Because this is what powers us between 23:00 and 02:00. Right click to open link in a new tab.
This is all new to me, but most of it is definitely not new.
Buscemi, Brazilian Jazz, try Ramiro's Theme
Dub FX, really good dub, try Clue 1
Kleerup, not a new band but still fairly unknown electronic artist, try Towers of Trellick
De-Phazz, Alt Jazz, try Jazz Music
Robyn, electronic pop... because it's hella catchy, try Stars 4 Ever
Zion I, hip hop. not that unheard of, try Coastin
Schmoov, electronic and soothing, try Playground
Gustav Holtz, Opera Number 32, VII, The Planets, Neptune. Best Classical musician I have ever heard.
Miami Horror, good upbeat electronica, try Sometimes
Simian Mobile Disco, this driving electronica song is very appropriate for when I listen to music. Sleep Deprivation
Grafton Primary, Good electronica, very 80's feel. try Left of Nowhere
C-Mon & Kypski, Funky Jazz cut ups, try Shitty Bum
Explosions in the Sky, driving mellow electric guitar, try First Breath After Coma
Saturday, January 29, 2011
Monday, January 24, 2011
Investment Banking Pay
I need to preface this by saying I don't actually know. I have a rough idea of what my first year compensation is, but I'm guessing on the performance bonus, and the hours per week are estimated from what analysts at wallstreetoasis.com are saying from my area and division.
I decided to try to map out the first 10 years, and I was fairly aggressive with my progression. My hour per day estimates are below.
So I'm thinking 2 years at Analyst, 3 at Associate, and then through VP to Director by the end of 10 years on the job. I know it's possible, I'm not sure if its plausible, but we have to start somewhere. I expect to be working 70 hour weeks as an Analyst, however I know that 100 hour weeks are possible. I set a lower bound at 45 hours, there is now way the deal could get sweeter than that, and if I'm only putting in 45 hour weeks its because my team has no work, so I'm likely both not looking at a good bonus and possibly out of a job.
Now for the numbers.
My sensitivity assumptions are subjective, should be viewed as upper and lower bands, not any statistical deviation measure derived from actual data sets. My expectation growth assumptions however are not entirely made up, there are lots of web resources that estimate salary scaling over time, I primarily used payscale.com.
So whats the story. The upper graph shows clearly that i-banking is a 6 figure foray with bulge bracket banks, a mind boggling figure for any starving student. Of course this is all pre-tax, but it's nothing to complain about. The lower graph however tells the real story; one that is a common find for anyone seriously considering the industry. The pay per hour can be less than great. I suggest you read this mergers and inquisitions article, to hear the horror stories of i-bankers making less than McDonalds wages in the crunch.
Now we aren't heading into a recession, especially not in commodities which is my area of work, so I should be hitting above $20 an hour, but there is no real reason to expect my pay to be that much higher. My "good" forecast assumes some pretty legendary bonus numbers, I really think I'm going to be hitting close to my expectations, especially early on. All this tells us that i-banking has its proving ground period, just like every other competitive entry level position in business. The salary numbers are big, but the hours put in at the office can be legendary; just look at all the i-banking blogs that talk about that exact topic!
It is sure to be an interesting couple of years.
I decided to try to map out the first 10 years, and I was fairly aggressive with my progression. My hour per day estimates are below.
Now for the numbers.
So whats the story. The upper graph shows clearly that i-banking is a 6 figure foray with bulge bracket banks, a mind boggling figure for any starving student. Of course this is all pre-tax, but it's nothing to complain about. The lower graph however tells the real story; one that is a common find for anyone seriously considering the industry. The pay per hour can be less than great. I suggest you read this mergers and inquisitions article, to hear the horror stories of i-bankers making less than McDonalds wages in the crunch.
Now we aren't heading into a recession, especially not in commodities which is my area of work, so I should be hitting above $20 an hour, but there is no real reason to expect my pay to be that much higher. My "good" forecast assumes some pretty legendary bonus numbers, I really think I'm going to be hitting close to my expectations, especially early on. All this tells us that i-banking has its proving ground period, just like every other competitive entry level position in business. The salary numbers are big, but the hours put in at the office can be legendary; just look at all the i-banking blogs that talk about that exact topic!
It is sure to be an interesting couple of years.
Friday, January 21, 2011
How to do a financial event study
Event studies, the financial professors favorite tool in Efficient Market Hypothesis (EMH) tests. If you don't use it you lose it, so I figured I'd detail the process now while I remember it.
The Event
So the above is the event study and its' many possibilities. Lets use a earnings release date as an example. The tallest vertical line is the actual release date, we will call this time 0. The left vertical line will be the end of the development period and we will say this is day -30. This means that the left line is 30 trading days before the earnings release date. Between the left line and the right line is the event window and it is symmetrical which means the event window ends on day +30. Now lets talk stats.
The Development Period
As said above the development period ends at day -30, the left vertical line, and is begins around 100 days before that, lets say day -130. Through this period you need to develop your expected returns model - the CAPM.
The intuition of this is you want to find out if the company has returns that are different than what you would normally expect on a average trading day. You could simply suggest it would return the average daily historical return for the company, but there is a much better way. By regressing your companies returns against the market (the S&P 500 usually) you can create a one parameter model that will predict the expected return for your stock based on how the market is performing.
E[Rj] = A + B(Rm - Rf). Where E means 'expected', Rj is the return of your 'j'th company, A is the 'alpha' AKA predicted abnormal return, B is the 'beta' the parameter that acts upon your independent variable to estimate your company return, Rm is the S&P 500 return and Rf is the risk free return for the period understudy. Many practitioners set their 'intercept' or A to zero so their regression simply is B(Rm - Rf). If this doesn't hurt the R2 of your test to much, it is an acceptable simplifying assumption.
At the end of this you might have a formula that says Rj = 1.2*(Rm - 0) for a daily return. In words this says that the return of the S&P 500 multiplied by 1.2 will be your best estimate of your stock return.
The last note on the development period is it needs to be 'sterile' or free of any big events. For instance if the company announced a bid to take over another company you will have some pretty irregular earnings in that period and your regression will not predict the event window very well.
The Event Window
Okay so why did we stop our development period at day -30 instead of day 0. The answer is leakage. Often corporate 'events' will leak into the industry before they are widely disseminated by the press. This is sloppily shown by the brown line, which starts to rise before day 0. If the brown line were to rise as it does before day 0 but then flatten off you may be able to make the following statement. "The earnings announcement was responsible for positive abnormal returns, however these returns were not tradeable by the general public because they occurred before the announcement. This effect is closely monitored when the focus of your event study is the affect of insider trading.
The red line that oscillates around the blue time-line represents a case where the company does not experience abnormal returns. It is important to remember this is not the same as saying the company has 0 return, if the market is going up or down the stock is following it with a closeness that is determined by its beta.
The yellow line represents what the EMH predicts (red line too). As soon as the information is released the price adjusts and nobody has time to trade on it, unless they were holding the stock before the event, in which case they still were exposed to the risk of the abnormal return being negative. Unlike the brown or burgundy line the change has no drift. In reality sometimes events have drift, where the original bump continues of the following days. This is evidence for 'weak form efficiency' because a trader could just buy stocks who had an abnormal bump and sell stocks who had abnormal losses.
Prove it
So lets say you think you can make money by always buying/selling a company after a certain event happens. You name it, it could be changing auditors, a technical indicator, a product announcement, whatever. Before you go dumping your money after seeing it work once, you should figure out the strength of the effect. Run the numbers on 30 firms who have gone through the event, calculating the CAPM for each and recording the abnormal returns. Add all of the abnormal returns in your chosen trading window (perhaps day 0 to day 45), this is the stocks 'cumulative abnormal return' or CAR. Then average all of the CAR's for each firm, and get your CAR_bar (bar means average in stats lingo). You also need the standard deviation of the sample of CAR's, call this Sigma_car.
Test it with the following formula, Tstat = (CAR_bar)/(Sigma_car/(sqrt(N - 1))) where N is the 30 because you have 30 firms. If this Tstat is 2, you have a bomber strategy that is going to work with 90% certainty. Tstat of 2.56 works with 95% certainty. Lower Tstats means that your CAR_bar might not be significantly 'not zero' enough to gaurentee you a pay day.
So What
Good point. Besides being a key component to any 4th year finance semester or grad school event studies aren't that common in the real world. They can however really simplify your search for indicators that affect stock prices, and if you find a match that has significant CAR with a lot of persistence or drift, you've got yourself a winning ticket.
The Event
The Development Period
As said above the development period ends at day -30, the left vertical line, and is begins around 100 days before that, lets say day -130. Through this period you need to develop your expected returns model - the CAPM.
The intuition of this is you want to find out if the company has returns that are different than what you would normally expect on a average trading day. You could simply suggest it would return the average daily historical return for the company, but there is a much better way. By regressing your companies returns against the market (the S&P 500 usually) you can create a one parameter model that will predict the expected return for your stock based on how the market is performing.
E[Rj] = A + B(Rm - Rf). Where E means 'expected', Rj is the return of your 'j'th company, A is the 'alpha' AKA predicted abnormal return, B is the 'beta' the parameter that acts upon your independent variable to estimate your company return, Rm is the S&P 500 return and Rf is the risk free return for the period understudy. Many practitioners set their 'intercept' or A to zero so their regression simply is B(Rm - Rf). If this doesn't hurt the R2 of your test to much, it is an acceptable simplifying assumption.
At the end of this you might have a formula that says Rj = 1.2*(Rm - 0) for a daily return. In words this says that the return of the S&P 500 multiplied by 1.2 will be your best estimate of your stock return.
The last note on the development period is it needs to be 'sterile' or free of any big events. For instance if the company announced a bid to take over another company you will have some pretty irregular earnings in that period and your regression will not predict the event window very well.
The Event Window
Okay so why did we stop our development period at day -30 instead of day 0. The answer is leakage. Often corporate 'events' will leak into the industry before they are widely disseminated by the press. This is sloppily shown by the brown line, which starts to rise before day 0. If the brown line were to rise as it does before day 0 but then flatten off you may be able to make the following statement. "The earnings announcement was responsible for positive abnormal returns, however these returns were not tradeable by the general public because they occurred before the announcement. This effect is closely monitored when the focus of your event study is the affect of insider trading.
The red line that oscillates around the blue time-line represents a case where the company does not experience abnormal returns. It is important to remember this is not the same as saying the company has 0 return, if the market is going up or down the stock is following it with a closeness that is determined by its beta.
The yellow line represents what the EMH predicts (red line too). As soon as the information is released the price adjusts and nobody has time to trade on it, unless they were holding the stock before the event, in which case they still were exposed to the risk of the abnormal return being negative. Unlike the brown or burgundy line the change has no drift. In reality sometimes events have drift, where the original bump continues of the following days. This is evidence for 'weak form efficiency' because a trader could just buy stocks who had an abnormal bump and sell stocks who had abnormal losses.
Prove it
So lets say you think you can make money by always buying/selling a company after a certain event happens. You name it, it could be changing auditors, a technical indicator, a product announcement, whatever. Before you go dumping your money after seeing it work once, you should figure out the strength of the effect. Run the numbers on 30 firms who have gone through the event, calculating the CAPM for each and recording the abnormal returns. Add all of the abnormal returns in your chosen trading window (perhaps day 0 to day 45), this is the stocks 'cumulative abnormal return' or CAR. Then average all of the CAR's for each firm, and get your CAR_bar (bar means average in stats lingo). You also need the standard deviation of the sample of CAR's, call this Sigma_car.
Test it with the following formula, Tstat = (CAR_bar)/(Sigma_car/(sqrt(N - 1))) where N is the 30 because you have 30 firms. If this Tstat is 2, you have a bomber strategy that is going to work with 90% certainty. Tstat of 2.56 works with 95% certainty. Lower Tstats means that your CAR_bar might not be significantly 'not zero' enough to gaurentee you a pay day.
So What
Good point. Besides being a key component to any 4th year finance semester or grad school event studies aren't that common in the real world. They can however really simplify your search for indicators that affect stock prices, and if you find a match that has significant CAR with a lot of persistence or drift, you've got yourself a winning ticket.
Thursday, January 20, 2011
The joy of always being wrong
I just watched a BBC documentary on science (video below) and it centered on a general theme of precision. Basically anything in life is unknowable if you want absolute precision; it is only by allowing a certain degree of estimation that we can know anything. You can never truly know the age of the universe, the length of the space bar on your computer, what time it is, etc.
This brings up a question: am I okay with being wrong. On a personal level I think I am okay that everything I think about life can be countered with a 'but'. I will really enjoy poking holes in all of the 'facts' my kids may bring home from school. It's being wrong as a professional that might get to me.
Valuation is all estimation, and over the course of the last year I am beginning to learn that the best investment banker isn't the best estimator. Because valuation is all estimation, everybody understands any value derived is a 'best guess'. The thing about i-banking is that bankers almost always have an incentive to make the value higher or lower than the true value. I used to think that i-bankers would spend a lot of time on getting the true value, and then adjust growth, margin, and discount metrics, to derive an either optimistic or pessimistic price. Now I'm beginning to think that i-bankers skip the true valuation altogether and go straight for the optomistic or pessimistic price. Instead of that price being a statistically plausible deviation from the true price, it just becomes a strategically plausible price that the buyer/seller will accept. The whole process moves from science to sales.
I like the science, I love how stochastic variables working together can bring meaningful estimations. The idea that time devoted to estimation of any variable makes it more accurate, that overtime any model can become more and more precise is extremely empowering. Like the mathematician/physicist/statistician, time spent on my task brings increase certainty; unlike the scientist, instead of studying how things are or were, I focus on how things will be.
I am okay with always being wrong if I cared about being right in the first place. Reality hardly ever strikes the middle of the bell curve anyhow. I am not sure if I'm okay with being wrong, if I use the excuse that I can never be right to shape (or fabricate) my sure-to-be wrong answer to the will of my employer. I can only hope after the mind shock of moving into the private sector that I remember the difference.
This brings up a question: am I okay with being wrong. On a personal level I think I am okay that everything I think about life can be countered with a 'but'. I will really enjoy poking holes in all of the 'facts' my kids may bring home from school. It's being wrong as a professional that might get to me.
Valuation is all estimation, and over the course of the last year I am beginning to learn that the best investment banker isn't the best estimator. Because valuation is all estimation, everybody understands any value derived is a 'best guess'. The thing about i-banking is that bankers almost always have an incentive to make the value higher or lower than the true value. I used to think that i-bankers would spend a lot of time on getting the true value, and then adjust growth, margin, and discount metrics, to derive an either optimistic or pessimistic price. Now I'm beginning to think that i-bankers skip the true valuation altogether and go straight for the optomistic or pessimistic price. Instead of that price being a statistically plausible deviation from the true price, it just becomes a strategically plausible price that the buyer/seller will accept. The whole process moves from science to sales.
I like the science, I love how stochastic variables working together can bring meaningful estimations. The idea that time devoted to estimation of any variable makes it more accurate, that overtime any model can become more and more precise is extremely empowering. Like the mathematician/physicist/statistician, time spent on my task brings increase certainty; unlike the scientist, instead of studying how things are or were, I focus on how things will be.
I am okay with always being wrong if I cared about being right in the first place. Reality hardly ever strikes the middle of the bell curve anyhow. I am not sure if I'm okay with being wrong, if I use the excuse that I can never be right to shape (or fabricate) my sure-to-be wrong answer to the will of my employer. I can only hope after the mind shock of moving into the private sector that I remember the difference.
Monday, January 10, 2011
Thoughts on Reputation and the Politics of Groupwork
I had some thoughts on personal reputation on the walk home and although I'm fairly hopped up on DayQuil I think I'll take the time to write about it while the thought is fresh. I'd also like to talk about institutional reputation (like school reputation), and how one might handle their personal reputation when being first seen as a representative of a brand (like a school or a business... "oh so and so works for goldman, they must be cutthroat"), but I'll do that in another post.
The reason the idea of reputation came to me, is I'm starting my third quarter in b-school and this comes with 4 classes that involve grouping into teams. It's a bit interesting to see the power plays and politics in this scenario, and I'm left to wonder how one might 'game the system' or pretend to be a real player when they are actually a academic lightweight.
The Game
Grouping into teams is nothing new for most business students, but many "hard science" students haven't had much exposure to the concept before b-school. Also in American b-schools competition is fierce and so even those students who are familiar with the politics, find the higher stakes ups the energy.
The class might ask for groups of 5. Strong students have an incentive to group up with other strong students, and weak students have a at least equal (but likely greater if they care about grades) incentive to group up with strong students. The theory is that groups with the most strong students will score the best on assignments, something that very well may not be true but under the assumption that it's generally accepted by the class, means that everyones goal is the same and so the basic principles of game theory should hold.
The Players
This is where reputation comes in. I'll briefly try to slot students into their separate 'types' and then I'll start talking about how reputation can both help and hinder the student.
The Apathetic
For whatever reason this type of student both doesn't put the time in, and doesn't want to put the time in. Maybe b-school isn't for them, whatever the case these students are quickly recognized by their group members as freeloaders and as the year progresses that word gets around. This doesn't necessarily mean they don't get into the best groups, and the politics of the group up play a big factor - I'll expand later. [on a re-read I never do, so I will now, Apathetic can transform into Unprepared]
The Unprepared
Business schools generally accept ~60% of their students from non BCOM backgrounds, and these students come in with no or very little academic background in the subject matter. Matched with a ESL status, it can be very daunting for these student to keep up to their BCOM background peers. Some have that special gift of an amazing work ethic and can compete toe to toe with those students on their second lap of the material, some don't and although their ROI on the degree is greater than the BCOM's they aren't able to lock in that high GPA. We'll somewhat unfairly call these students "The Unprepared".
The Scattered
It may be maturity, it may be a hard coded need to procrastinate, it may be a slice of genius, whatever the cause every class has the scattered student. Some of them do surprisingly well on exams and homework, after cramming and last minute all nighters; some fall apart when they time their push a bit late. I personally find this type of student the most frustrating to work with, but I do admire those who pull it off.
The Work Horse
These types of students are the ones who put their blood sweat and tears into their work. I truly wish I was one, but I burn out hours before these students really start pouring it on. The interesting thing about these students is half of them are 'dark horses' and half are well known for their work ethic. People will often think The Naturals (below) are the students with the highest grades, but in my experience they almost always trail a handful of work horses that are knocking of 4.0 gpa's every semester. The dark horses won't speak up in class, but can be often found in the library late into the evening, while the recognized - light horses?? - will be very questioning students that contribute regularly (sometimes to excess) in class discussion.
The Naturals
These students are either set up for success by being both relative hard working and coming from a BCOM background. There is a bit of grey area between this student and the successful 'Scattered' student. Although the successful scattered student is obviously 'a natural', I'll just keep the naturals in this discussion to the nutured (as opposed to by nature) kind.
The Politics
So what actually happens? From what I can tell there are three behaviors, the initiators, the receivers, and the lost. Weeks, sometimes months before the class begins the initiators will start approaching who they view to be strong students, and ask them to be group mates. Initiators seem to usually be The Unprepared, The Workhorses, and sometimes The Naturals. The receivers tend to either be Workhorses or Naturals. And here is where it gets interesting, the answer is almost always yes.
Being a receiver is a gratifying experience, everybody likes to be asked into a group, it's like getting picked first for the sports team. The trouble is receivers have a chance of being asked by an Unprepared, and it is really hard politically to say no. This cockiness can lead to a sub-optimal group allocation, but sometimes the Natural or the Workhorses get asked by a fellow Workhorse or Natural, and that is the ultimate ego boost - being asked to be on the A team, by the A team. Ironically because of the reputation effect Naturals benefit from, they have the opportunity to be recruited by students stronger than themselves (A team recruiting the B team). This phenomena creates an incentive to be a receiver for two reasons, first the ego boost, second receivers who are asked are automatically identified as either Workhorses or Naturals by both the asking team, and anyone around the person when the asking occurs. This reputation boost can create a bit of an aura for the person in the best way possible - they don't have to say a thing.
As a quick aside before we move to initiators, those not asked become quickly (either wrongly or rightly) identified as Scattered or Apathetic, a dark mark but sometime a fairly unobservable one.
So as you can probably see the Workhorses and the Naturals have a choice to make. Take the risk to become a receiver and get the reputation benefit of being on the A-Team, or the cost of either not being picked at all, or being picked, and begrudgingly accepting, by the Unprepared. As a initiator the cost is small and the reward is small - if they are a Unprepared they have the chance to group up with Workhorses or Naturals, if they are Workhorses or Naturals they have the chance to group up with other like-strength peers before being asked by a Unprepared. So there is little downside, but they 'signal' to the student body that they may be an Unprepared student (recalling that we assume only Naturals and Workhorses are recievers and initiators can be Naturals, Workhorses or Unprepareds). So they don't get a reputation boost, and depending on the information known about their abilities they may actually be mislabeled as Unprepared.
So what happens. It seems to me workhorses that have BCOM (or other team work related) backgrounds tend to choose the initiator path and keep their reputation intact by talking about their marks a bit, and making an effort to show their fellow group members that they are workhorses in hopes that the word spreads (and in time it does). By thinking ahead these students can secure some really powerful groups and if given the chance only play the game once, by grouping up with the same students in every class. Workhorses from non-teamworking backgrounds seem to be receivers mostly because they are not familiar with 'the game', and they often are picked up by the Unprepared students who can see their strength and (relative) ambivalence. Most of the Naturals seem to play the waiting game, and those with the gift of the 'silver tongue' can dodge unwanted invites with class and end up in 'power groups' (or what they perceive to be good groups at least). This can be a reputation boost, but interestingly the biggest winner seems to be the Natural who willingly accepts both A Team and B Team groups, and carries the B Team group. Although not energy efficient and likely not mark efficient it seems to be reputationally efficient, because it showcases their academic vigor, and because they end up working with a variety of people which spreads the population of 'witnesses'.
Does it matter?
Not really. Most of the marks end up on the final exam anyways, and reputation is a very surface level assessment of a person. It can be manipulated or misrepresented only when track record and third party information is unavailable (as in previous group mates, not agencies). Also the group split is far from equal, in my experience over 80% of the class could be characterized as workhorses or naturals, I have yet to work with a 'dud'.
Still the small scale political theater is fun to watch and it drives home an old point. Those that say the least about themselves (both in words and in actions -> being a initatior) have an opportunity to create an aura of excellence that surpasses their actual skills. Although overtime this difference normalizes as their true track record comes to light, it can provide some interesting opportunities and benifits in the short run. It reminds me of Lt. Spiers from The Band of Brothers, here is the quote:
The reason the idea of reputation came to me, is I'm starting my third quarter in b-school and this comes with 4 classes that involve grouping into teams. It's a bit interesting to see the power plays and politics in this scenario, and I'm left to wonder how one might 'game the system' or pretend to be a real player when they are actually a academic lightweight.
The Game
Grouping into teams is nothing new for most business students, but many "hard science" students haven't had much exposure to the concept before b-school. Also in American b-schools competition is fierce and so even those students who are familiar with the politics, find the higher stakes ups the energy.
The class might ask for groups of 5. Strong students have an incentive to group up with other strong students, and weak students have a at least equal (but likely greater if they care about grades) incentive to group up with strong students. The theory is that groups with the most strong students will score the best on assignments, something that very well may not be true but under the assumption that it's generally accepted by the class, means that everyones goal is the same and so the basic principles of game theory should hold.
The Players
This is where reputation comes in. I'll briefly try to slot students into their separate 'types' and then I'll start talking about how reputation can both help and hinder the student.
The Apathetic
For whatever reason this type of student both doesn't put the time in, and doesn't want to put the time in. Maybe b-school isn't for them, whatever the case these students are quickly recognized by their group members as freeloaders and as the year progresses that word gets around. This doesn't necessarily mean they don't get into the best groups, and the politics of the group up play a big factor - I'll expand later. [on a re-read I never do, so I will now, Apathetic can transform into Unprepared]
The Unprepared
Business schools generally accept ~60% of their students from non BCOM backgrounds, and these students come in with no or very little academic background in the subject matter. Matched with a ESL status, it can be very daunting for these student to keep up to their BCOM background peers. Some have that special gift of an amazing work ethic and can compete toe to toe with those students on their second lap of the material, some don't and although their ROI on the degree is greater than the BCOM's they aren't able to lock in that high GPA. We'll somewhat unfairly call these students "The Unprepared".
The Scattered
It may be maturity, it may be a hard coded need to procrastinate, it may be a slice of genius, whatever the cause every class has the scattered student. Some of them do surprisingly well on exams and homework, after cramming and last minute all nighters; some fall apart when they time their push a bit late. I personally find this type of student the most frustrating to work with, but I do admire those who pull it off.
The Work Horse
These types of students are the ones who put their blood sweat and tears into their work. I truly wish I was one, but I burn out hours before these students really start pouring it on. The interesting thing about these students is half of them are 'dark horses' and half are well known for their work ethic. People will often think The Naturals (below) are the students with the highest grades, but in my experience they almost always trail a handful of work horses that are knocking of 4.0 gpa's every semester. The dark horses won't speak up in class, but can be often found in the library late into the evening, while the recognized - light horses?? - will be very questioning students that contribute regularly (sometimes to excess) in class discussion.
The Naturals
These students are either set up for success by being both relative hard working and coming from a BCOM background. There is a bit of grey area between this student and the successful 'Scattered' student. Although the successful scattered student is obviously 'a natural', I'll just keep the naturals in this discussion to the nutured (as opposed to by nature) kind.
The Politics
So what actually happens? From what I can tell there are three behaviors, the initiators, the receivers, and the lost. Weeks, sometimes months before the class begins the initiators will start approaching who they view to be strong students, and ask them to be group mates. Initiators seem to usually be The Unprepared, The Workhorses, and sometimes The Naturals. The receivers tend to either be Workhorses or Naturals. And here is where it gets interesting, the answer is almost always yes.
Being a receiver is a gratifying experience, everybody likes to be asked into a group, it's like getting picked first for the sports team. The trouble is receivers have a chance of being asked by an Unprepared, and it is really hard politically to say no. This cockiness can lead to a sub-optimal group allocation, but sometimes the Natural or the Workhorses get asked by a fellow Workhorse or Natural, and that is the ultimate ego boost - being asked to be on the A team, by the A team. Ironically because of the reputation effect Naturals benefit from, they have the opportunity to be recruited by students stronger than themselves (A team recruiting the B team). This phenomena creates an incentive to be a receiver for two reasons, first the ego boost, second receivers who are asked are automatically identified as either Workhorses or Naturals by both the asking team, and anyone around the person when the asking occurs. This reputation boost can create a bit of an aura for the person in the best way possible - they don't have to say a thing.
As a quick aside before we move to initiators, those not asked become quickly (either wrongly or rightly) identified as Scattered or Apathetic, a dark mark but sometime a fairly unobservable one.
So as you can probably see the Workhorses and the Naturals have a choice to make. Take the risk to become a receiver and get the reputation benefit of being on the A-Team, or the cost of either not being picked at all, or being picked, and begrudgingly accepting, by the Unprepared. As a initiator the cost is small and the reward is small - if they are a Unprepared they have the chance to group up with Workhorses or Naturals, if they are Workhorses or Naturals they have the chance to group up with other like-strength peers before being asked by a Unprepared. So there is little downside, but they 'signal' to the student body that they may be an Unprepared student (recalling that we assume only Naturals and Workhorses are recievers and initiators can be Naturals, Workhorses or Unprepareds). So they don't get a reputation boost, and depending on the information known about their abilities they may actually be mislabeled as Unprepared.
So what happens. It seems to me workhorses that have BCOM (or other team work related) backgrounds tend to choose the initiator path and keep their reputation intact by talking about their marks a bit, and making an effort to show their fellow group members that they are workhorses in hopes that the word spreads (and in time it does). By thinking ahead these students can secure some really powerful groups and if given the chance only play the game once, by grouping up with the same students in every class. Workhorses from non-teamworking backgrounds seem to be receivers mostly because they are not familiar with 'the game', and they often are picked up by the Unprepared students who can see their strength and (relative) ambivalence. Most of the Naturals seem to play the waiting game, and those with the gift of the 'silver tongue' can dodge unwanted invites with class and end up in 'power groups' (or what they perceive to be good groups at least). This can be a reputation boost, but interestingly the biggest winner seems to be the Natural who willingly accepts both A Team and B Team groups, and carries the B Team group. Although not energy efficient and likely not mark efficient it seems to be reputationally efficient, because it showcases their academic vigor, and because they end up working with a variety of people which spreads the population of 'witnesses'.
Does it matter?
Not really. Most of the marks end up on the final exam anyways, and reputation is a very surface level assessment of a person. It can be manipulated or misrepresented only when track record and third party information is unavailable (as in previous group mates, not agencies). Also the group split is far from equal, in my experience over 80% of the class could be characterized as workhorses or naturals, I have yet to work with a 'dud'.
Still the small scale political theater is fun to watch and it drives home an old point. Those that say the least about themselves (both in words and in actions -> being a initatior) have an opportunity to create an aura of excellence that surpasses their actual skills. Although overtime this difference normalizes as their true track record comes to light, it can provide some interesting opportunities and benifits in the short run. It reminds me of Lt. Spiers from The Band of Brothers, here is the quote:
- Speirs: You wanna know if they're true or not, the stories about me? Did you ever notice with stories like that, everyone says they heard it from someone who was there. Then when you ask that person, they say they heard it from someone who was there. It's nothing new, really. I bet if you went back two thousand years, you'd hear a couple centurions standing around yakkin' about how Tertius lopped off the heads of some Carthaginian prisoners.
- Lipton: Well, maybe they kept talking about it because they never heard Tertius deny it.
- Speirs: Maybe that's because Tertius knew there was some value to the men thinking he was the meanest, toughest son of a bitch in the whole Roman Legion.
Sunday, December 26, 2010
Working for the Empire - Convincing family and friends that i-banking is moral
Christmas has passed, marking the mid way point of another winter vacation and the conclusion of two great nights of debate with the extended family. Christmas eve was devoted to the virtues and problems of wikileaks, religion, foreign policy, Canada's involvement in Afganistan, and the motivations behind joining the army. Christmas day however centered largely on one topic, the financial crash and the gross immorality of investment bankers.
Before I resign myself to the idea that my years ahead will be spent reporting to Lord Vader on how I managed to squeeze $30M out of 5 deals that were not in the underlying firms interest, I think my trade needs me to give it the chance to be moral and just.
My fathers side of the family is composed of many families that have made their living in C level careers in the oil in gas industry. They are in essence a good composition of my future clients, and it's a bit startling to learn how much my future customers might secretly hate me.
Their main gripes center around two issues: the money, and the incentive. On the macro level they feel that the high pay draws away bright minds from engineering, medical, and policy careers that the country needs more. On the micro level they feel that salaries give i-bankers a false sense of superiority and invincibility. Make millions when times are good, get government bailouts when times are bad.
I argue that the money is good because few are up to the task of doing the work. There is no oligopoly in the investment banking world, it is highly competitive on all levels and in all areas. With so much competition, and so much interest in becoming an i-banker, if the workers didn't hold some element of a scarce resource their salaries would be squeezed away to keep the firms margins high. But it is the people who drive the business, and when one person manages to bring in $10M of revenue their salary becomes justified.
What I have more trouble explaining is why the business itself is so profitable. How has the ~3% spread stayed so high if there really is so much competition. The obvious answer is risk, but my uncles seem convinced that the i-banks of today have almost completely eliminated their exposure. I'm sure the next 12 months will shed some light on the issue. If in fact the industry has been able to collude and keep these historical premiums while managing to eliminate the risk (and the costs that come with it) then maybe the industry isn't so "fair". But it's a industry wide phenomena, and one that is admirable to any capitalist as long as there is not some anti-capitalist framework keeping it in place (government relationships, or true collusion). Over time the markets should correct for these non-normal profits and my future paycheck should get a bit slimmer.
The incentives are another issue because they really are a product of the individual i-bankers business conduct. My firm will make money when a deal is done. It will make more money if the deal is big, and it will make more money on more deals. Thus the big incentive is to have i-bankers drive as many big deals as possible. Deals are IPO's, bond issuance's, mergers, acquisitions, consolidations, spin offs, carve outs, and LBO's. It is my uncles contention that i-bankers advise companies to do deals when they shouldn't, and that when the deal is going through the i-banker isn't thinking about the firm, but instead how to maximize their cut.
I think the biggest point of issue is that business men know how to run their business, and if they are good, they know how to run it well. Unfortunately finding oil reserves and getting to them efficiently, doesn't have a lot to do with a bond deal or a takeover. Very smart individuals get caught in a world they don't understand and it's their baby at stake if things go sour. When you don't have a good grasp of the playing field it's natural to feel like its slanted against you. And sadly it is natural for it to be slanted against you. Businesses need capital, and so they need i-bankers, and i-bankers need businesses to need capital. This symbiotic relationship should ensure that neither side screws the other on the macro level because each side can permanently avoid the other on an individual level. If an i-banking team gets a rap for being sleazy and underhanded they won't get business. If a business gets a rap for providing faulty information and trying to screw their bankers, they won't get capital. These 'jungle rules' keep both parties from the extremes, but I would agree that the balance of power is in the i-bankers court. Tough.
Neither of these business realities are immoral. I truly believe that an i-banker that screws his clients, is not long for his job, and definitely not destined for riches in the long term. Like any service industry the client is king, and the i-banker that does right by his clients, will win their trust and lead them through the really bid deals when the time comes.
Before I resign myself to the idea that my years ahead will be spent reporting to Lord Vader on how I managed to squeeze $30M out of 5 deals that were not in the underlying firms interest, I think my trade needs me to give it the chance to be moral and just.
My fathers side of the family is composed of many families that have made their living in C level careers in the oil in gas industry. They are in essence a good composition of my future clients, and it's a bit startling to learn how much my future customers might secretly hate me.
Their main gripes center around two issues: the money, and the incentive. On the macro level they feel that the high pay draws away bright minds from engineering, medical, and policy careers that the country needs more. On the micro level they feel that salaries give i-bankers a false sense of superiority and invincibility. Make millions when times are good, get government bailouts when times are bad.
I argue that the money is good because few are up to the task of doing the work. There is no oligopoly in the investment banking world, it is highly competitive on all levels and in all areas. With so much competition, and so much interest in becoming an i-banker, if the workers didn't hold some element of a scarce resource their salaries would be squeezed away to keep the firms margins high. But it is the people who drive the business, and when one person manages to bring in $10M of revenue their salary becomes justified.
What I have more trouble explaining is why the business itself is so profitable. How has the ~3% spread stayed so high if there really is so much competition. The obvious answer is risk, but my uncles seem convinced that the i-banks of today have almost completely eliminated their exposure. I'm sure the next 12 months will shed some light on the issue. If in fact the industry has been able to collude and keep these historical premiums while managing to eliminate the risk (and the costs that come with it) then maybe the industry isn't so "fair". But it's a industry wide phenomena, and one that is admirable to any capitalist as long as there is not some anti-capitalist framework keeping it in place (government relationships, or true collusion). Over time the markets should correct for these non-normal profits and my future paycheck should get a bit slimmer.
The incentives are another issue because they really are a product of the individual i-bankers business conduct. My firm will make money when a deal is done. It will make more money if the deal is big, and it will make more money on more deals. Thus the big incentive is to have i-bankers drive as many big deals as possible. Deals are IPO's, bond issuance's, mergers, acquisitions, consolidations, spin offs, carve outs, and LBO's. It is my uncles contention that i-bankers advise companies to do deals when they shouldn't, and that when the deal is going through the i-banker isn't thinking about the firm, but instead how to maximize their cut.
I think the biggest point of issue is that business men know how to run their business, and if they are good, they know how to run it well. Unfortunately finding oil reserves and getting to them efficiently, doesn't have a lot to do with a bond deal or a takeover. Very smart individuals get caught in a world they don't understand and it's their baby at stake if things go sour. When you don't have a good grasp of the playing field it's natural to feel like its slanted against you. And sadly it is natural for it to be slanted against you. Businesses need capital, and so they need i-bankers, and i-bankers need businesses to need capital. This symbiotic relationship should ensure that neither side screws the other on the macro level because each side can permanently avoid the other on an individual level. If an i-banking team gets a rap for being sleazy and underhanded they won't get business. If a business gets a rap for providing faulty information and trying to screw their bankers, they won't get capital. These 'jungle rules' keep both parties from the extremes, but I would agree that the balance of power is in the i-bankers court. Tough.
Neither of these business realities are immoral. I truly believe that an i-banker that screws his clients, is not long for his job, and definitely not destined for riches in the long term. Like any service industry the client is king, and the i-banker that does right by his clients, will win their trust and lead them through the really bid deals when the time comes.
Saturday, December 18, 2010
How do YOU DCF?
Many a young financier becomes enraptured with the power of the discounted cash flow model in their third year finance classes, and student investment funds are teeming with DCF models that do three way sensitivity, have 4 growth stages, and model 20 years into the future. However quickly students feel cheated by this tool that promises to value everything with a income statement.
At first it's the baseline figures; what should the beginning change in net working capital be, what if it's been on a negative growth path for the last 3 years. Back in 2009, baselines were really ugly; what should you do with deferred tax loss, a negative NOPAT, uncertain CapEx expenditures. Then it's growth rates, which are especially tricky for growth companies. Soon you end up with a value per share for Google at $103 and you wonder where you went wrong.
I'll outline how I approach some of the many DCF decisions and then I'll talk about where the DCF really shines - when it's used backwards.
Revenue
This is the best line item to spend the majority of your time modeling because it's the hardest to fake. Managers can push and pull on SG&A, depreciation, tax expense, and really everything else before net income, but there is very little you can do (legally) to move the top line around.
I like to use one of three approaches to model revenue growth, regressions, market share & growth, and historical. Regressions are by far my favorite, especially for resource companies. How much correlation do you think there is between Nobel Energy Corp's historical revenue and the yearly price of oil multiplied by the barrels of oil produced per year? 85%.
Yes it's a very short development period, but it tells a good story, and it makes sense (when oil price drops so does revenue). The biggest plus is with regressions you are able to use industry data to forecast the company. In this Nobel Energy example I used the EIA oil forecast (see my "must have links" post) and with this I can model out 30 years if I desire. If the company you are tackling has been pretty stable for the last 10 years (no M&A or spin offs) you'd be best to create your model in a development period and then test it on recent ex ante data to see how it performs. For example create the formula on data from 2000 to 2006 and then see how it performs from 07 to 08. Or develop through 08 and test 09 to the TTM. Most models might break through the crash period, so give your power tests some slack if you use this as your test period.
If you are valuing a consumer goods or manufacturing business you will be most interested in market share and market growth. Analysts produce a lot of research on which companies are likely to gain or lose market share, and how much the entire market is expected to be worth today and tomorrow. The gathering and aggregating of this data will be much messier than resource data, but if you are able to put it all together I'd bet your model power would be far stronger than just extrapolating on historical data.
Sometimes you need to use what you have in the company, and although it hurts there are ways to make the best of it. The big first step is to dive deep into the MD&A and scour past conference calls to get a good idea on managements expectations for growth. Any negative expectations should be taken very seriously, any very positive expectation should be taken with a grain of salt.
NWC, CapEx, Dep, EBIT
So after spending all that time on modeling revenue, must we do the same for each DCF component as well. I say we do not! Whittling to our free cash flow using the formula:
Terminal Cash Flow
This whole calculation is a headache in itself, but extremely important as it usually makes up ~2/3 of the firm value. If the firm is steady state you could probably do the terminal cash flow anywhere from 5 to 10 years out. Theory and historical data suggests that the profit margin should squeeze that the firm will increase dividend payments (or start them) so your plowback is smaller. If the firm is growth, you might want to go through the exercise of smoothing to a mature stage over a 10 to 15 year horizon. Generally this is a messy business and it is hard not to feel like you are crudely making up growth factors when it's 2021.
Making the DCF do something useful
So after playing with your DCF and making it produce a price that fits your thesis, it's hard to think that the model is anything more than a elaborate way of making up a stock price. And honestly, without rich financial data, a professionally developed model, and a team of 7am to 11pm working analysts your not going to generate alpha. You can however make the model do something useful.
By accepting Semi Strong Form Efficiency instead of WFE we can say that the stock price is the correct price. This allows us to chose a different output variable, like equity risk premium for our DCF model (hope you soft coded it, so you can use solver). The beauty of it is, if the output variable is a 'common variable' (not firm specific) we can use several different firm DCF models to narrow down on a more accurate value. It's like triangulation in mountaineering, but in finance! For instance by modeling all of the big companies in the auto sector (and yes going through the work of regressions and margin estimates for each of them) we can turn all the models around and figure out what equity holders demand as a risk premium. Of course adjustments for outliers will be needed and you may want to weight your average to focus on 'pure' peer firms in the industry. Boil this down through all the sectors and you have completed a much more robust and market sensitive estimate of E[Rm], which we can feed back into either the CAPM or Famma French Model to make passive investment decisions. The power obviously would be found in narrow sub sectors where you are likely to be have some added information, but the method is actually a widely used practice.
What actually keyed me onto this method was a paper by Aswath Damodaran. You can find the paper here, and it nicely details some of the implications of the E[Rm] DCF solution method near the last third.
Revenue
This is the best line item to spend the majority of your time modeling because it's the hardest to fake. Managers can push and pull on SG&A, depreciation, tax expense, and really everything else before net income, but there is very little you can do (legally) to move the top line around.
I like to use one of three approaches to model revenue growth, regressions, market share & growth, and historical. Regressions are by far my favorite, especially for resource companies. How much correlation do you think there is between Nobel Energy Corp's historical revenue and the yearly price of oil multiplied by the barrels of oil produced per year? 85%.
If you are valuing a consumer goods or manufacturing business you will be most interested in market share and market growth. Analysts produce a lot of research on which companies are likely to gain or lose market share, and how much the entire market is expected to be worth today and tomorrow. The gathering and aggregating of this data will be much messier than resource data, but if you are able to put it all together I'd bet your model power would be far stronger than just extrapolating on historical data.
Sometimes you need to use what you have in the company, and although it hurts there are ways to make the best of it. The big first step is to dive deep into the MD&A and scour past conference calls to get a good idea on managements expectations for growth. Any negative expectations should be taken very seriously, any very positive expectation should be taken with a grain of salt.
NWC, CapEx, Dep, EBIT
So after spending all that time on modeling revenue, must we do the same for each DCF component as well. I say we do not! Whittling to our free cash flow using the formula:
[Rev-Exp=GM-Dep-Tax=EBI+Dep+Chg_NWC-CapEx=FCF]
We are just left to consider proportions and margins before the terminal cash flow. If management gives firm CapEx projections than you can hard code those in if you feel they will happen, otherwise look closely at the CapEx proportion to revenue over a historical period that represents the future 5/10 years. Same should be done with depreciation, NWC and the profit margin to EBIT to estimate expenses. Generally if the company is doing more business all of these items should increase roughly linearly. The exception of course is if the business is shifting from one business cycle to another. If you feel the company is losing its "core competency" or competitive edge then you should make some adjustments to the EBIT margin you assume in the future. This is tricky business and you might want to look at a historical example. For instance you might look at Microsoft's margins coming into 2000, to model Google's next 10 years.Terminal Cash Flow
This whole calculation is a headache in itself, but extremely important as it usually makes up ~2/3 of the firm value. If the firm is steady state you could probably do the terminal cash flow anywhere from 5 to 10 years out. Theory and historical data suggests that the profit margin should squeeze that the firm will increase dividend payments (or start them) so your plowback is smaller. If the firm is growth, you might want to go through the exercise of smoothing to a mature stage over a 10 to 15 year horizon. Generally this is a messy business and it is hard not to feel like you are crudely making up growth factors when it's 2021.
Making the DCF do something useful
So after playing with your DCF and making it produce a price that fits your thesis, it's hard to think that the model is anything more than a elaborate way of making up a stock price. And honestly, without rich financial data, a professionally developed model, and a team of 7am to 11pm working analysts your not going to generate alpha. You can however make the model do something useful.
What actually keyed me onto this method was a paper by Aswath Damodaran. You can find the paper here, and it nicely details some of the implications of the E[Rm] DCF solution method near the last third.
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