Guest Post: Asymmetric Information in the Banking Sector: How adverse selection & moral hazard affect our banking system


By Munene Laiboni

Q: Which is one sure way to kill a rat but its hideout can’t be traced? A: Set the whole house on fire ~ my high school math teacher”

The economy of any nation – Our OPEC-Member-in-waiting included – is heavily dependent on the banking sector to provide the needed capital so as to keep its economic activity abuzz. It’s common knowledge that capital can either be raised in equity markets or debt markets. Nevertheless, Kenyans have always had a thing for debt and the little confidence that equity had started accumulating has been largely eroded (partly due our bourse’s terrible performance in recent times). Many of our commercial banks posted super-profits inasmuch as our economy is still recovering from effects of last year’s drought and the downward spiral of the shilling. One might ask, “how did the banks make all that money?” Well, the question is really straightforward – the interest rates on loans were at record high levels last year as a direct result of the intervention of the regulator toward the end of last year to save the ailing shilling. However, there are other factors independent of Central Bank Intervention which might as well affect the interest rates charged on individual’s loans. In this paper, I will discuss how information asymmetry is a key determinant of interest rates and how it lead to adverse selection and moral hazard – two possible occurrences which are clearly not pleasant to bankers.

Information Asymmetry:

Information asymmetry is asymmetric distribution of material information which could be influential in decision making by both parties to the loan agreement. Suppose an entrepreneur comes up with a business idea, and is looking to raise capital for the actualization of his dreams. Ordinarily, he will approach a bank with his business plan in hand but although the bank will try to do due diligence before disbursing the loan, the investor will always have has a better understanding of the prospective returns and risks of the business than the lender. Asymmetric information creates problems in the banking sector both before the transaction is closed (adverse selection) and after the transaction has been closed (moral hazard)

Adverse Selection – Occurs when bad credit risks (firms which have poor investment channels and high inherent risks) become more probable to acquire loans than good credit risks (firms with better investment opportunities and less inherent risks).

Because of information asymmetry, lenders tend to have a hard time differentiating between good credit risks and bad credit risks, and demand a blanket premium over and above the existing rates as compensation for the risk arising out of the inability to determine who indeed should be lent to. This causes the good firms to stop borrowing from such a lender because the high rates have devalued their strong credit history while the bad firms become very eager to borrow from such a lender because they know for sure that judging by the strength of their cash-flows, they should be charged an even higher interest rate. As a result, lenders end up with a loan portfolio comprising almost entirely of bad credit risks.

Moral hazard – Moral hazard occurs after the money has been disbursed to the borrower and it arises out of the fact that the borrower may have an incentive to breach the loan covenants by investing in ‘immoral projects’ which are unacceptable in the eyes of the borrower because inasmuch as they have a high possibility of gain to the borrower, they also have a high possibility of failure which will have the most detrimental effect on the lender. Information asymmetry once again causes moral hazard because of the lender’s lack of knowledge about the borrower’s activities. Moral hazard also occurs as a result of high enforcement costs of the debt covenants. In this instance, the lender simply decides that its not worth the effort to keep on chasing after borrowers and have them invest the money in stipulated projects – giving them a freeway to invest in high risk ventures.

Remedies to Information Asymmetry:

Now that we have seen how information asymmetry has detrimental effects on a nation’s banking sector, let’s look at some ways through which we can reduce this problem in our banking sector.

Credit Referencing – In the developed world, any firm which wishes to sell its debt must ranked by credit referencing firms (the main ones being American firms – Standard & Poor’s, Moody’s, and Fitch ratings) Firms are ranked by the strength of their repayment ability anywhere in a range which varies from the esteemed AAA rating to the lowest rating (high yield/junk rating). Judging by the credit score, a lender is able to approximate the probability of default of any single borrower and charge a rate of interest which is proportionate to the innate risk evident in a borrower’s enterprise. In Kenya, credit referencing is yet to take root and as at writing of this article, I am not aware of any firm which offers this crucial service in Nairobi. I think it’s the high time we embraced credit referencing as a way of reducing information asymmetry.

Data Sharing – Due to the secrecy which has continued to shroud our banking system, at times there are red flags all over about serial defaulters but banks miss them simply because they consider immaterial information about their customers as classified. Thus, a person may become unable to repay a loan on a bad investment idea (which he won’t drop because of his singlemindedness) and get his property auctioned but walk over to a different bank and secure financing for the same bad project, probably using a different product. If our bank’s IT systems could get partially integrated, this is a problem which can be quite easily eliminated.

Government Participation – Due to the vulnerability and corruption in our administration systems, there has been instances of forgery of collateral documents such as title deeds. The land records at Ardhi House are, for example, manual and computerization of land registration in this country is long overdue. There has been instances in which several title deeds have been issued on a single piece of land, and I do not want to imagine what would happen if each and every one of those holders would approach his bank for a loan with his title deed as collateral and default on the same.

Improved Loan Underwriting – In some commercial banks, loan underwriters are basically data entry clerks. They simply key in information from an application form without paying due attention to material facts which could be evident right on the application form. Underwriting is the entry point of risk in any financial services firm and some risks could be avoided if this initial process could be carried out meticulously. There is always an essence of evaluating the proposed project’s cash generation potential, its SWOT analysis and its history before acceptance.

THE DAY’S

Gratitude to my good friend Munene Laiboni for this informative guest post.

About the Author: Munene Laiboni is a financial advisor. You can find more of his work here and you can follow him on twitter munenelaiboni.

Guest Post: Capital Asset Pricing Model and Its Application to Investment Risk Management


By Samuel K. Kiranga

The task of investment capital budgeting and project appraisal can be a complex and challenging management issue. Many modern methods have been invented and developed to aid investment managers and analysts in finding the right mix of assets in any portfolio or project to which the company intends to make investment return on its excess cash flow. The concept of time value for money has been crucial in the development of these appraisal models and techniques and some of the models that make use of this idea are Discounted cash flow techniques, Accounting rate of return, Profitability index, Internal rate of return and Real options. When using discount rates for establishing the net present value of an investment model such as the Capital Asset Pricing Model, mean variance portfolio analysis and weighted average cost of capital are useful in today’s business environment. We shall take an intrinsic analysis of the Capital Asset Pricing Model, and explore its application to the investment strategy of the multinational telecommunications firm Vodafone Group.

The Capital Asset Pricing Model

Introduction

The Capital Asset Pricing Model concept was developed by Sharpe (1964) and this work on the subject of portfolio management won him the 1990 Nobel Prize in economics. The model was founded upon earlier works on investment portfolio analysis. A portfolio is a set of different assets that are held for investment purposes. In his paper on modern portfolio theory, Markowitz’s (1952) puts forward the idea that the risk accompanied by investing in such a portfolio of assets in a particular ratio outlay is lower than that of investing in each asset separately using the same level of capital funding. This idea is referred to amongst investors as portfolio diversification. In investment analysis though, Sharpe recognizes two types of risk to the prospect of an asset’s returns i.e systematic and non systematic risk. Systematic risk is risk that cannot be reduced by diversification and may come as result of factors such as interest rates and inflation trends on the entire market while non-systematic risks can be reduced by diversification as shown by Markowitz and includes risks for specific assets in a stock market. (Wang, 2003)

Credibility of Risk Valuation within the Investment Model

The backbone of the CAPM decision making strategy is Harry Marcowitz’s (1952) Mean variance portfolio theory which basically states that for a given market the optimal market line on which to base your asset selection is that which minimizes standard deviation and for any given expected return upon investment of the asset. The standard deviation in this case represents the individual asset’s risk. Typically this market line is known as the efficient frontier. This line represents the most economic points in terms of capital investment value at which investors are willing to trade off expected return for minimal risk. In a market that combines risk free investment options (e.g loans and treasury bonds) and risky assets such as stocks, the graph of expected return against risk (standard deviation) will have a tangent line that runs through the risk free return rate on the y-axis while touching the efficient frontier at a point where minimal risk is absorbed if the maximum expected returns are to be brought from including the risk free options into the portfolio. The resultant equation for calculating the expected return of such a portfolio is known as the Capital Asset Pricing Model and appears as follows,

E[˜ri] = E( rF ) + βiM(E[˜rM] − rF)

Where ˜ri  is the return on asset portfolio i , rF is the rate on the risk free asset, ˜rM is the return on the risky market asset and βiM (beta) is the systematic risk βiM = σiM/σ2M

The first term on the right-hand side of the equation, rF,,is the expected return on risk free assets that have market betas equal to zero, which means their returns are uncorrelated with the market return. The second term is a risk premium, i.e the systematic risk function beta of asset i, βiM, multiplied by the premium per unit, which is the expected market return, E(RM) less E(Rf). (Farma & French)

Hamilton (2004, p4) stated that the different between a 15% discount rate and a 14% discount rate can mean a difference in value conclusion of hundreds of thousands of dollars. How could that be? Take an example of the following scenario;

Suppose that a market’s expected return (discount rate) on a stock is 14% and the treasury bills are repaid at a rate of 5% then with a beta value of 0.75 on the stock, what would the expected return on one unit of stock be?

By simply applying the above formula we will have:

E(˜ri) = 0.05 + 0.75(0.14 – 0.05) = 0.1175

Now take an example of an altered risk premium that comes from a discount rate of 15 %.

The calculation then shifts to

E(˜ri) = 0.05 + 0.75(0.15 – 0.05) = 0.125

Evidently we can already see that the non-systematic element in each of the answers remains constant and so does the expected return on risk free assets. Thus we use a standard capitalization to see the difference in using either discount rate. Suppose a company such as Vodafone which makes billions in net profits annually invests Kshs. 10 Billions in the above CAPM portfolio with the funds going the a risky market asset. The expected return under the 15 % rate will be 0.125(10,000,000,000) = 1,250,000,000 while under the 14% rate it will be 0.1175(10,000,000,000) = 1,175,000,000 which is an entire difference of Kshs. 75,000,000. Economically speaking, βiM is proportional to the risk each unit currency investment in asset i adds to the market portfolio (Farma & French, 2004) while the risk premium signifies the value added by investing in the risky asset over risk free options. These combination of factors thus add credibility to the model by incorporating an asset specific risk factor for consideration during the investment appraisal process.

APPRECIATION: I am grateful to Sam Kiranga for this insightful post. You can find more of his work here

4 Facts about Actuarial Science


It is easier to become a professor in Actuarial Science than become an actuary – Dr. F. Onyango.

One day as I did what I know best – which is to mind my own business – I was punished by being the proud person to overhear two grown, educated men talk like it was a crime to be intelligent. Sample this:

MAN A: District X produces the creme de la creme students in the country. It has produced over 6,000 actuarialists (sic).

MAN B: They are called Actuarial  Engineers or Scientists!

I had to interject to save myself from all this non sense.

ME: They are called actuaries and I highly doubt…

Just before I had spat out my two sense, I was cut short by…

MAN B: Sasa wewe kijana unafikiri sisi tulizaliwa jana?*

I immediately got an urge to die a natural death or if not to atleast puke to save myself from any more B.S from these two guys. The questions that will always linger in my mind are: Why were stones wasted on Stephen (the first martyr) when we have worthy contenders for the price of Stoning? Why could not the killers of Tupac spare him and kill guys who are worthy to die for their chupidness? Since those two men could not listen to me as I had been born yesterday, I will atleast speak to you because, like me, you were born yesterday as the proud son/daughter of Mr and Mrs. Intelligence!

Here are a few facts about Actuarial Science:

1. Advice to kids in high school who want to be actuaries: If at one time in your life you hated mathematics (even for just one lesson), do yourself a favour and look for another career. The commandments that you need to follow to become anything near an actuary are just three: Love Mathematics as you love yourself. Love Calculus as above and Love Probability as above. If you still feel that with your small hate for mathematics, you can still cope, then go ahead. It is anyone’s duty to warn any kid of the consequences of a sharp blade. It is more fun to watch the spoilt little brat kid (if he is not your own) cry after the blade has cut him though.

2. In order to be an actuary, you have to pass some very difficult exams. Ask anyone who has attempted them and they will tell you how easy it is to fail when you have read, crammed and mastered everything concerning a particular paper. If you have not done the above three, you will UNDOUBTEDLY FAIL! Having a first class in Actuarial Science does not make you an actuary!

3. Numbers: Based on 1 and 2 above, it is highly unlikely that a certain district in the country can produce 6,000 actuaries. Infact, the number of students who have graduated with an Actuarial degree in East and Central Africa is less than 6,000. (these are no where near being called actuaries). More puzzling is the fact that there are less than 35,000 actuaries in the world! I hope there are others in other planets. Compare that with the over 300,000 teachers in Kenya alone. There are less than 15 actuaries in Kenya. (the actual figure is 11 but I have decided to add 4 incase 1 or 2 qualify in the next few years). Of these, only one did not study abroad. In summary, after completing your Actuarial degree, you have a 1 in 1000 chance of being an actuary. The probability of death is about 9 in 1000. which means you are more likely to die than to qualify as an actuary!

4. Having said that, being an Actuary is the best job . It was named the best career of 2007, and has been ranked atleast in the top 4 in the last 6 years. Find other honours here.

Also read Actuarial Science in Kenya by Lucy Muthoni.

THE DAY’S

NOTE: Elizur Wright is considered by many as the father of Actuaries/Actuarial Science. You can read about him here

Quote: click here and here

Disclaimer: Once in a while I think about semi-serious stuff. This was one of those whiles.

*Young man, do you think we were born yesterday?