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The Challenge of Behavioral Finance on Credit Risk Management Pan Jun

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The Challenge of Behavioral Finance on Credit Risk Management Pan Jun
The Challenge of Behavioral Finance on Credit Risk Management
He Linjie
Ma Chaoqun
(Business School, Hunan University, Changsha, Hunan 410082)
Pan Jun
(Jangsu University, Foreign Linguistics School, jiangsu, zhenjiang, 212013)
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Abstract The study of the developments in the credit risk management theory shows that financial
institutions are thinking more and more about the investor's attitude towards risk In the meantime behavioral
finance completely from the perspective of investor's attitude towards risk, shows that investor's risk
preference may transfer and that capital structure has great impact 0n corporation value all of which
issues a severe challenge to the conventional credit risk management based on efficient market
hypothesis
Key words behavioral finance credit risk risk preference
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Recently, along with the trend of financial globalization and the vehemence of the fluctuation of the
financial market, risk management has always been the concern of financial circle home and abroad.
Among the many financial risks threatening financial markets, including credit risk, interest rate and
foreign rate risk, liquidity risk and corporate risk, etc., credit risk has always been one of the most
important. According the research on global bank crisis by the World Bank, credit risk is the most
common course for bank failures, which, in turn, caused the increasingly concern over credit risk within
the international financial circle. With constant circulation and innovation, credit risk management
technology is developed and perfected day by day; therefore, a lot of quantification technology, support
tools and software are commercialized.
The above theory and technology are based on efficient market hypothesis, which has caused more
and more doubts along with the increasing concern over Anomaly at financial markets. Under these
circumstances, it sees the coming of behavioral finance theory. Along with the transformation of
traditional finance theory into behavioral finance theory, or rather, with the co-existence of both, there
comes the great challenge on both the theory and technology of credit risk management, which is based
on efficient market hypothesis. Through studying credit risk, the development of it and behavioral
finance theory, we will demonstrate the challenge facing credit risk and the development direction of it
here.
1 The development of credit risk management theory
During the initial credit risk management, most financial institutions applied subjective analysis, or
expert systems to evaluate the credit risk on corporate loans, with the major disadvantages of
over-subjectivity, lack of quantitative results and weak comparability, gradually taken place by some
other quantitative models.
One of the quantitative models applied widely now is statistical model, which is raised up by
Fisher upon his heuristic research in 1936, with common models such as the linear probability model,
the Logit model, the Probit model and the discriminate analysis model, among which, the discriminate
model and the Logit model the most popular. However, many demonstration results indicate that the
relationship between corporate financial situations and the accounting ratios are usually non-linear, that
explanatory variables, i.e., accounting ratios, are highly correlated, and that many indexes are not in
normal distribution, all of which, will affect the prediction effect of the statistical methods. Therefore, a
new method of artificial intelligence, based on informational science and computer technology, is
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introduced into the field of credit risk management.
Since the 1980s, along with the rapid development of informational science and computer
technology, artificial intelligence technology has been introduced into commercial bank and credit risk
management, having overcome statistical methods’ comparatively strict requirement on hypothesis and
the credit risk of stationary reflection, with neural network analysis as its representative. However, the
greatest disadvantage of such analysis is its high randomicity on operation and the difficulty in the
economic definition of each variable caused by its theoretical basis. Altman et al (1995) concluded that
neural network analysis is virtually not superior to
, in his comparative
research on both.
Most of the methods mentioned above are mainly focused on credit risk of individual loans or
investment items, however, along with the globalization trend of financial markets and the appearance of
various financial instruments, financial institutions and investors have gradually realized that you cannot
put all your eggs in one basket, and therefore, they have applied the method of portfolio to disperse and
dispel such risks. In the meantime, the study on finance theory is transformed to the direction of study
on human psychology. Under this circumstance, the original statistical method of credit risk
management is gaining a lot of criticism due to its ignorance of the investor’s attitude, while the new
model of credit risk management, Value at Risk model, which is in accordance with investor’s attitude,
has appeared.
In 1993, KMV raised the PortfolioManager method, while in 1997, J P Morgon published its
CreditMetrics method, both with the theoretical foundation of Option Pricing Theory by
Black-Scholes— Merton. Meanwhile, in 1997, CSFP (Credit Suisse Financial Products) published its
CreditRisk+ method, while in 1998 McKinsey raised CreditPortfolio View method. One common
feature of the above models is that the portfolio risk is described and compared by VaR (Value at Risk)
finally. However, there are two disadvantages of the VaR method, namely, it cannot identify risk
correctly, and it lacks sub-additivity required by portfolio risk specialty, so we cannot use VaR to
optimize portfolio.
In 1999, Artzner and his colleagues raised Axiomized System of coherent risk measure. A risk
measure satisfying the four axioms of translation invariance, positive homogeneity, subadditivity and
monotonicity, is called coherent, which can guarantee that each value corresponds to different risk, and
that measure value of portfolio with greater risk is bigger than that with smaller risk, while measure
value of portfolio with the same risk are the same. Due to the disadvantages of risk value and the
requirements on risk measure put by the development of finance theory, the definition of Cohesive Value
at Risk is introduced into credit risk management. CVaR is the expected value of tail 1 α within a
certain time period T, with α as the confidence level. CVaR has subadditivity, and therefore it is
coherent risk measure, which is very useful to the optimization of portfolio. The ultra-optimization of
CVaR will get the correspondent VaR, and at the same time, as CVaR is the expectation value upon the
measurement of the α at the tail, it can demonstrate correctly the feature that the tail distribution is a
fat tail. Stanislav Unasev(2001) and his colleagues conducted portfolio optimization of credit risk of
bond portfolio by combining the method of CVaR and CreditMetrics, and it is demonstrated that by
introducing coherent measure, the result is even more reliable.
linear discriminate model
..
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2 The Challenge Put on Credit Risk Management
The earliest research on behavior finance’s research can be dated back to the following books in
the 19th century, Gustave LeBon’s The Crowdt and Charles Mackav’s Extraordinan Popular Delustons
and the Madness of Crowds, both of which, the earliest on behavioral finance theory, are still regarded
by many investors as the classical works concerning crowd behavior at investment market. Burrell, who
published an article with the title of Possibility of an Experimental Approach to Investment
Studies, is the forerunner on the research of behavioral finance theory in the modern sense. In his
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article, he raised the method of testing theory by constructing experiment, and further on opened a new
financial field combining quantified investment model with human being’s behavioral features. After
that, Paul Slovic and his colleagues continued to conduct some psychological research on the decision
course of human beings. From the 1960s to the middle 1980s, the research on behavioral finance was
mainly represented by Tversky from Stanford and Kahneman from Princeton. The research by Tversky
was mainly about risk psychology, concerning the three aspects human behavior contradictory to the
basic hypothesis of classical economical model on investment decision, risk attitude, mental accounting
and overconfidence, and he called the observed phenomenon cognitive bias. In 1979, Kahneman and
Tversky raised expectation theory, which has become a milestone in financial history. After the middle
80s, the research on behavioral finance entered in to a golden period, achieving a breakthrough,
represented by Thaler at Chicago University, Shiller at Yale. Thaler(1987, 1999) mainly studied the time
mode of return rate of stock and investor’s mental accounting, while Shiller(1981, 1990a, 1990b) mainly
studied the abnormal fluctuation of stock price, herd behavior at stock markets, speculative price, and
relation between prevalent psychology of the herd, and so on.
Credit risk can also be called default risk, which means the borrower, security issuer or the trade
partner, due to various reasons, will not or is unable to fulfill the contract items so that breach of the
contract and possibility of loss might be caused. Such possibility is closely connected to probability. For
the sake of convenience and conciseness, we can simply regard that if the possibility for the loss of the
borrower is minor, then the credit risk for the investor (lender) greater and vice versa. The loss of the
borrower is closely connected with his credit preference. However, under Efficient Market Hypothesis,
as any economic person is a rational person, his risk preference is always the same. In the meantime, at
the core of the corporate finance theory, based on efficient market, it is regarded that under the
precondition that the investment strategy of the given company is the same, the market value of the
company will not be affected by the capital structure of the company, which is actually the famous
Modigliani-Miller theorem (M&M theory). With the further development of the study, behavioral finance
theory, from the perspective of actual attitude of the investors, raises questions about these two points,
which, in turn, raises direct challenges on credit risk management.
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2.1 Challenge One: The Transformation of Risk Preference
One of the Anomalies which cannot be explained by traditional fiancé is the enigma of excess
stock return, i.e., at security markets in US or other countries, analyzed by the historical statistics, the
return rate of stocks is about 7 higher than that of bonds (Mehra and Prescott). When studying this
Anomaly, Burberry and his colleagues, referring Kahneman and Tversky’s expectation theory (1979),
raised in behavioral finance that the gambling psychology of the investor shall expand along with the
increase on the profits at the stock market (Tbaler and Johnson). This theory, which had been recognized
within the psychology field, was then demonstrated in finance field as well, and it did explain very well
the stock price of the US in 1998. They regarded that a lot of investors in US are sitting at the existing
portfolio of stocks with great returns to wait for their harvest, which has been very high already, and
therefore they are willing to undertake greater risks, as a result, the stock price is pushed up again.
From the study on behavioral finance, the risk preference of the investors are not fixed, which will
be changed by the change of absolute wealth and some other factors. Therefore, we have no reason to
believe that borrower are a special group, as their major aim is usually for investment, which means that
they are a part of the many investors, so their risk preference will be changed as well, which will, in turn,
directly affect the risks put in front of them, which, at last, will affect the credit risk of the lenders.
Therefore, it raises great challenge on credit risk management.
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2.2 Challenge Two: The Influence of Capital Structure on Corporate Value
It is very easy to testify the M&M theory. If the security price reflects correctly the value of future
flow of cask income, then whatever kind of security is distributed, the aggregation of the market value
of all of the securities distributed by the company must be equal to the present value of the future value
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of the company. At the same time, capital structure becomes inessential due to risk-free arbitrage
activities. If two companies with exactly the same essence are sold at the market with different prices
due to their capital structures, the arbitrager can buy the total securities of the company with a cheaper
price and then sell them at a market with comparatively higher prices.
However, when the market is in an ineffective state, the financing structure of the company will not
be irrelevant any more. Different flows of cash income will have different levels of attraction towards
different investors, who are willing to provide a high price for their interested flow of cash income. In
the mean time, according to M&M theory, the arbitrager can guarantee the corporate financing structure
is not relevant to the overall organization. This will cause some problems as well. Even if there is
perfect substitute for one kind of or one portfolio of securities, due to the risk of noise traders, there is
still risk for arbitraging, not to say finding out a certain corporate security of a perfect substitute (Roll
1998 Wurgler & and zhuravskaya, 1999).
When conducting credit risk according to the traditional finance theory, we do not have to consider
the influence of capital structure on corporate value. The current behavioral finance, both theoretically
and demonstratively, has indicated the existence of such influence, and therefore we cannot avoid it any
more in credit risk management. At least, we have to consider capital structure when classifying credit
risk management. Therefore, in credit risk management, we will inevitably analyze different bias of
investors towards capital structures, and the influence brought about by it on risk undertaken by the
target company.
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3 Conclusions
Through the study on the development of credit risk management, we find out that the
development of credit risk management is considering increasingly about the attitude of the investors on
risk; in the mean time, totally from the inspective point of the attitude of the investors, behavioral
finance has demonstrated the theorem that risk bias of the investors transforms and that the capital
structure has a great influence on corporate value, which has put on serious challenge on credit risk
management and exhibit the direction of the study of credit risk management.
References
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[1] Altman E.I., Saunders A Credit risk measurement development over the last 20 years. Journal of
Banking & Finance.1998. 1721-1742.
[2] Croudy, M., Galai, D., Mark, R.. A comparative analysis of current credit risk models. Journal 0f
Banking & Finance, 2000, (24):59-117.
[3] Andersson, F., Mausser, H., Rosen, D., and S. Uncasev. Credit Risk Optimization with Conditional Value
At-Risk Criterion. Mathematical Programming, Series B89, 2001, 273-291
[4] Kahneman,D. and Mark W. Riepe(1998, ). Aspects of Investor Psychology: Beliefs preferences, and
biases investment advisors should know about. Journal of Portfolio Management Vol.24 No.4.
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