Prince William and Kate – Let the Market Decide

Apparently, Brits will bet on anything. I was just watching the BBC and there was a short clip on questions Brits were asking and what bookies were placing in terms of odds of various events happening at the wedding: Kate’s car breaking down, Prince Harry dropping the ring etc.

This got me to thinking about mathematically predicting the future. Not just in physical models (for instance forecasting weather) which is difficult enough, but also in these sort of abstract ideas. After all, how do you predict what is Prince Harry’s chance of dropping the ring? Let’s say you could even come up with a very convincing model that would give you one good guess.

Another method would be to let the invisible hand of the gambling market decide. Assuming you could get enough interested people to bid in the “market” on an individual question, having more or less people bid for or against any particular question can give you an indication as to whether the odds offered are too “generous” and allow you to recalibrate the odds directionally until you arrive at a relatively stable number.

For example, when asked: “What’s the chance of the Queen wearing a blue hat?” initially it’s even odds. However, people start doing their homework, statistical multi-factor regressions, etc and discover that she actually tends to wear a blue hat quite often (especially at royal weddings) and people starting overwhelmingly taking bets that she will wear a blue hat. If the bookie is paying attention, they will start moving the odds, maybe now offering 2:1 odds that she will wear a blue hat. People start fine tuning their model and continue to make bets until the odds rest at 3:1 (the current odds of the Queen wearing a blue hat). The bookie takes bets on either side taking the spread.

The invisible hand of the gambling market has determined the probability of the Queen wearing a blue hat. While we aren’t even sure what models were used, assuming intelligent people using real money have staked a “best guess” at what the appropriate value for the probability of the Queen wearing a blue hat is, we are given another robust answer.

Emerging Markets - China

We just completed our final presentation in the Emerging Markets class taught by Linda Yueh at LBS. Our group chose China, and we did a cross section of important business factors and how they affect investment decisions and mechanisms into the country. While the presentation was performed by myself and my classmate Tim, the other team members contributed greatly to the material and their preparation was reflective in the cohesive and comprehensive story we presented. It was easy to present the highly refined material to create an investment thesis branching across a broad range of industries and tell a story about how specific companies would benefit or be hindered by government regulations and financial market mechanisms.

Tim did a great job going into the details of the domestic business environment with some personal anecdotes from some of our team members’ recent trip to China. JEMBAs (aka January intake Executive MBAs) at LBS are required to a week intensive in a foreign country and three of our JEMBA team members visited China together.

I think we represented the hard work and analysis of our team well.

YCIF London – First Conference Call

Today the YCIF in London hosted its first conference call. The speaker was Mr. Carlos Leitao, Chief Economist and Strategist at Laurentian Bank of Canada. He gave some great insight into the outlook of the Canadian economy by providing some colour as to the major macro indicators and the story of what was going on behind the key performance metrics.

I'm looking forward to the next conference call which is slated for Wednesday of next week. The topic will be the European state of the economy for 2011.

Bid / Ask Curves

At the break in behavioural finance, I was speaking to a prop trader about the mechanics of the market. This reminded me of my short trading exercise in the Rotman Finance Lab with the Trading Simulation software.

In microeconomics, we discuss demand curves and how they are based on individuals with different levels of willingness to pay. So as the price increases due to a supply curve shift right (less supply), the quantity demanded decreases.

In markets, this is a little more transparent if you look at the bid / ask lists. These lists show the prices and volumes people are willing to buy and sell for. The other unique thing about the capital markets, is that there is actually a set number of securities (assuming that banks do not issue or buyback securities in the short term, supply is price inelastic based on total float) and that investors can be both buyers and sellers (short term suppliers and consumers) of securities. Actually, a better way to put it would be they can either hold or release securities (demand based relationship).

If their intrinsic value (IV) of a stock is above the current market price, they will buy the stock. If their IV is less than market, then they will sell. And in an exchange market, that is exactly the case (orders, unless removed before execution, are commitments to buy or sell at the stated price).

Shown here is an illustration of a “complete” market. This assumes that everyone’s IVs are included, that there are no hidden orders and that people’s opinions won’t change with the market price (a snapshot by nature). The current ask price is $8.00 and the bid is $7.75. As people’s sentiments change (or new investors are introduced into the market), orders to buy are satisfied at $8.00 and orders to sell are satisfied at $7.75. If all the potential sellers at $8.00 are taken up, then people can only buy the stock at $8.25 and the stock price goes up. Note that the differential between bid and ask is a proxy for market liquidity, as the lower the transaction fee to enter and exit a position the lower the cost of trading the security.

Also note that the steepness of the curve is a good proxy for potential volatility as well. Because if the slope of the curve is steep over a variety of prices, it means that the market doesn’t necessarily agree on the price. And if a few people cross the line from one side to another, the price can change quickly and dramatically (shown below):

Bridge to Value

One graph I've seen which I thought was clever was a breakdown of change in EV. This brings together many other details I've learned about M&A, LBO's and transactions in general.

Previously, I mentioned a framework for PE deal success, but it is easy to cut into more detail if necessary and really define and put a mathematical value to "synergies".

For example: After a transaction, we've increased sales by 21%. How does that affect EV? Well on one hand, you've immediately realized a 21% increase in revenue. After you account for associated costs with that increase in revenue (ie. You've sold more widgets, but it still costs you money to make those widgets), what do your future growth prospects look like as a result of this new growth (ie. Should you trade at a higher multiple? Have you gone from "boring" to "exciting"? Or is it just general market conditions?)

Previously, you had:

Market Cap = $100
Shares outstanding = 100
Price per share = $1

Debt = $100 (@ 5%)
Excess Cash = 0

EV = $200

Revenue - $100
COGS - $40
GPM = $60

Op Ex - $20
EBITDA = $40

DA - $10
EBIT = $30

Interest = $5

Tax = 40%

NOPAT = $18
NI = $15

Therefore:

EPS = 15 cents

P/E = ($1/$0.15) = 6.67x

EV/EBITDA = ($200/$40) = 5.00x

Let's tell a story: The 21% increase comes from opening a new line of products. You are selling 10% more products by introducing a new product line and this new product line actually increases your revenue per unit (across the board) by 10% (110% x 110% = 121%). All margins are the same.

What should we do? Bring everything down to the EBITDA level:

Now:
Revenue - $121
COGS - $44 (10% more products at same costs)

GPM = $77

Op Ex - $22

EBITDA = $55

DA - $10
EBIT = $47

Interest = $5

Tax = 40%

NOPAT = $28.20
NI = $25.20

EPS = 25.20c

(Magic happens - Which we will explain shortly)

New Price per share = $1.80

Market Cap = $1.80 x 100 shares = $180
Debt = $100
EV = $280

P/E = ($1.80) / ($0.2520) = 7.14x
EV/EBITDA = ($280 / $55) = 5.09x

Analysis:
So a lot is going on. The price of the equity and the enterprise has changed, but how can we do a cross section such that we know exactly where all the value is being driven from?

How much of this value is because of leverage (hint, we didn't change amount of leverage)?
How much of this value is simply because we are operationally better?
How much of this value is because we have a "brighter future" (better growth prospects)?

Step 1: Value from leverage arbitrage:
No change = 0

Step 2: Value from "synergies":
Total EBITDA level changes: $40 to $55 or $15
At a multiple of 5.00x (previous multiple), value increased is $75

Step 3: Value from "Brighter future"
Brighter future (higher multiple) due to either market conditions or expected future growth:
$55 at 5.00x versus at 5.09x = $55 x (5.09 - 5.00x) = $5

Total value created: $75 + $5 = $80 (note total increase in value of EV / Market Cap)

Next step, look closer at Step 2:
Change of $40 to $55 is created by:
$21 in Revenue (Price +10%, Volume +10%)
$4 in COGS (Volume + 10%)
$2 in Opex (Volume +10%)

For a $21 increase in revenue, keeping margins constant we would have expected an increase of:
$8.4 in COGS (40% of revenue) and $4.2 in Opex (20% of revenue). COGS is lower by $4.4 and Opex is lower by $2.2 versus what is expected.

Note we mentioned we can sell products for 10% more across the board.
This created value for existing product base (at EBITDA level) of
$110 - $40 - $20 or $50 versus $40 creating $10 of additional EBITDA level value (makes sense, increase topline growth by 10% without changing expenses / sales volumes results in increase of EBITDA by 10% of revenue)

Also, selling an additional 10% at old price we would expect:
$10 (additional sales) - COGS ($4) - Opex ($2) or $4

But selling new products at new price: Gain $1 (similar to math shown above)

Total change in EBITDA: $10 + $4 + $1 = $15
At 5.00x
$55 or ($10 + $1) x 5.00x of EV is generated from selling at a higher price
$20 ($4 x 5.00x) of EV is generated from selling new products (higher volume)

Above is what the bridge would look like if a PE firm had 60% ownership and management had 40%.

Note, this framework is iterative and can be applied across multiple product lines to help do a break out and sum of the parts analysis for companies to see where value is hidden in undervalued divisions.

Also note that as an interesting aside, if you were actually to build out a proper DCF model of this (using some basic business assumptions holding margins constant etc.), your short term growth rate would have to be adjusted upwards in order to come to the same intrinsic valuation that would justify the higher multiple.

Flight of Fancy - Market Price for Courses

So it turns out I’m not entirely crazy.

Obviously, one of the benefits of going on exchange is to meet new people and learn from new experiences. Previously, I had posted on a flight of fancy, where I wondered what would happen if we extended the idea of our bidding system at Rotman to the “next level”. Well it turns out that elective bidding at some other schools can be slightly more complicated.

Talking to some students at other MBA schools, they have more “progressive” (market driven, while not entirely "pure") bidding systems for courses, where you can actually buy and sell courses for a profit (expressed as a gain in “points”) where you bid for classes early and then “sell” them at a later date when their point value has increased.

The only short coming of their system (from a market perspective) is that there is only one form of “liquidity”. The only way to truly “exit” the market? Take a course. There is no other way to liquidate these points. This could easily be solved if you could simply transfer the points to another student (a secondary market would develop and even create an OTC market price in $$$'s for course points).

And then you would have a market answer for the question: “How much is the margin worth to take the elective course you want?”

Islamic Finance – Executive 3 Day Program

Today, I presented remotely from London at the Islamic Finance Program, hosted at Rotman for the Executive Program in partnership with Bennett Jones. This was the final component of our Islamic Finance ICP. The team lead by Walid included myself, Arash, Shahzad, Khaled, Kashif and Noureen and we presented our materials to the executive program participants.

With the rise of Islamic finance, I’ve had some interesting conversations with people I’ve met while on exchange at LBS regarding Sharia compliant financial structures such as the various types of sukuks we’ve been analyzing.

Our spread analysis drove quite a bit of interest for people who were interested in what a Sharia compliant structure would yield and the market conditions for doing such an issuance. Even before our presentation, I had the opportunity to answer some basic questions regarding the pricing spread between Islamic instruments versus conventional.

Back from the Holiday

Well after a well deserved break, all the students are back.

First years are in their Negotiations class which odd for me as I didn’t do this last year due to the Middle East study tour, but now I see what it was like with the Atrium constantly being flooded by ambitious MBA students trying to get better deals in their exercises. There have also been requests for help with preparing for recruitment week which is coming up next week and some postings already up.

Even second years are at school, many having the clever idea of taking an intensive or two to lighten their final term course load.

Yesterday, we did a presentation for our ICP in Islamic Finance. Arash and I did a presentation on two comparable securities, one conventional and one Islamic and we showed they were strategically and operationally comparable (same industry, business model, enterprise value, capital structure, debt ladder, similar maturity, seniority and economic conditions, but different country and terms) and we analyzed the yield, adjusting for country risk and broke down the spread accounting for liquidity risk, minor maturity differences and increased cost of capital related to Sharia compliant terms.

This material will be used as part of Rotman’s new Executive MBA program class on Islamic Finance. While we aren’t quite finished with our work, the next step being to propose a term sheet for what the conventional financing would look like if it were Sharia compliant, I’m very happy with our progress and the insight we were able to bring into this new product class.

Flight of Fancy: What If...? A Market for Bid Points

One common theme I've heard is that MBA's are often upset when they don't get all the elective courses that they want. While I certainly can't complain, it brings up an interesting question: "What if someone like me was able to sell their bid points? What would I get for them? And how would you value them?"

For example, my course choices weren’t very restrictive, I got 500 points to bid on four courses, most of which I could have gotten with a zero bid. Whereas, Mr(s). Ambitious was trying to take TMP and Value Investing while going on Exchange (physically impossible, Value Investing is a year long course and Exchange means you are physically gone). If there existed a mechanism (and therefore a market) for me to transfer my points for a price, what would I get for them? What should they be worth? Clearly, there is currently some "market inefficiency" as we are both unsatisfied: Mr(s). Ambitious because they didn't get all the courses they wanted [net deficiency] and me because I didn't realize the full value of my bid points because I had more than I could use - [net surplus].

Well let’s make some assumptions:

• Rotman tuition is C$35k per year (let’s not include first year as it’s common, or you can adjust the value of points accordingly if you feel second year courses are more / less important)
• You take 10 elective courses in your second year
• You are given 1000 points with which to bid

A “book value” of the points would simply be C$35k / 1000 points or about $35 per point.

But keep in mind that when something is inherently useful, especially in a scenario where a few points margin can mean the difference between getting the course you really want versus having to settle for a less popular course, there can potentially be bidding wars from “oversubscription” (points trade at a multiple above their book value) especially if they were in limited supply.

While people are paying C$70+k to go to school, for a marginal $35 x 100 points (a rough approximation of the average points allocated per student / course) or $3500 you can get any course you want (including the highly coveted TMP and Value Investing – which includes a trip to visit Warren Buffet – one of the reasons why this course is so wildly popular).

If you could some how do it, you could see how much additional probability you have of getting into the classes you wanted and put a dollar value on how badly you wanted to be in that class (regression analysis), you can determine a price you’d be willing to pay to attend that class. For example: Would there be a correlation between the number of points you consumed to get into classes of your choice against your overall earning power once out of university (thinking along the lines of DCF to value bid points like common shares).

And also imagine if this market had a “market maker”. For example, the PSO will (create and) sell you points for a certain value (either regulated and pre-determined or floating with the market). Students could liquidate their points at market value and get money back or buy points of the market to be more competitive for course selection and the school could potentially get revenue from selling points.

And since you have a market with underlying assets, imagine if you created financial instruments for those assets (shorts, puts, and calls for bid points, futures).

And imagine if other schools had market systems (I’m told that bidding systems are not uncommon at other MBA schools), you could trade between these. Or even other programs!

Of course, these points would inherently have an “expiry” as to their value (you wouldn’t want to be holding (take delivery of) 5000 MIT Engineering points if you were going to Stanford Law School).

There are some interesting implications. For instance, a new ranking system for schools where the relative value of a course is determined by the market value (determined by students taking courses there) in real time with comparisons to year over year values. Example: Would an engineering calculus class go for more at Waterloo or Toronto? Could you couple this with flexibility between schools (accreditation programs) which allow students to take equivalent courses at other schools and what do you get?

It would be a more sophisticated and real-time version of tuition regulated by the market. Taken to the extreme, here is another idea: drop the original tuition completely and have students buy bid points for classes. And then what if you were able to connect this market to actual financial markets? An S&P Index of Undergraduate studies to benchmark the valuation of your individual class’ performance.

Another thought: If the value of courses in a particular faculty started to "overheat" would that be a leading indicator of oversupply of labour in a particular industry in 4 years time?

Bidding Strategy - The Mechanics

So I've been lucky enough to receive all the classes I want in all the sections I want and it turns out that LBS doesn't use a "bidding" system per say (classes awarded based on listed "preference" - an ordinal system).

A few people were asking about how my bidding formula works and while it's hardly perfect, I figured I'd put up some of the details just for laughs (or a least as building blocks for someone who plans on taking this model to the next level). It uses only public information available to all students at the time of bidding.

In this model, each course bid is determined by three factors. The first is the inital base and most people will choose one of two initial bases: Last year's minimum bid or last year's median bid (depending on how competitive the class is).

After determining the appropriate bases for your five courses, the remaining points (“the Remainder”) can be divided amongst your courses to make your bids more competitive. But like all dilemmas in bidding, you want to assign just enough points so that you get the courses you want, but not so much that you jeopardize your chances of getting the other courses. So how do you do it?

I propose that the two major factors you should look at are what I call:

1. The Ballot factor (anticipated) (x% of the Remainder, or “X-Factor”)
2. The Historic factor (backward-looking) ([100% - x%] of the Remainder, or the “Y-Factor”)

Where x% is the weight of value of your Ballot factor versus your Historical factor (In other words: how much you believe your Ballot Factor represents real bidding behaviour versus historical).

Ballot Factor:

This factor accounts for the number of people who say they will take the course. A few notes:

• People don’t always bid for the courses they ballot for
• Use the numbers as guidance to see if the course is oversubscribed
• Calculate the expected utilization capacity = total number of students balloting for any course in that section / total class capacity
• Square the utilization capacity to create an “intensity factor”
• Total all the factors and express each factor as a percentage of the total
• Multiply the percentages by the X-Factor
• The result is each individual courses’ Ballot Factor offset

Example:

• 2 classes have a capacity of 40 people each
• You have 200 points allocated to Ballot Factor
• 20 people bid on Class A (fairly certain everyone who bids will get in… There is even a chance that a 0 point bid could win) has utilization 50% and Ballot “intensity factor” of .25
• Class B has 60 bidders has utilization 150% (red flag: guarantee that not everyone will get in) and it’s “intensity factor” is 2.25.
• Class A’s weight is .25/(.25+2.25) = 10%
• Class B’s weight is 2.25 /(.25+2.25) = 90%
• Class A’s Ballot factor offset is 10% * 200 points = 20 points (a non-zero bid with decent margin, you'll probably get in)
• Class B’s Ballot factor offset is 90% * 200 points = 180 points (a strong bid, considering an average of 100)

This model tries to account for the fact that only very high bids will win the competative class, but you also don't want to low ball Class A incase a few stray bids appear from people who take the class last minute (obviously, the less people who originally bid on the class, the less you have to worry about dark horse bidders).

Note that it is 9x because at least 20 people are guaranteed to not get in the class. Classes that are oversubscribed will have intensity factors much higher than 1 with much heavier weights and undersubscribed much lower than 1 with much lower weights. This accounts for the premium on variation and intensity due to the number of bids in a competitive environment. Note that in this pure form, this is a best effort bidding mechanism with the scaling of points to consume all remaining points.

Historical Factor:

Another way to try to guess what the bidding will look like is to use the historical bidding as guidance for the variation of bids (were the bids tight or across a broad range?) One indicator of that is the minimum and median bid. If you make some HUGE assumptions, you can use these two points to create a normal curve with standard deviations. Since the mechanics of this are taught in stats in first quarter, I won’t bore my readers with a poor facsimile of Prof. Krass’ lecture.

Even if you don’t technically know the actual distribution of the curve, you can also use Chebyshev's inequality to position yourself within a certain percentile (also looking at the expected capacity utilization of the class based on your previous calculations). How? Here’s a hint (shown above): the bidding percentiles (% of students bidding that are not successful being admitted into the class) should be the same as the bid oversubscription capacity (again, huge assumptions) to provide the number of standard deviations. Combine this fact with the distance from the median to the minimum should provide a clue as to size of a standard deviation. Note that using this method, you may not (probably won't) have enough points to guarantee getting into the courses you want (unless like me, you probably have a surplus of points or are taking unpopular courses), but it is probably one of the best mechanical methods for balancing aggresive bidding with conserving points as well as building a view for what the bidding landscape looks like. In practical terms, at this point you can use a best effort model similar to the one shown above using the Y-Factor.

Also, I’ve deliberately left out methodology for mechanically scaling up courses based on your individual preferences (ie rating courses from 1 to 10 and incorporating that into your bidding strategy). Also, there are huge economic implications for bidding strategy considering that the involved parties do communicate with each other and affect the bidding levels of courses (ie Friends talk to each other about how they plan to bid). Signalling, game theory and strategy all come into play.

While not perfect, this model will give you some perspective into what a reasonable, very mechanically inclined bid would be. Admittedly, while I built this model, I did do some “emotional” adjustments to my bids (there was one course where I wanted to work with my friends on their team, so I wanted to be CERTAIN that I got the course). Like anything done on a computer, it’s just a tool.

Disclaimer: Like anything on this blog, this model does not guarantee any degree of success. This post is intended as a conversation / pensive reflection piece only. It is possible for you to use this model and not get ANY courses you want. For instance, it is physically impossible to get both Top Management Perspective AND Value Investing because both courses usually require exceptionally high bids. Note that by definition, there will be some people who don't get the courses they want. The more you want to be certain that you are in one course, the less certain that you will be in another (almost like the Heisenberg uncertainty principle). For better or worse, it is a zero-sum game.

Also, more importantly, I've been told that it's all a wash and at the end of the day, after the drop and add periods are over, most people get the courses they want anyways.

Ethanol Fuel – Is Sustainable Fuel Socially Responsible?

In our Business Ethics class, I had done a presentation on the Ethics of Ethanol Fuel and now that I am in Brazil, the topic of ethanol fuel and its relationship to sugar cane and other staple food commodities has been emphasized. And even if global trade increases and creates wealth it does not make any guarantees with regards to how that wealth is distributed as explained by our GMP Prof. Blum.

I proposed this question: “Is it ethical for rich countries to drive cars if it causes poor countries to starve?”

This problem was exacerbated in the World Food crisis in 2007 and 2008, when oil prices hit all time highs and the arbitrage relationship between oil and food was exploited.

Here is the relationship:

1. The US and Brazil are the largest producers (89%) of ethanol fuel using corn and sugar cane (respectively).
2. When oil prices increase, people have a tendency to switch to ethanol based fuels.
3. When the demand for ethanol increases (as a substitute) the demand on the inputs for ethanol (corn, sugar cane, potatoes) also increases.
4. There are real arbitrage opportunities by hedgers, speculators and even farmers as they shift the use of arable land to produce more valuable crops. However, even with arbitrage there are limits as arable land is limited (to create more arable land, there is often deforestation which creates its own sustainability issues).
5. Also poor countries have a much higher sensitivity to fluctuations in the price of raw commodities whereas rich countries are insulated from food costs because they only represent a small percentage of the final costs (including value added costs such as manufacturing, distribution and retail costs). It’s the difference between eating a bowl of rice versus a bowl of Rice Krispies.

The consultants at Deloitte confirmed this relationship in very concrete terms using the Brazilian gas pumps as an example (where prices for ethanol based fuel versus gasoline are explicitly posted in the same way gas prices are at the pump). Currently, the cost of Ethanol fuel is 50% the price of gasoline (although it only provides 70% of the performance). However, in the “off season” when sugar cane is more expensive, it can even reach a price of 80% of gasoline (which is clearly not as cost effective). There is a direct relationship between the price of sugar cane and the price of ethanol fuel.

Valuing Commodities Companies - Looking at P/NAV

Recently, I've been trying to learn more about commodities companies building on what we've learned in class as well as discussions with colleagues. Especially as Canada is a strong resource based economy (with a currency heavily influenced by the price of oil, I'm told), it is important to understand how these companies are valued.

Shree was previously very kind to explain why commodities companies provide leveraged exposure to the underlying commodity. In fact, I'm told that this is the reason why companies trade a P/NAV multiples greater than 1.

When I inquired as to what exactly Net Asset Value (NAV) was composed of, I was told that it is essentially the Asset Value (value of the commodity "in the ground") netted by the cost it took to get that asset out of the ground. So if you had to take a snap shot of what that company was worth, you'd intuitively assume that the value of the company was it's NAV.

However, as Shree demonstrated, the markets are always moving and the price of the commodity which the company bases its value on will change. This produces option like behaviour in the price of the company. While not a perfect explaination, I was told to think of it this way:

The value of the company is related to it's NAV PLUS a premium associated with the volatility of the commodity and the probability that the price of that commodity will increase. This is analogous to Intrinsic Value (NAV) + Time Value of a call option.

Obviously, there are a host of complicated relationships related to volatility, future price expectations, supply and demand, hedging and speculation which make this basic generalization a little too simple. However, I think it serves as a good starting point for how to think about and model the price.

This is similar to what we learned in Finance II when our professor explained the example of land that contained 1M barrles of oil which could be extracted at a price of $70 per barrel when the market price of oil was $60 per barrel. While a naive NPV calculation at today's rates would imply a negative NPV, the potential for the price of oil to top $70 provides real value to the land.

Purchasing Power Parity

Another example of exchange rate theory is the Purchasing Power Parity (PPP) condition. This model is similar to IRP in that it assumes that when purchasing commodities across borders that the same real price should be used regardless of currency.

For example.

• A widget costs $150 USD in the US
• The same widget costs £100 in the UK

What is the implicit FX rate between US dollars and pounds?

Well, you should be able to buy the same widget with either $150 USD or £100, so the implicit exchange rate (assuming PPP holds) is:

= $150 / £100

= $1.5 / £

Obviously, there are some HUGE assumptions required for this theory to hold. Minimal or zero transaction costs (including transportation, cross border tarrifs etc). In practice, it is probably more realistic to say that there is a threshold for which arbitrage probably won't happen in PPP because of the real costs incurred to handle transactions.

Also to be more accurate, rather than just use a "widget" it would be more appropriate to use a basket of goods to reflect a more broad use of the currency.

Interest Rate Parity Condition

The Interest Rate Parity condition that is in the CFA and discussed in our Global Managerial Perspective (GMP) class talks about.

Long story short, it says: Regardless of what financial mechanisms are used, two countries which are considered to be default free should generate the same real returns for the same period.

For example:
Today:

• You hold $1 USD
• The FX rate is 100 yen per USD
• The Japanse Bonds are yielding 5%

A year from now:

• FX rate is expected to be 103 yen per USD

What does IRP imply the interest rate on the US bond should be?

This can be graphically represented by:


The blue path shows how $1 USD is convered to Japanese Yen, held in a bond, and converted back at the new exchange rate back into USD. IRP states that whether this route is taken or if the USD is just held in a US bond (Red path) should make no difference. It should result in the same amount otherwise there is an arbitrage opportunity.

This is the solution. Note that the US bond rate is reverse engineered from the information given such that the end result produced in the red path is the same as the blue path.

You also note that there is a relationship which is defined by IRP. That is:

Japanese Bond Rate = US Bond Rate + [Appreciation / Depreciation of Foreign Exchange Rate]

Notice that this framework can have a blank in any one cell which can be derived using algebra if the other cells are filled in.

George Soros - Breaking the Bank

We were discussing the notorious story of George Soros' shorting of the UK pound in our GMP class this morning.

The story begins with the UK joining Europe's Exchange Rate Mechanism (ERM), not exactly fixed but had a policy where the currencies were staying within an exchange rate band. This simultaneously existed with a carry trade scenario where the German government was offering a higher interest rate than the British Government so people were borrowing in pounds and lending in DMs.

George Soros foresaw the opportunity where people who were participating in carry trades with the British pound created an opportunity for currency deprectiation. He sold off all his positions and then proceeded to short the position. This aggressive "attack" position resulted in many other fund managers dumping pound denominated assets. On the "Black Wednesday", the Bank of England tried to fight back by raising interest rates up to as high as 15% that day, but it wasn't enough.

Also, this is compounded by the required draw of the UK's foreign reserves (another avenue to defend against currency depretiation) to prop up the currency would have resulted in a significant depletion which would not have benefited nor saved the currency (essentially paying out the speculators).

This reminds me of the scenario I experienced in the finance trading lab where I could see the ask list depleting very quickly (low number of orders). The Interest Rate Parity condition only holds if you have a player who is large enough to hold the position of the currency. In a scenario (often repeated in other markets) where people put a currency (or any financial instrument) under siege, it makes it difficult for players to hold their positions as they take massive losses.

Eventually, as the story concludes, the epilogue is that the UK bank decided to let go and allow the currency to depreciate. George Soros also made a reported $1B USD.

Exchange Rates - Currencies as Investment Instruments

Oddly, in my Global Managerial Perspective (an international economics course), we were discussing exchange rates and the professor brought up nominal and real exchange rates (nothing new really), but also introduced the idea of using a weighted average of currency exchange rates as a bench mark for appreciation. There are two neat ideas which I took away from this:

The first is that when currencies appreciate relative to one another (on a bi-lateral basis) it is often hard to tell what is happening. For example, the Canadian dollar flirting with parity to the US dollar in the last few years. Is this a result of the US recession and lack of confidence in their dollar? Or the fact that Canadian Exports are in high demand and driving up our dollar value? Or both? In putting together a weighted average of exchange rates against your currency, you can tell on a more clear individual basis if your currency is appreciating against your "basket", a proxy for global currencies and real Purchasing Power Parity growth.

The second idea is that this weighted average looks an awful lot like an index. Much of the language above encompasses the idea that it behaves like a portfolio of financial instruments. Having said that, I recognize that things like CAPM probably wouldn't work (considering that currencies are not return generating instrumentns) so a regression of gains over time from the currency to the "index" would probably be meaningless. In the same way that commodities (although they are assets - items that store value) are not investment assets in the same way other financial instruments are because they don't generate return.

As the CFA material mentions, the only real way to receive gains from non-income generating instruments (commodities and currencies) is to rebalance after a change in price of the underlying asset.

The Acumen

I've been exceptionally busy the last few weeks. Even though I've decided to place a moratorium on competitions, I’ve still found myself overwhelmed with the MBA and the CFA Level II material. One thing I’ve been trying to keep track of in my posts is the absolutely brilliant ideas I’m encountering, whether in the Rotman MBA or CFA Level II.

But lately, my ability to post and get work done has drastically dropped off. And I would like to think it’s not because I’ve gotten lazy, but rather because there is so much brilliant and interesting stuff going on that I can’t post it all.

And I think that is exactly the case. Example? ITP, a course that had many of us scratching our heads in the beginning has suddenly burst forth with ‘light bulbs’. For example, not too long ago, I was complaining about how “past performance does [NOT] predict future behaviour” in some cases but not others. For example, individuals (as social science will tell you) are predictable (past performance DOES predict future behavior) but this is not so in finance and capital market instruments (good luck buying a stock based on it’s past behaviour hoping for gains).

And ITP has taken this exact point and, in our latest team assignment, asked us to investigate why this is. How “predictable” individuals (with their behaviour well defined) in a group produce slightly unexpected results. And the effect this has on our perception of what we learn in finance classes as it relates to theoretical concepts (CAPM comes to mind at the top of this list, but is closely accompanied by the idea of bubbles and crashes in the stock market, a very timely topic).

You can imagine that I am very excited. Suddenly, I am being given the vocabulary to describe in practical terms what we’ve been taught in textbooks. One of the reasons why I came to the MBA program was to help me structure my thoughts so that I could package them and deliver them to someone in a more digestible format.

While I was fortunate to have known many brilliant minds at McMaster Engineering, in my work life, I felt like I wanted to further develop my business skills to speak the same language as my colleagues and be able to use descriptors outside of the Engineering dictionary so we could understand each other, work together and accomplish amazing things. It was one of my goals coming into Rotman: I thought I was pretty clever. But I wanted to develop my business acumen.

Getting a Job in Asset Management

Previously, I was asked to write a post about getting a job in the Asset Management industry. While I myself am not that well versed on the buy side, I interviewed and got advice from other people who know more than I and were successful in getting interviews / offers. This is of course, beyond what is expected in any capital markets job, and this is what they had to say:

Application Materials

As always, you have to get your initial application materials in order with the hope of getting that first round interview. At the application stage, asset management companies have been known to ask for typical materials such as resume, cover letter and transcript as well as:

• Writing samples
• Sample stock pitches

Networking

As always networking is an important part of getting that first interview. Before you start networking, you should already be very polished, particularly on the topics of:

• Their fund strategy and your style / fit
• Know their holdings and weightings
• Diversification - know the focus of the fund

To get this information:

• Hedge funds tend to be proprietary and it will be more challenging to get info
• Mutual funds holdings are generally public

Either way, go go onto Capital IQ / Bloomberg and find out as much as possible. Read manager's letter about their performance and strategy. Know their best performing success stories as well as their dog holdings. Know where their exposures are and what strategies they are using.

If you impress them at the outset with a networking or informational interview, that improves your chances of getting that first round interview.

Interview

For the interview, they will probably ask you a few fit questions. I've been told buyside will not ask you too much technical stuff on valuation (i.e. walk me through a DCF) because these are expected. If your resume doesn't show some experience in capital markets, I'm told you have a much lower chance of being interviewed.

Have a view of the market and ensure congruency in your view. A great piece of advice was to really understand the "off balance sheet items". The reasoning for this is because these are the items that are more difficult to value and provide you a potential differentiation advantage. If you are able to better interpret this information, this is your potential competitive advantage in the market.

Also, the "stock pitch" component of this interview is generally more intense than in other finance jobs. I've heard it described as the interview was just "10 stock pitches". Having said that, while most finance interviews usually require 2 longs and a short, it's been suggested that you have a mix of 10 long and short positions, with a mix of long and short companies and industries. Also, you have to make sure that there is congruency in your view as well as your story.

For instance, one example from an interview was: "I see that you've recommended this stock because you think the industry is strong. If that is true, why not buy an ETF of that industry rather than cherry pick stocks? What if the one or two companies you buy in that industry turn out to be dogs?"

Asset management is much more than just "buying and selling stocks" as any portfolio manager will tell you. There are many aspects that portfolio managers are responsible for in funds including:

• Risk Management and protection or hedging strategies
• Investment style
• Investment objectives and goals
• Liquidity requirements

It is important to comprehensively understand and prepare as much as possible so that you can maximize your chances of getting a successful result in your interviews.

Investment Challenge - 3rd Place

Yesterday, we participated in the Investment Challenge hosted by the Rotman Asset Management Association. Shree, Petar, Natalia and myself presented to a panel of judges from industry who critiqued our fund's performance, benchmark, goals, execution etc. There were teams from the Rotman MBA as well as the Masters of Financial Economics (MFE) program.

We made it to the final round and placed 3rd overall. More importantly, I learned a lot while in this competition about the practical side of asset management beyond the academic theory. And it is certainly more than just "trading stocks".

It was absolutely a pleasure to work with our team members. Shree is particularly passionate about asset management and it shows when you speak to him about stocks and what is happening in the economy. I learned a lot from him in terms of the practical side of asset allocation, risk management, company analysis etc.

I've been mentioning this to several people, but I think it is absolutely critical to participate in competitions and activities. Rotman does a decent job of trying to mix up the groups and have as much interaction as possible between the incoming 265 students every year. There is your 1st semester section of 65 people and team of 5, 2nd semester section of 65 people and team of 5, your 24 hour strategy comp team of 5 or 6, your markstrat team of 5 or 6 etc.

One of the most valuable experiences in the MBA is take advantage of these opportunities to work with smart driven people on interesting challenges and topics they are passionate about. Also, the open bars don't hurt.

Country Risk Premium

Previously, I was talking about the risks associated with bonds and how yield prices are constructed based on different types of risk.


I wanted to take a moment to have a peek at bonds. First is the US 10 year bond which is a proxy for the Risk Free Rate (RFR). Next, I wanted to look at the equivalent instruments available in different countries and their respective yields. If I'm not mistaken, the difference in yield prices should be accounted for by country risk only (having your bond issued by one country versus another). This should in theory account for both foreign exchange risk as well as sovereign risk.


Let's have a look:

Bond yields source: Bloomberg

A few interesting notes: While the US bond is considered risk free, there are some countries which have yields which are lower (Canada, Germany, Swedish, Swiss, and Japan). Other countries with bond yields at a premium include: Italian, Spanish and Australian. French and Dutch seem to be about par.