnam Issuer Default Ratings

Although Vietnam banking system is in improvement process with its own merits, the country still has to face many difficulties brought by a weak banking and finance industry, which is rated as ‘BB+’/Negative B by the Standard&Poor’s and as ‘CCC’ by the Economist Intelligence Unit (Champleboux, Kurdi, Bultez & Gain (2010)) . Fitch Ratings have also just downgraded Vietnam IDRs (Issuer Default Ratings) of long-term loans in both domestic and foreign currency from ‘BB-’ to ‘B+’, in which short-term IDRs in foreign currency is remained at “B’ because of Vietnam weak banking and financial system [4] .

Liquidity risk and inflation risk

The potential risks in Vietnam current financial market contain two main risks that are liquidity risk and inflation risk. One of the main reasons of the global subprime crisis which started in the US from 2007 is liquidity risk, in which short-term deposits were used to finance long-term loans. In Vietnam, the economy has suffered from the crisis, however, the effects were not too serious as developed countries who received direct consequences of the downturn because Vietnam banking system’s scale is small, banks in Vietnam are mainly commercial banks and Vietnam has not yet deeply joined the world playground. Nevertheless, the global turbulence has been warning Vietnam about the danger of liquidity risk in Vietnam financial system (Hookway & Barta (2009)). Inherent liquidity risk in Vietnam is caused by bad-debt (NPLs) which is mainly from SOEs (State-owned Enterprises) and therefore, it is difficult to be resolved. Another reason is the dollarization of Vietnam economy which disappears one of the most important functions of central bank: “lender of the last resort? (Hauskrecht & Nguyen, T.Hai (2004)). Dollarization is the situation in which a country use foreign currency (US dollar) to carry out the transaction in this country due to the stability of foreign currency compared to domestic currency [1] . Vietnam has a highly dollarized economy and it is not beneficial dollarization (Hauskrecht & Nguyen, T.Hai (2004)): in case liquidity demand become emergent, central bank could not have enough money to inject to the economy because of the inelasticity of money supply in foreign currency. Bank panics followed by financial crisis are easy to happen especially when Vietnam economy is wider opened as its promise for the entry to WTO.

Beside liquidity risk, inflation risk is also a danger to Vietnam financial system. In current time, Vietnam has plenty liquidity which pushes the Government of this country pumping a great deal of credit to the economy to reduce lending interest rate from which supports banks’ lending. This may worsen the problem of NPLs and NPA which have existed in Vietnam banks system for a long time, raise the current account deficit of this country and increase inflation back to the level of over 20% of 2008 (Champleboux, Kurdi, Bultez & Gain (2010)) [6] . Therefore, Vietnam financial system is still facing the danger of future inflation.

Applications of VaR in Vietnam bansks (for market risk)

Vietnam financial market is in reform progress to the light of Basel II; therefore, using VaR as a measure of risk in risk management system of this country (pillar 2 of Basel II) would be inevitable in the near future. Currently, there are three main methods to measure VaR: Delta-Gama, Monte Carlo and historical simulation in which Monte Carlo method or stochastic simulation method is used most popularly because of it is more exact and more flexible than the others (Natasa Kozu (2010)). Delta-Gama method is quite similar due to its assumptions that risk of the portfolio is linear and risk factors are normally distributed and thus these are also disadvantages of Delta-Gama method. Historical simulation method uses historical data to measure VaR with weights in present and Monte-Carlo method is more sophisticated with assumption that each risk factor has different distribution and parameter. (Natasa Kozu (2010)).

To apply VaR into risk management system of Vietnam banks and financial institutions, Ha Tran, Manh (2009) recommends historical simulation method because it seems the most suitable method for Vietnam banking system. According to Tran, Delta-Gama method is simple but lack of accuracy, while Monte Carlo is too complicated with a small economy like Vietnam: it is difficult to build different simulation program for each Vietnamese bank due to limit in financial ability as well as staff’s skill to apply the model. Historical simulation is considered as the most suitable one because it brings quite exact result and is easy to construct; in addition, Vietnam commercial banks’ historical data is initially established and market factors’ data like interest rate, exchange rate are also easy to capture in the past ten years. Applying historical simulation method to measure VaR in Vietnam banks follows seven steps: The first step is determining the risk factors (as market variables) which can affect banks’ income. Second step is gathering historical data in an enough long period. In the third step, the simulation of changes in market variables in the future will be carried out based on assumption that they have the similar tendency as in the past, from which identifying the potential lost correspondence to each episode of market variables as the fourth step. In the fifth step, VaR could be calculated with a given confidence interval. The two final steps are using Stress-test and Back-test to ensure the accuracy of VaR and banks’ ability to stand changes in risk factors that are far different from changes in the past.

To calculate VaR of an investment portfolio, assume that present value of the portfolio is Pt:

Pt=P (f1,t , f2,t ,….,fn,t)


f1,t , f2,t ,….,fn,t are risk factors at time t

The changes in risk factors in the past could be expressed as:

∆f­ik = (∆f1,t , ∆f2,t ,…, ∆fn,t ) (k=1,2,…,s where s is the number of episodes of each factor)

From which calculate simulated value of each factor, starting with factor 1:

f­ik = f1,t + ∆f1,t

And simulated value of the portfolio will be:

Pk= P (f­1k , f­2k ,…, f­nk)

The changes in portfolio value will be:

Rk=( Pk – Pt )/ Pt

From that VaR could be calculated as:

VaR=Ave(Rp) – Rp(c)

Where Ave (Rp) is average return of the portfolio

Rp(c) is return correspondence to the cth differentiation

Thus we could use changes in stocks’ market price to calculate VaR without deriving parameters as well as identifying functional distribution.

To illustrate this method, assume that a bank has a portfolio including 1,000 stocks ACB of Asia Commercial joint-stock Bank with price VND28,350,000 in 09/09/2010 and 1,000 stocks AGC of An Giang coffee joint-stock company with price of VND24,200,000 in 09/09/2010. Historical data about price of this stock is collected in Table 2, and that is data from 09/09/2009 to 09/09/2010. From Table 2, VaR was calculated equaling to -2039.2 [1] that means the maximum loss of portfolio in 10/09/2010 will not excess VND2,039,200 with confidence level of 95%. By calculating VaR, bank could prepare enough money to survive if the worst situation occurs.

However, in fact there are changes in risk factors that are not similar to changes in the past, thus Stress test becomes a necessary tool in banks’ risk management (Ha, Tran Manh (2009)). One of the risk factors that affects bank operation is interest rate: changes in interest rate will immediately have effects to banks’ assets. Therefore, banks could use historical simulation method to estimate the maximum loss in the future with a given confidence level based on changes in historical data of interest rate, after that expanding changes more than the fluctuation in the past thus banks would prepare more careful for potential loss in the future. Similarly, Vietnam banks could use historical simulation method to calculate VaR due to the changes in inflation rate as well as exchange rate and use Stress test to establish solutions to cope with these market risks.

Applying VaR in Vietnam banking system requires Back-test from central bank because sometimes VaR model could bring wrong result compared to reality. For example, if a bank applies VaR model with confidence level of 95% in 252 working days for the past year, the bank would calculate VaR of its portfolio. However, if portfolio loss excesses VaR in more than 13 days (5% of 252), VaR model of that bank would not be accurate and it should be modified to more accurate model.

Applications of credit score model in Vietnam banks (for credit risk)

As indicated above, liquidity risk due to non-performing loans is a big problem in Vietnam banking system. To solve this, some commercial banks in Vietnam have used credit scoring model which is an important section in their credit manuals (Dinh and Kleimeier (2006)). One of the credit score model being used in Vietnam banks is described in Table 3, in which 14 variables are divided into two panels A and B corresponding to first round and second round in evaluating loans. Borrower is first evaluated based on nine variables (Panel A) and if his or her score is over a certain level, loan applicant will be continuous assessed by other five variables (Panel B). After this second round of assessment, borrower will be ranked as different categories in Panel C and from which, banks could make its loan decisions.

As can be seen from Table 3, loan applicants are assessed by experience of credit staff while score assigned to each borrower is not calculated statistically. Therefore, the bank could not find the probability of default of borrowers that helps banks in enhancing their risk management system. This requires a more effective credit score model (in which probability of default should be considered) for Vietnam banks to improve their bad-debt problem. Dinh and Kleimeier (2006) suggested a model with 22 different variables including nine quantitative and thirteen qualitative criteria. Each variable is divided into small groups, then it is coded based on the formula:

Ln(gi/bi) + ln(B/G)

Where: gi and bi is number of good loans and bad loans of ith group respectively

G and B is number of good loans and bad loans in total

From this probability of default corresponding to each group could be estimated and banks would have reference to make loans decisions.

Table 4 presents 22 variables being used in the model, in which data was collected from one of Vietnam commercial banks (Dinh and Kleimeier (2006)) to calculate probability of default. As can be seen in Table 4, each group in each variable is assigned to a probability of default from which the bank would find it much easier to make their own loans decisions. For example, the higher income, the lower probability of default, so loans condition could be easier for applicants with higher income; or college graduate applicants have the highest probability of default thus should be limited in making loans.

Bank can depend on probability of default of each loan applicants’ group to decide whether loans should be made or not; however in this model ln(B/G) is constant but it might be changed in reality of banking practices (Dinh and Kleimeier (2006)). Therefore, it should be combined with relationship lending, that is, banks could apply this model for loans that are already approved by credit staff before because in some cases, credit officer may realize qualitative risk based on his relationship with borrowers (Schereiner (2003)).

Risk management herding in Vietnam

Herding in risk management policy

In the past, risk management in Vietnam was still considered as an expense and banks must pay for it because of law and regulations. Very few banks, especially state-owned commercial banks, understand the strategic importance of risk management as well as the danger of financial turbulence arising from poor risk management. The reason for it is the strong protection policy of this country in which State Bank of Vietnam intervene deeply into state-owned commercial banks’ operation, thus they rarely have to face bankruptcy; therefore, state-owned commercial banks usually care about profit rather than risk management. Currently, although banking system in Vietnam is in reform process in which risk management has been focused to satisfy WTO’s requirement, it is still very initial and in original process in which commercial banks have still not conceived enough about risk management techniques as well as their applications [2] .

Herding in Vietnamese stock market

V. Kallinterakis (2007) has found the evidence of herding in Vietnamese financial market and the relationship between herding and thin traded market in which people do not trade stock continuously thus stock price is unchanged for several days. By using the model of Hwang and Salmon (2003), Kallinterakis (2007) collected the data of VN-index and closing prices as well as market capitalization of the stocks listed in Ho Chi Minh Stock Exchange from 01/03/2002 to 28/02/2007. The result shows that thin trading could be the intensiveness for herding behavior in Vietnamese stock market because after the days in which orders implementation were postponed, the next active trading days would witness a huge demand or supply accumulated in the postponed days, from which possibility of herding in buying or selling stocks could be increased. By using model of Christie and Hwang (1995), Faber, Nam and Hoang (2006) also find out the exist of herding in Vietnamese stock market in 2000-2006 period, toward stocks that have extreme positive returns in the market.

The results from research above due to bubble in stock market in the period 2006-2007 in which Vietnamese stock market grew at second highest speed in Asia, just followed China (Pham Ha (2010)). The growth of Vietnamese stock market in which stock price soared is the main reason for herding behavior between stock investors. Nevertheless, stock market bubble has burst in 2008 leading to the plunge in Vietnamese stocks’ price after VN index reached its peak at March 2007 ( this can be seen in Figure 1). This due to global financial crisis which started from US subprime crisis 2007-2008 pushing Down Jones and S&P500 index fell at very high speed.

At present time stocks have been thought that being traded with real price, however, with the lack of information and transparency, the only way for many individual investors is herding, especially herding to foreign investors. Although Vietnamese financial system is in reform progress which contains enhancing transparency, this still has faced many difficulties; therefore, herding could still happen in the future.


Some conclusions

From above analysis about risk management system and potential risks in Vietnamese financial market, I would like to conclude the main points as follow:

Firstly, risk management system in Vietnam banks is still in initial stage with many drawbacks, especially bad-debt and liquidity problems, transparency or the lack of international auditing. Government still intervenes too deep into banks’ operation to protect state-owned commercial banks and this creates dependent psychology in state-owned banks system. Regulations about risk management system in Vietnam banks are still based on Basel I, however, this country is in reform process toward the light of Basel II, in which VaR model is core tool in controlling market risk. Vietnam are facing many potential risks in which liquidity risk (credit risk) and inflation risk (market risk) are big dangers to the financial system thus finding solutions for these risks would become essential once this country steps completely to the world playground. Therefore, using VaR model to estimate price of market risk would be inevitable although herding could be its possible outcome.

Secondly, herding has happened in Vietnam stock market as a common phenomenon in 2000-2007 period, and whenever the lack of transparency and the inaccuracy of information in Vietnam financial market exist, herding still to happen and can could other stock bubbles which has burst in 2008. After bubble burst in stock market, investors are more careful in making investment decisions; however, the transparency has not been improved enough and the evidence for which is that Vietnam’s Issuers Default Rating has been downgraded by Fitch to “B+? due to high NPLs and NPA which are considered as state secret. In addition, Vietnam stock market is still a start-up market, very few investors have ability to analyze market and make decisions strategically; therefore, herding would not disappear from the market just after one bubble burst.

Thirdly, according to V. Kallinterakis (2007), thin trading in Vietnamese stock market could encourage herding phenomenon; thus correction for thin trading can reduce herding in Vietnames stock market.

Fourthly, Vietnamese financial market is in reform toward WTO entry requirement; thus in the future, transparency must be enhanced, VaR could be used to measure market risk from which avoiding loss if crisis occurs. However, as Persaud (2000) research, the application of VaR and more transparency in banks’ operation could worsen the problem in case of crisis. If banks use the same VaR model, it could create contagion phenomenon; and Vietnamese banks are in a very early stage in using such a VaR model, with limitation in skill of staff to apply VaR, thus banks using similar models is a reasonable possibility. Moreover, Vietnam Central Banks, as requirement of WTO, would must not intervene too deeply into banks’ operation as well as financial market; therefore, in the future, this country would have to face crisis more frequently with more serious consequences: Vietnamese financial market would not separate from global financial system, and if herding happens in time of crisis, the downturn could contagious widely and worsen the problem.

Fifthly, beside market risks, Vietnamese banks also have to cope with credit risks and credit score model is using in some commercial banks of Vietnam although it just stops at a qualitative model in which loans decisions are made mainly based on experience of credit staff. The suggestion of Dinh and Kleimeier (2006) about the model in which banks could estimate probability of default of each category of loan applicants might be a good suggestion for Vietnamese banks in controlling credit risk. This could help banks reduce probability of default of borrowers and increase liquidity, especially in crisis situation.


From the conclusion above, I would like to suggest some recommendations for improving risk management system in Vietnamese banks as well as controlling herding behavior in Vietnamese financial market and banking system:

Firstly, applying solution of Persaud (2000) that is using VaR model as a risk measure and enhancing transparency because they are good for banks in the long-term. In Vietnam the application of historical simulation VaR model could help banks avoid their DEAR limit being hit at the same time because each bank has a different portfolio and even in case they unintentionally invest in the same portfolio, banks have prepared for the loss thus avoiding probability of default. Transparency should be enhanced, especially in stock market, but it should stop at publishing information weekly only.

Secondly, applying credit score model of Dinh and Kleimeier (2006) to decrease credit risk and increase liquidity.

Thirdly, thin trading in Vietnamese stock market should be corrected, i.e orders should be implemented continuously in everyday to avoid excess trading in the next “active? day. It could decrease herding in stock market.


Drawback of this paper is I could not find the evidence for herding of institutional investors but find the evidence for herding in all market containing both institutional and individual investors thus just could bring solutions for all market. This due to limitation in collecting data in Vietnamese financial market. In addition, risk management in Vietnam is still initial, therefore, risk management herding and its consequences to this country are not as mature as in developed countries and thinking of solutions for this problem seems too early to the country in which banking system is still in strong protection of Government. However, Vietnam is in reform progress and sooner or later, the country would have to face this problem when it really becomes a player in global playground.