Starting off with a jab on California and two pinchers on jobs, one on social security and combo on Greece and banks. Enjoy.

We begin by asking the following questions:

• Should you focus on the best case or worst case scenario for your financial statements?
• How deep should you go in your analysis?
• What is credibility and how is it achieved?
• How do footfall and ticket size apply to financial modelling?
• What are top-down and bottom-up approaches to financial modelling?
• What is the Break-Even level?
• What should your final financial statements look like?

Back to Basics

For a start up, financial modeling is essentially developing financial statements for the future business which you wish to undertake. It is much more than just juggling with numbers to form profit, expense, capital and revenue statements. Before drafting any document or statement for an investor or venture capitalist it is paramount to understand their psychology. Potential investors are interested in your credibility and will not write that first cheque until they are convinced of it. Your credibility is defined by conveying how well you understand the nature of your business and knowledge of various aspects of it. Having a solid business idea and an effective way of making it a reality is what the investors will actually be looking for when assessing your credibility. Your projected financial statements will help to convey this to them to a large extent.

The first step in drafting your projected financial statements is to determine the expected footfall and conversion rate, i.e. the number of customers expected and those who will convert. There are two approaches to do this. First is a top-down approach. Suppose you are selling clothes for children. The client-base is 3 million households in the region of the city you are planning to operate in. Assuming each household has only one child and that only one out of every three households will actually buy an item of clothing within a suitable time period, e.g. 3 months. Assume additional filters that could impact your client base such as competition as well as how much you would actually be able to cater for, etc. In this manner you arrive at the footfall; the number of customers expected at your doorstep. What remains next is to convert them into effective sales. Not all those who arrive at your outlet will purchase clothes. For example, you may assume that out of all those who come to your shop, only 0.5% of them may actually convert into sales.

The second approach is the bottoms-up approach. With this approach you begin with how much you need to make to achieve your objectives. For instance, you are planning on opening a restaurant and daily costs work out to $5000 per day. If you sell 100 meals at $50 per meal your sales revenue would equal your cost. This is your breakeven level. The breakeven level is the output at which costs equals revenues resulting in a net profit of zero. Out of the two approaches, the bottom-down approach is more credible because it is more realistic and based less on assumptions than the top-down approach.

The second part of credibility is to communicate your plans to enable conversion as well as what will be the size of your ticket sales. Online forum sites such as Quora have discussed conversion rates for a number of real world businesses including retail stores where rates have ranged from 1% to 10%. Ticket size applied to the conversion rate gives sales revenue. Ticket size is the amount generated in an average transaction.

The next step is to match ticket size with costs (fixed and variable) per transaction to arrive at the net profit margin.  To determine the cost break down a transaction into various components. What procedures/ items are required to make a single piece of clothing? What is the cost of each procedure/ item?  Determine the steps from raw material to production to inventory to sale. Add value at each step to cover costs and profit margin.

In your projections cover both best case and worst case scenarios. However, what investors really look for is a realistic case. The greater the degree of realism in your business plans the more serious you appear to a potential investor. Set realistic benchmarks that can be met. For instance, if you will be starting in the retail clothing business it may be wise to set cost benchmarks that are comparable with the local clothing market.

Revealing Insights

Your financial model should be linked to your understanding of your business. Target a particular customer segment; see their price tolerance or appetite and purchasing frequency to arrive at the profit margin you would be able to generate.

After determining the average market transaction you need to define your capital needs. This depends on the nature of your business. Also, the investment capital that you will be asking for should cover a period of one year for support so that your business can be functional and liquid. This one year is a period of experimentation – a period to see how your initial assumptions fare in the real world. The capital needs should be divided into startup capital and working capital.

Will you be making a profit after 18 months? Not necessarily. The time-to-breakeven and how sustainable the break even position is, needs to be determined. It is import to determine the time-to-breakeven on a cash basis; not on an accrual basis. Time-to-breakeven on a cash basis is the time taken for net cash-flows to turn positive, whereas on an accrual basis costs and revenues are assigned to a stated period whether actual payments were received or not. For a startup liquidity is very important and it is also very important to the investor therefore determining time-to-breakeven on a cash basis is crucial. This involves making a month-by-month or quarter-by-quarter analysis of how close you are to approaching breakeven.

Investors are keen to know what the payback period is for your business. This is the time required by the business to pay off investments and achieve self sufficiency. Investors like a moderate payback time depending on the nature of the business. Also the mode of payment is important. Will the investor be paid in cash or will he be given shares in the company or will be continue as a partner in your firm, etc.

Facing investors

Credibility comes from this; determining the market size and explaining it coherently in front of the investors. Time for presenting your case is limited therefore you need to be succinct and precise. The projections of the financial statements should be for a number of years. Investors are interested in how you plan to increase revenues over time. To effectively communicate this you need to understand the nature of the market and how it interacts with your business. Market research and asking potential customers will help you be more aware. However, the projections and commentary should be brief avoiding unnecessary details to keep potential investors interested and engaged.

For the model you can start off with a simple spreadsheet containing a separate section for assumptions (e.g. discount rate, inflation rate, revenue growth, etc), a revenue model and an expense model. The latter two are transaction based. Project the revenues and expenses to determine the net profit or loss for a number of years.

Next you can make a more detailed model but note that this increases the complexity so only include details if you have sufficient data to back it up.

Your final model that you will present however should be easy to understand. What is more important than the actual numbers used is that these figures reflect your vision. Credibility requires more than just a presentation of numbers; you must be able to defend your figures. This requires an insight into the nature of the business and a depth of understanding of the projected figures.

What happens next once the business has started

It is imperative to know that investors will pay you survival money not your market value. Suppose you are a lawyer and your market worth is a salary of $10,000 per month. However, if you open your own legal consultancy service, an investor will pay the money to keep your business surviving which may not include your salary. This is one of the drawbacks of starting up a business. There can be no returns made without risk and compromise.  Adopt the 3 month milestone framework if you are serious about entrepreneurship. Set a 3-month target to obtain a customer or small number of initial ‘break-through’ customers.  Keep reflecting and revisiting your assumptions based on real market experience. If assumptions are not being met obtain feedback and investigate the reasons why they are not materializing. For example if the growth assumption is not being met obtain feedback from customers as to their reasons for not buying.

The first year of business is precisely the time for such experimentations because this is when your assumptions face stark reality and need to be adjusted if needed. It is important to understand that investors will cover your experiences not your assumptions. Reflect continuously every 3 months or so on all your targets and goals based on real world experience. Initiate a $100 experiment – spend $100 on your business and see where it goes and where it leads you. Investors look for realistic answers which show that you have done your homework.

Based upon your experience, see the trend in your sales. Does it show signs of customer loyalty? If so, understand that it is important to lock-in customers. Loyalty informs you of your core competencies which need to be strengthened and built on.

Brought to you by the fine folks at FinanceTrainingCourse.com

For a call option, assuming that all the rungs are in one direction (i.e. all long calls), on maturity the payoff will be the maximum of the underlying asset’s price at maturity and the rungs reached, less the original strike. In effect this means that the payoff will be the maximum of a) the terminal price less the original strike or b) the highest rung reached less the original strike. The payoff will be floored at zero.

In a similar manner the payoff for a put option with all rungs structured in the same direction (i.e. all long pulls), will be the original strike price less minimum of the terminal price and the rungs reached. In other words the payoff will be the maximum of a) the original strike less the terminal price or b) the original strike price less the lowest reset strike/ rung reached. The payoff will be floored at zero.

Let us now consider the following examples. Note that we will be using a ten time step Monte Carlo simulator to simulate the future prices of the underlying asset. For further information on how to build a Monte Carlo simulator in EXCEL you may like to see the following course:

Computational Finance: Building Monte Carlo (MC) Simulators in Excel

Let us assume that you are bullish on the stock of McDonald’s Corporation (MCD). You therefore buy a one year Ladder European call option on McDonald’s Corp.’s stocks that can be exercised only on maturity. The current price of MCD stock is 90 and the original strike is set at 92. The ladder for strike resets are at 97, 99 and 109 respectively. The volatility in MCD is 30%. The risk free rate is 2% and there are no dividends on the stock.

Every time the stock price reaches a particular rung the strike will be reset to the price at the rung and a profit/ payoff equal to difference between the new strike and the old strike will be locked in. For example consider the following path of prices generated using the Monte Carlo simulator:

At time step 1 the stock price is 106.90. This means that the option has reached the second rung price of 99. Therefore the payoff of 99-92 = 7 will be locked in. At time step 3, the option has now reached the third rung price of 109 as the underlying asset’s price is now 122.73. An additional profit of 10 (=109-99) will be locked it. So, the total profit locked in is 17 (=7+10). The terminal price of the option at expiry is 88.71 in this scenario which is below the original strike price of 92. In a regular vanilla European call option this would have resulted in no payoff as the Max (Terminal Price – Strike Price, 0) is zero. However in the case of the ladder option the payoff for you, despite the terminal price being below the original strike price, will be that already locked in, i.e. 17.

To arrive at the result in another way consider that the maximum price in the price path series is the price at time step 3 of 122.73 which means that the highest rung price reached was the third reset of 109. As mentioned earlier the payoff is the Maximum of (Highest Rung Reached-Original Strike, Terminal Price –Original Strike, 0) or stated in another way Maximum of (Highest Rung Reached, Terminal Price) – Original Strike, floored at zero. That is,

Payoff = Max(109-92,88.71-92,0)=Max(Max(109,88.71)-92,0)

Payoff = Max(17,-3.29,0)=Max(109-92,0) = 17

The ladder call option premium is then simply the discounted value of the payoff = Payoff × e^-rT
Ladder Call Premium = 17e-0.02×1 = 16.66

Using the Data Table functionality we simulate results for 100 scenarios as follows:

The average of all the results in the Call Premium column is the price of the ladder option obtained using the Monte Carlo simulation approach. Based on 100 scenarios this price falls in the range between 9.5-19.5. One view of the distribution of the results across these 100 scenarios is given in the following graph:

To get a more stable and accurate value or a more narrow range of results for the ladder call premium, the number of scenarios may be increased or the methodologies mentioned in the following post may be used:

Convergence and Variance Reduction Techniques for Option Pricing Models

Visit our store to purchase the EXCEL file “Pricing Ladder Options using a Monte Carlo Simulator”, a detailed numerical example of ladder call option pricing.

Here are a few hints from recent media coverage about the direction of oil prices in the coming months.

• Noted by Moming Zhou of Bloomberg, the first update was Fed’s reluctance to add another stimulus to the economy. Combined with the upcoming refinery turnaround season in China, and the jump in inventory, the news was enough to push oil lower.

   

• The second update, covered by Robert Rapier on FinancialSense.com is important not because it sets the record straight on shale formations and the true picture of US reserves. Many politicians in US have been referring to the Green River Formation in Colorado Utah as a field with reserves larger than those of Saudi Arabia. Robert clarifies the position under the ground.

   

• Gregor     Macdonald also at FinancialSense.com discusses how supply is the main factor rather than demand and currency. Supply has been slow to grow causing the prices to rise. Building up on his work on Peak Oil, Gregor predicts increased volatility in oil prices in the New price of oil

On “The Recovery”

On Immigration and Republicans

On Executive Compensation on the Street

Risk Video Training Courses: ALM and Capital Adequacy Course

Description

The “ALM and Capital Adequacy Course” serves as an introduction as well as a refresher course to Asset Liability Management. The course is divided across a number of core topics from basic concepts such as duration and convexity to more advanced topics such as ALM measurement tools. The following topics are covered in the course:

• Introduction to ALM
  
• Interest Rate/ Maturity Mismatch Risk and Liquidity Risk
  
• Duration and Convexity, including relationship with options and volatility
  
• Asset and Liability Sensitivity
  
• ALM framework
  
• Building blocks for an ALM model
  
• ALM measurement tools
  
• ALM reporting
  
• ALM Stress Testing
  
• Introduction to Capital Adequacy (including a background of capital adequacy regulation)
  
• Internal Capital Adequacy Assessment Process (ICAAP) & Liquidity Risk Capital Extensions
  

Practical Skills Takeaway

You will be able to understand how to build an ALM framework as well as how to use and interpret ALM measurement tools.

Pre-requisites

Familiarity with basic asset liability management concepts, value-at-risk (VaR), local markets, portfolio management and the Basel II framework.

Audience

The course is targeted to intermediate and advance users and is aimed primarily at banking professionals and individuals responsible for asset liability management and risk management within banks, insurance companies and mutual funds who need to quickly review or refresh their understanding of ALM and Capital Adequacy regulations for work, professional review, audit or personal development.

Risk Video Training Courses: Option Pricing using Binomial Trees

Description

The “Option Pricing using Binomial Trees” video course presents an alternative method of implementing a two-dimensional binomial tree compared to the traditional method of building a binomial tree in Excel. The alternate approach is based on the techniques documented by Professor Mark Broadie at Columbia Business School as part of his coursework in Security Pricing and Computational Finance courses at Columbia University.

The course begins with the pricing of plain vanilla European call and put options; then is followed by the pricing of American options and Knock out and Knock in (Sudden Death) exotic options.

Practical Skills Takeaway

You will be able to extend a simple 3 step tree to a 50 – 100 step option pricing tree in a few minutes to improve the accuracy of the results.

Pre-requisites

Familiarity with basic asset liability management concepts, value-at-risk (VaR), local markets, portfolio management and the Basel II framework.

Audience

The course is targeted at intermediate and advance users and is aimed at professionals who deal with pricing, valuation and risk issues related to structured fixed income and foreign exchange transactions.

Risk Video Training Courses: Cross Selling Treasury Products

Description

Price volatility in crude oil, gold, silver, cotton, sugarcane, wheat and cereals has created an unprecedented opportunity for corporate relationship managers to cross sell treasury products to their institutional, trading, manufacturing and high net worth customers.

The “Crossing Selling Treasury Products” video course presents a framework for empowering client facing treasury teams to go out and cross sell high value, high margin trading concepts to clients. They do this by educating customers about their exposures and by presenting a range of available solutions that would help reduce the risk associated with these same exposures.

Practical Skills Takeaway

You will understand the five core themes surrounding treasury sales team discussions with customers on trading products and related concepts.

You will learn about the various trading strategies offered by Treasury marketing teams and ways to address TMU customer reactions to those strategies.

Pre-requisites

Corporate Banking and Treasury products, Value-at-Risk (VaR) and counterparty limits.

 Audience

This course is targeted at intermediate and advance users and is aimed at banking, corporate, treasury and sales teams, relationship and account managers that offer or present derivative products as part of their marketing and sales efforts and repertoire.

Risk Video Training Courses: Option pricing using Monte Carlo simulation

Description

The “Option Pricing using Monte Carlo Simulation” combines both theory and practice and uses model building and option pricing exercises as learning tools to tie in a number of essential topics such as:

• the difference between and the significance of the risk-adjusted probabilities of the Black Scholes option pricing model
• the intuition behind the Black Scholes European call option formula
• a preliminary overview of how to create a Monte Carlo simulation model of the Black Scholes solution in Excel
• Estimating errors of and improving results generated from the Monte Carlo simulation model
• Pricing vanilla and exotic options using the Monte Carlo simulation model

The course splits the time evenly between building a model step by step in EXCEL and working with the model to price and value derivative products.

Practical Skills Takeaway

You will be able to build a Monte Carlo simulator in EXCEL and use the model to price vanilla and exotic options.

You will be able to improve the results of the simulation model by increasing the number of runs/ scenarios with the use of EXCEL’s data table functionality.

Pre-requisites

Comfort with basic mathematics, numbers and EXCEL and familiarity with derivative products.

 Audience

The course is targeted at intermediate and advance users and is aimed at professionals who deal with pricing, valuation and risk issues related to structured fixed income and foreign exchange
transactions.

Risk Video Training Courses: Quant Crash Course

Description

The “Quant Crash Course” covers volatility, value at risk, capital and limit management frameworks for a treasury function. The course material has evolved over a number of years as part of our risk training practice particularly in the areas of derivative pricing and risk management. The following topics among others are discussed in the course:

• The Distribution or generator function
• The importance of Volatility and Correlation to a risk management or control function
• The importance of having pre-trade controls and limits
• Duration, Convexity and Optionality
• Value at Risk (VaR) measure and its various applications
• The limitations of using a VaR measure
• Various types of capital and risks for which capital may be attributed
• A capital focused risk management framework
• A limits management framework
• Types of limits such as stop loss, transaction, expectations and counterparty limits

Practical Skills Takeaway

You will understand terminology such as volatility, correlation, duration, convexity, optionality as well as their importance to the risk management function.

You will learn about the various Value-at-Risk methods, the difference between methods and the qualifications for using this tool to measure risk.

You will understand the various elements of a limits management framework.

Pre-requisites

Comfort with basic mathematics and an understanding of the risk management environment together with a familiarity of financial markets, banking industry, economic capital, portfolio management concepts and the Basel II framework.

 Audience

This course is targeted at intermediate and advanced users and individuals responsible for capital allocations, limit setting and risk management with the treasury functions of banks and other financial institutions.

Risk Video Training Courses: Selling Derivative products

Description

A derivative product is a financial instrument whose value is determined completely by external variables. The external factor, or the underlying, could be anything but in general is either a financial asset or an economic variable (such as interest rates). Derivative instruments include forward and futures contracts, vanilla and exotic options, and swaps.

The “Selling Derivatives Products” course introduces basic vanilla and exotic derivative products. Vanilla products, such as forwards, futures and options, are discussed in detail with simple examples. A comparative view of these products is also presented to highlight the similarities and differences between them.

Practical Skills Takeaway

You will understand:

• the uses of core treasury products and other trading strategies.
  
• vanilla and exotic derivative products.
  

Pre-requisites

Some familiarity with corporate banking and treasury products.

 Audience

This course is an introductory course on derivative products for beginners and is also aimed at banking, corporate, treasury and sales teams

Risk Video Training Courses: Setting Value-at-Risk, Stop Loss, Pre-settlement and Counterparty limits

Description

There are a number of challenges when it comes to communicating, enforcing and setting limits in a trade, treasury, portfolio and risk function. One big factor is the language of limits. Traders speak and understand Stop loss, risk managers work and vouch for value at risk, credit officers track and live for PSR (Pre-Settlement). How do we link the three?

The “Setting Limits” video course introduces the linkage between Stop loss and Value at Risk by defining the median trading loss. We then link the median trading loss to a VaR confidence level that can be used for communicating results and suggesting limits to Board Risk Committees. Next we focus on PSR (Pre Settlement Risk) limits calculations. PSR limits are counterparty limits that calculate the worst case likely loss on account of default on the settlement date by a given counterparty. Once again a VaR measure subject to changing volatilities, our approach highlights the usage of maximum, minimum and median volatilities and suggests that the underlying VaR estimates need to be reviewed more frequently than the once in 2 or 3 year practice we have observed in the region.

Practical Skills Takeaway

You will understand how to link Stop Loss and Value-at-Risk limits and how to calculate VaR- based PSR limits.

You will learn how to communicate VaR results to Board Risk Committees in terms that will make sense to them so that any recommendations made in view of those results may more likely be heeded.

Pre-requisites

Comfort with basic mathematics and EXCEL and an understanding of the risk management environment together with a familiarity of financial markets, banking industry, economic capital, portfolio management concepts and the Basel II framework.

And the following video courses:

• Quant Crash Course
• Calculating Value at Risk

 Audience

This course is targeted at intermediate and advanced users and individuals responsible for capital allocations, limit setting and risk management with the treasury functions of banks and other financial institutions as well as finance departments of non-financial organizations.

Risk Video Training Courses: Calculating Value-at-Risk

Description

One of the most asked questions in risk management has been: How much do you stand to lose, over a certain period and with a certain probability? The common answer to this question today is Value at Risk, a risk measure that expresses itself as one number.

This course takes an in-depth look at the calculation methodologies of the Value at Risk measure. We review Value at Risk (VaR) calculation methods in particular the Variance-covariance approach and the Historical simulation approach. We build a simple portfolio comprising of Euro, Australian dollar, Yen, GBP, Brent, WTI, Gold and Natural Gas and calculate VaR for the portfolio using both of these methodologies.

Practical Skills Takeaway

You will learn how to calculate the Value at Risk measure using the Variance-covariance approach and the Historical simulation approach for individual securities as well as a portfolio of securities in EXCEL.

Pre-requisites

Comfort with basic mathematics, statistics, probability and EXCEL and some familiarity with local markets and portfolio management.

 Audience

The course is targeted towards intermediate and advanced users and is aimed primarily at individuals responsible for capital allocation, limit setting and risk management within banks, insurance companies, mutual funds, as well as finance departments of non-financial organizations who need to quickly review or refresh their understanding of VaR methodologies for work or personal development.

Risk Video Training Courses: Stress Testing

Description

The “Understanding Stress Testing” video reviews the concept of stress testing, risk capital, value at risk, asset liability management and capital adequacy applications for stress testing. The following topics are covered:

• The need for stress testing
• Stress testing capital
• Stress testing framework
• Stress testing methodologies for price risk, credit risk, interest rate mismatch and ALM risk
• ALM reports

Aside from the main course there are a number of annexure topics that cover the following:

• Value at Risk
• Capital and Understanding Capital Adequacy & ICAAP
• Duration & Convexity

Practical Skills Takeaway

You will understand how stress testing ranges can be calculated using tools like value at risk, analyst expectations and worst case price history.

You will understand the essential elements required for creating a viable stress testing framework.

Pre-requisites

Familiarity with economic capital, local markets, portfolio management concepts and the Basel II framework.

 Audience

The course is targeted at intermediate and advanced users and is aimed primarily at banking professionals who need to review stress testing material for work, professional review, audit or personal development as well as for individuals responsible for capital allocation, risk management and implementing the ICAAP framework within banks.

Risk Video Training Courses: Understand N(d1) and N(d2)

Description

The “Understanding N(d1) and N(d2)” course provides a theoretical and practical overview of the risk-adjusted probabilities of the Black-Scholes option pricing formula. The theoretical overview considers the various elements underlying the Black-Scholes European call option formula, whereas the practical application involves the creation of a Monte Carlo simulation based model in Excel to further clarify these concepts.

Practical Skills Takeaway

You will have a better understanding of the risk adjusted probabilities of the Black Scholes model.

You will be able to build a basic Monte Carlo simulation model to illustrate the difference between N(d1) and N(d2) and to calculate the call option value using the Black Scholes formula within the Monte Carlo simulation approach for determining N(d1) and N(d2).

Pre-requisites

Comfort with basic mathematics, statistics, probability and EXCEL and some familiarity with derivative products and pricing.

 Audience

The course is targeted at basic and intermediate users and is aimed at individuals who deal with pricing, valuation and risk issues related to structured fixed income and foreign exchange transactions.

Basic concepts of risk management

So you’ve decided to take the first step in your quest to understanding the risk management framework of Banks and their regulations, like Basel II and Basel III. This involves understanding the basic concepts underlying most of risk management, concepts that you will come across time and again as you make your way through the immense store of knowledge available out there on this subject. Among other things these include:

1. Risk Preferences
2. Diversification
3. Arbitrage
4. Market Efficiency
5. Market Completeness
6. Asymmetric Information
7. Time consistency
8. Moral Hazard
9. Nash Equilibrium
10. Bayes Rule

Risk Preferences

We first consider the interaction of economic agents with the economic situations they face. It is necessary to understand the psychology of the participants before reaching any conclusion about their behavior. What are the varying attitudes among participants engaged in risky activities?

Suppose you are playing a game in which you are presented with two options;

1. Receive a guaranteed amount of $200 or
2. Consider this gamble: A fair coin is flipped with equal chances of heads or tails showing up. If it turns up with heads you win $400, if the result is tails, you get nothing.

What would you choose? If you say both options are fine and equal then you are being indifferent to risk. That is the first attitude; of being risk neutral.

Your friend ignores the gamble and instead opts for the first option which guarantees $200. His choice indicates that he is risk averse. In order to partake of the second option the risk premium he would need from that option needs to exceed the guaranteed option. In this case the expected return from the second option (0.5*400+0.5*0 =200) matches the guaranteed payoff of the first option. This means the risk premium for the uncertain option is zero and therefore, preferring less risk to more, he will opt for the first option.

Another friend says “If I was offered $250 instead of $200 in the first option I would go for the certain option, but as it stands I would like to try my luck with the second option”. This shows that he is risk loving, preferring the riskier and potentially higher rewarding option to the certain one, in a situation where expected returns from both options are the same. Next time you want to go on a roller coaster ride with someone, be sure to invite this risk preferring friend.

Why know all this? An important assumption in finance theory is that individuals are risk averse requiring greater risk premium to compensate for undertaking greater risk.

Diversification

We’ve all heard of the saying: “Don’t put all your eggs in one basket”. This provides a fairly accurate picture of diversification in finance – invest in multiple opportunities to reduce your overall risk. For risk adverse individuals this is an important concept.

Consider the following instance. You have some spare money which you want to invest to fund your college education. What would be more beneficial for you; buying stocks of just one company or buying stock from 20 different companies bellowing to varying industry or sector categories? Choosing stocks whose returns are not strictly positively correlation ensures that as the number of stocks in your portfolio increases, the risk of any individual stock reduces. In other words diversifying your investment opportunities helps to reduce the risk of your portfolio. Note however that this reduces only specific risk, i.e. risk that is specific to a particular investment.

Now ask yourself this question, “Why do seemingly diversified portfolios (benchmarks such as the S&P 500 index) still incur losses of such magnitude as was the case with the Financial Crisis of 2008?”

The answer lies with non-diversifiable or systematic risk. There are a number of reasons why risk may be non-diversifiable. One reason is that all markets may be impacted by a single event, be it a man-made event such as a liquidity crisis or a natural disaster such as a tsunami. These events tend to break down historical correlations and cause markets to react in a similar way across sectors and industries, thus reducing any previous advantages of diversification that holding a varied securities portfolio entailed.

But there are other sources of systemic risk. Due to purchasing power, mobility etc, an investor may only have access to a particular market and therefore to the instruments in that given market. If the market is constrained as to the number of securities (including range across sector or industry) and investment opportunities, the investor will have limited choices for diversification.

Other reasons include the cost of administering and rebalancing a portfolio and the cost of researching new industries and sectors to invest in versus the benefit of reusing information. These act as disincentives for diversification.

Arbitrage

Defined simply, arbitrage is profit without risk and investment, i.e, a free lunch on the back of an ignorant market. Most of financial theory is based on the assumption that arbitrage is not possible in an efficient market. Empirically speaking, instances of exploiting arbitrage opportunities have existed in the past and will continue to come up in the future as savvy investors take advantage of pricing disparities. However these advantages are usually short lived and of questionable magnitude as market forces quickly act to remove any existing discrepancies. Arbitrage-free assumption is crucial for Arbitrage Pricing Theory as well as the influential Black Scholes Framework which covers valuation of derivatives.

Market Efficiency

The basic idea is that markets are efficient. After all why would anyone trust the market with their hard earned money if it was not? Efficiency is displayed in markets prices of securities confiscating or assimilating all appropriately defined information immediately and fully. This is achieved through competition among investors and the resultant exchanges of information between them.

There are three types of market efficiency. Weak form efficiency is when prices assimilate all historical information. This implies that tomorrow’s price may be forecasted by using the entire spectrum of historical prices or alternatively just today’s price (which already includes the information of all past prices). Mathematically, predicting tomorrow’s price is a Markov Process where an event is predicted using its present state (today’s price) alone.

Semi strong form efficient markets are markets where the current price reflects all publically available information. Strong form efficient markets are markets where prices reflect all information; even those known only to insiders. Most economists rule out strong form markets to be realistic preferring to use the weaker versions in forecasting future prices.

Market completeness

A complete market is a market where the price of any new security may be valued using the prices of those already existing in the market, such as a derivative product that can be dynamically replicated via cash and the underlying asset. Replicated means any desired profile can be achieved through available securities without friction. Although real world markets are far from complete, the conceptual beauty of a complete market is that it can give individuals total freedom to design portfolios with desired payoffs in varying economic states, subject to only their purchasing power limitations.

Note that in reality the innovation we see in financial markets is a response to the lack of market completeness as financial investors devise ways to work in a world where expectations regarding payoffs and returns exceed the range of securities available to play with.

Asymmetric Information

Economic transactions involve different people with different sets of information. Statements like “the members of the stock exchange run the market, the market does not run them” illustrate the general sentiment that those with access to privileged information exploit opportunities created by such knowledge. On the other hand it is also expected that those uninformed, know that they are uninformed and anticipate their asymmetric information handicap by acting in a manner that reflects how they foresee those better informed will behave. It is this asymmetry that causes market inconsistencies and irrationality to impact the collective behavior of the market. A good example is a bank run. In a name crisis for instance people anticipate that those with access to insider information will withdraw their money and in response will begin withdrawing their own funds. This often times irrational behavior quickly goes out of control leading to the failure or bankruptcy of many banks.

Time consistency: Renegotiation proof and Adverse Selection

An important part of financial products is that the contract terms, caveats or conditions within them will be upheld by both parties to a deal over the period of the contract. The assumption is one of time consistency- that the contract will be renegotiation proof and will not allow for adverse selection.

When contract terms are weak, any party may exploit the terms to their advantage and to the detriment of the other parties to the deal. A borrower for example may default on a payment and then rather than be considered a defaulter, the lender may be forced because of weak contract terms to restructure the deal whenever this happens to enable the borrower to be considered active. Such a leeway could provide an incentive to borrowers to continue to be irresponsible about their commitments to lenders. The lenders in turn would have to suffer a higher cost of restructure and renegotiating the deal.

In the insurance industry weak screening processes and badly conceived contracts may lead to adverse selection. For example, if there is no effective screening process for smokers then smokers may be provided standard (non-smoker) rates despite the increased risk. The resulting higher incidence of deaths among the insured population will be greater than the mortality assumption and related cost factored in the insurance premiums exposing the insurance company to greater mortality risk than expected.

Moral Hazard

I vaguely remember a crime story in which the husband killed his wife by maliciously tampering with the iron and she died due to the electrocution. In the end, when police were able to extract the truth from him, he said he had committed this heinous act to receive the insurance money on her wife’s life. Talk about stacking the odds.

This is an example of moral hazard but note that it is an extreme one. In most cases these events may not even be illegal. A moral hazard occurs when the interest of the principal for example an employer, bank or an insurance company does not match with that of the agent for example an employee, investor or policyholder and causes conflicts, such as an agent making the principal cover a false claim on insurance due to ineffective underwriting processes. Therefore in setting up contracts, the principal has to account for the possibility of agents gaming the system and include terms and conditions to prevent or reduce these occurrences from happening.

Nash Equilibrium

Nash Equilibrium occurs when two players or more in the game, knowing each other’s possible moves, enter into a situation which is optimal for each and there is no motivation for them to change positions. It is the collective resolution of individual actions. The motivation or belief is that players acting in collective interest produce better results as compared to the outcome if players acted alone. An example is an oligopoly where a few dominant firms rule the market. Real world instances include oil and gold companies. For instance, OPEC oversees decisions in the oil sector, routinely making decisions like production quota allocations to each member country instead of letting them compete in the open market. OPEC countries therefore can control supply and hence oil prices often to the monetary advantage of each member.

Bayes Rule

Bayes rule is composed of three components; prior beliefs that a person already may have regarding a certain random event before the arrival of new data or information, the likelihood of various outcomes based on observation and posterior perceptions which are a result of the combination of prior beliefs, existing data and observed outcomes about a random event. The rule indicates how a rational person would react to new information- reassessing previously held expectations based on revised circumstances and conditions.

For more information on these topics we recommend Greenbaum and Thakor’s “Contemporary Financial Intermediation“.

Convertible Preferred Shares

Issuing new equity is an expensive source of capital for an entity. According to the Australian Prudential regulation authority, banks and insurance companies have devised a way to increase funding that qualifies as equity but at a lower cost of capital than equity. Convertible Preferred Shares (CPS) pays an interest rate which tracks the price of debt instruments and is converted to ordinary shares after a set period of time.

However, in order for the CPS to qualify as Residual Tier 1 capital they must have the following characteristics:

• Non-cumulative dividends- i.e. if not paid at designated time then they will not be paid in the future
• Dividend payments are based on the entity’s discretion and whether or not the issuing entity passes a profitability test and a minimum capital requirements test.
• Will convert forcibly to ordinary shares in the event that the entity needs to raise capital when a non-viability trigger event occurs. This will usually happen when the entity’s share price is depressed leading to large losses for the investor.

Analysts believe that this exposes CPS investors to risk (pricing risk because on conversion converts to ordinary shares not cash) for which they are inadequately compensated. They feel that a position in a CPS is at least as risky as purchasing ordinary shares of the company but investors will receive a return less than the return on equity.

These shares tend to rank just above ordinary shares where in the event of a winding up of the company the investors would be paid after subordinated unsecured note-holders and preferred and secured debtors have been paid but before ordinary share holders.

More expensive hedge fund trading strategies

In the US the impact of the BASEL III regulatory changes will most probably lead to an increased cost of trading. Brokerages have already stated that they will pass any increase in their costs due to the implementation of Basel III rules through to their hedge fund clients in the form of higher trading and financing costs. These are likely to increase the cost for plain vanilla strategies such as trading equities (which are fairly liquid) as well as for more complex strategies that trade relatively illiquid securities such as mortgage backed securities. It is also feared that the latter may be priced to such an extent that it could reverse any recovery in MBS markets and subsequently also hamper the recovery in the property markets.

The fear is that these new liquidity and capital requirements would by increasing the cost of trading further hamper and slow trading which could exacerbate the liquidity situation of the market. Hard to value niche global markets could shut down as they become more and more expensive to finance in the market.

Increased pace of infrastructure funds trades

In the UK, banks and insurance companies have been reducing their investments in infrastructure because under the regulatory capital changes of Basel III and Solvency II, these investments will be more expensive to maintain on their books requiring a higher level of minimum regulatory capital.

The increased supply in the market of these investments has been absorbed by Pension funds who have a need for long term inflation protected return assets, as well as asset managers who have clients with long term liabilities (such as insurance companies who will be able to bear the higher cost of holding such assets), large fund of funds, private equity funds, etc. So much so that demand for direct investment in infrastructure is on the rise.

The rate at which these infrastructure funds transactions are occurring is beyond what is usually witnessed in these normally highly illiquid markets. Recent sales have cumulatively exceed over 300 million pounds with more such transactions in the pipeline.

Divestment of financial institution shares

The Basel III changes to the capital rules discourage banks and financial institutions from holding large investments in other banks/ financial institutions. As a result we have recently witnessed Citigroup cut its investments in the Turkish bank, Akbank from 20% to 10%.

Increased repo volume and Shadow banking risk

Stricter bank regulation without accompanying regulation for market-based financing could increase the volume of trades, particular high risk activity, in the repo market and hence encourage a greater involvement of the shadow banking sector (structured investment vehicles, money market mutual funds, hedge funds) in these market, usually the lenders in these repo deals.

This would be the result of banks being assessed higher capital charges for holding or trading riskier securities. On the other hand as it currently stands, the shadow banking industry would not be impacted by these regulatory regime changes. There would be an incentive therefore to shift higher risk (and hence higher reward) trading activity from the banking sector to the not as strictly regulated shadow banking sector thus defeating the purpose of regulators to discourage excessive risk taking in the financial sector.

Analysts fear that a great involvement of the shadow banks in these high risk markets could lead to a potential increase in liquidity risk as witnessed during the US credit crisis. In the crisis repo markets played their part in the increased price volatility seen for illiquid assets and securities which were no longer eligible to be posted as collateral as well as the funding problems of dealers with reduced access to the market.

References:

IAG’s CPS: Rotten tomatoes with Basel (Nathan Bell – March 26, 2012)

Investors have issues with hybrid shares (Eric Johnston – March 28,2012)

Hedge funds face higher trading costs (Sam Jones – March 26, 2012)

Demand for infrastructure takes off (Kiel Porter and Mark Cobley – March 26, 2012)

Akbank says Citi’s stake trim driven by Basel III (Simon Cameron-Moore- March 24, 2012)

Basel III Measures May Heighten Repo Volume, Risks, Fitch (Liz Capo McCormick – March 19, 2012)

Last week we ran another detailed customer persona exercise for the Starting up class I teach at SP Jain. Here is the output from the class session.

The primary question posed to the group was:

What does a typical North American MBA student look like at a top 10, top 20 business school? How would you start building a customer profile for business school students with this profile? How would you sell 3 different knowledge products to them?

We started off with a day in the life of my customer technique.

A day in the life of a business school student?

Up between 5 and 8 am: Goes to sleep around midnight.

First and last things done during the day: Prep, review, exercise, connect, catch up

Head to the first class that starts at 8:20.

May have 3 – 4 classes on a given day. Upto 18 hours of instructions in a given week.

Has about 6 planned work, networking or recruitment related sessions in a given day.

Review case, answer questions, print out submissions. Stop in the Library or Career Office. Head out to the break out room. Grab a drink (Water, Juice, Gatorade, Ice Tea or Coffee) between classes.

Decide where to grab a bite, attend a lunch networking session, sit with classmates, group mates or friends. Catch up on the world during the bite break, head back to classes.

Review cases due for analysis and discussion. Sit down with group to review submission or assignment

Look up jobs, speakers list for this week or next. Find out what is happening on campus.

Go for a break, jog, run, bite, drink after school or depending on the case load just drop.

Head back to room to do work, chill, relax or catch up and connect.

We then moved on to Sources of Stress at Business School?

Assignments, submissions, networking sessions, interviews, shortlists, career related stuff, second and third term tuition bills?

Single biggest source of stress? By Term

First Term: Grades

Second Term: Internship

Third Term: Full Time Offer

Fourth Term: Full Time Offer followed by risk of not performing and getting laid off.

Post Graduation: Student Loan

Then we used the demographic bucket

Business School Student: Age group

Between 25 and 35. With the higher ages spreading out over 5% or so. Most students are in the younger bracket with the most common age being 27 – 28 years old.

25 – 28 Age Bracket for MBA students

2- 4 years of professional work experience – in most cases a single function job with a one or two employers. Most likely banking or consulting. More likely to be single than married, US resident versus international. Focus on recruitment and placement. Very competitive but also concerned about social circles, standing and network. Using the business school to upgrade compensation and prospects.

28- 32 Age Bracket for MBA students

Married, sometime with kids. More likely to be international with a broader professional exposure.

More likely to use business school to switch careers and professions. Concerned about placement but have a few back up options. More concerned about acquiring the right skill set. If international most likely to head back home.

32 – 37 Age Bracket for MBA students

Not at business school to switch career or look for placement. Most focused on education & acquiring skill sets.

Short Profile by Age group for Business School students

So the final questions posed to the class and their assignment during this session were:

a) Designing three separate knowledge products for this group and selling them?

b) Identifying as many possible practical & addressable segments

How many segments can you really make from the above profiles? Which segment would you chose for your knowledge products? How do you know a segment is large enough or even profitable enough for you to begin work with it?

Here is some raw data as well as content and reading materials to help you along. More than 200,000 students take the GMAT exam every year. Business school students in North America produce over 120,000 Master in business graduates every year and about 300,000 undergraduates.