MSF Class Quality, Simon Graduate School of Business, University of Rochester

I'm going to detail these as it applied to my interests and background. I am a direct from undergrad applicant, where I did a Bachelors of Commerce, and I am heading into Investment Banking. Thus I am biased against foundation courses as they are repeats for me, and I selected more banking courses than modeling courses.

All ranks are out of 5.
Content - Is the content itself interesting and at a masters level of comprehension.
Pace - How fast do you move through the material.
Real World Applicability - How likely will this class prepare you for your interviews and job.
Workload - Is it a 7 day a week grind?
Difficulty - How much re-tracing do you do on homework. How often do you not 'get it'.
Exams - How hard are the exams.
Cohort - How good are the classmates you are competing on the curve with.
Engagement - How pumped was I to be in class.
Professor: Who taught me.

Summer: 5 weeks, August to September
Corporate Budgeting - Mandatory
Content 3
Pace 5
Real World Applicability 3
Workload 4
Difficulty 2
Exams 3
Cohort 4
Engagement 5
Professor: Irfan Safdar
Bottom line: A good warm up for a challenging year ahead, it takes some getting used to Sunday nights at Simon!

This course is a total review of 2nd and 3rd year undergrad finance taught at breakneck speed. Although nothing's new it is useful to have it all put together in a short period, and is an excellent refresher for anyone doing a the fall banking recruitment push. The exams were challenging primarily because the midterm was a take home and several students grouped up so the curve was extremely competitive.

Core Statistics - Mandatory
Content 2
Pace 2
Real World Applicability 1
Workload 1
Difficulty 1
Exams 2
Cohort 2
Engagement 2
Professor: PhD Student
Bottom line: A baseline 'to little to fast' stats course that is mind numbing for ex stats grads and too fast for first timer's.

All the basics, t tests, regressions, binomial distributions, bayes theorem. It was almost comical to be taught time series econometrics in 5 slides after taking two entire courses on it in undergrad. This course is the vegetables you have to eat before the steak is brought out.

Core Economics - Mandatory
Content 3
Pace 1
Real World Applicability 1
Workload 1
Difficulty 1
Exams 3
Cohort 2
Engagement 3
Professor: PhD Student
Bottom line: Micro, Macro in 5 weeks.

A five week course somehow still felt painfully slow. However economics is always interesting, and these exams were surprisingly challenging, they really spread the curve out.

Communications - Mandatory Continues Until Spring
Content 1
Pace 1
Real World Applicability 4
Workload 1
Difficulty 1
Exams Nil
Cohort 1
Engagement 3
Professor: Dan Struble, Bob M.
Bottom line: Public speaking, business writing, leadership.

It seems fruitless but being video taped and critiqued on your presenting skills is an activity that can only happen in school.

Fall, 10 weeks, September to December
Investments - Mandatory
Content 3
Pace 2
Real World Applicability 3
Workload 3
Difficulty 3
Exams 3
Cohort 4
Engagement 3
Professor: Anzhela Knyazeva
Bottom line: It's Rochester so Investments means EMH.

This investments class does not extend beyond undergrad investments, but has some useful case based homework. Beware the Knyazeva sisters can get pretty tricky on their exams.

Institutional Finance - Mandatory
Content 4
Pace 3
Real World Applicability 5
Workload 4
Difficulty 3
Exams 3
Cohort 4
Engagement 5
Professor: Diana Knyazeva
Bottom line: Banking

I really enjoyed this class because it was the first departure from curriculum I had already received in my undergrad, and it cowed very closely to real world applications.

Corporate Financial Accounting - Or Financial Accounting 1
Content 2
Pace 1
Real World Applicability 2
Workload 1
Difficulty 1
Exams 1
Cohort 2
Engagement 2
Professor: Heidi Tribunella
Bottom line: Intro accounting.

Taking this class was a mistake. If you have any undergraduate accounting you definitely need to switch into the upper accounting course. This course does intro accounting, intermediate 1 and intermediate 2 at a surface level.

Corporate Finance - Mandatory
Content 3
Pace 2
Real World Applicability 2
Workload 4
Difficulty 3
Exams 3
Cohort 4
Engagement 4
Professor: Wei Yang
Bottom line: Stakeholder Game theory

This is Corporate Finance as you probably haven't been taught it before. You discuss all the different stakeholders, things like bankruptcy costs and debt overhang, but you do it all in a game theory framework. Homework, exams, and lectures are all structured around multistep games, with payoffs and NPV's. This added complexity makes the tuggle war between debt holders, shareholders, and management more interesting as you can quantify their actions and then the firms value as a result.

Winter, 10 weeks, January to March
Economic Theory of Organizations - Mandatory
Content 3
Pace 3
Real World Applicability 3
Workload 2
Difficulty 2
Exams TBD
Cohort 3
Engagement 3
Professor: Michael Raith
Bottom line: MBA course in an MS degree

I'm not entirely sure why we are learning this material in an MSF degree, but I think it's because Simon has been a research powerhouse in this area. It's all about incentive plans, intrinsic motivators, and org structure, all through economic maximization's. 

Cases In Finance - Optional
Content 4
Pace 3
Real World Applicability 5
Workload 3
Difficulty 3
Exams 3
Cohort 3
Engagement 5
Professor: Gregg Jarrell
Bottom line: Story time with some investment banking and cut throat 'negotiations'

The prof for this class is a riot, lots of great finance stories about half of which have a useful takeaway message. So far I've learned: never mess with the government, the best clients are billionaire's facing jail time, always act innocent even if your not, and bankers aren't really financiers, they are salesmen. Aside for the war stories, I have picked up some great 'things to remember' nuggets on the major valuation models. Jarrell is full of pointers on what mistakes professionals always make in their modeling and how to avoid them. For instance the much loved, beta unlever-lever technique for comparable valuation is flawed at it's core because it ignores the bankruptcy effect on the cost of equity.  The negotiations are a great game theory experiment, grades from 60 to 100 need to go out, and it all depends on what 'deal' you get from your peers. Every winner is matched with a loser, great class politics!

The test's are not hard however any mistakes are costly. Jarrell drops many useful hints in class, and non-Finance MBA's are nice to have on the curve come test time, because negotiations can be bloody and indiscriminant in their grade split ups.

Investment Management & Trading Strategies - Optional
Content 4
Pace 4
Real World Applicability 5
Workload 4
Difficulty 3
Exams 2
Cohort 3
Engagement 5
Professor: Daniel Burnside
Bottom line: A current mutual fund chief economist shows where EMH bends and where it breaks.

The prof for this course is very good. Down to earth, but extremely bright, lots of class engagement. The course starts a bit slow as you rehash EMH, but soon you are talking about anomalies, alpha creation, and I think the last half of the course is market structure and trading strategies. You are worked, but its mostly qualitative assignments.

The midterm is very similar to the practice midterm.

Financial Statement Analysis - Optional
Content 5
Pace 5
Real World Applicability 5
Workload 5
Difficulty 5
Exams 5
Cohort 4
Engagement 5
Professor: Charles Wasley
Bottom line: The hardest and most worthwhile course I have ever taken in my university career.

Do yourself a favor and sign up in the evening class with MBA's to make the curve a little easier because this class will punish you. The prof is a slave driver, and extremely strict (extra case assignment for anyone whose cell phone rings). I write the exam tomorrow morning, and after studying all week for it I can easily say it will be very difficult. The takeaways are huge though, FSA is an absolutely crucial skill for anyone who wishes to be an analyst, and this guy is the best I've seen. If you come to Simon for one course, make this it.

Be very weary as to how Wasley answers his qualitative questions on practice solutions, if you are not giving his 'right answer' on the exam to qualitative questions credit is very hard to come by.

Spring, 10 weeks, March to June
Fixed Income - Optional
Content 4
Pace 3
Real World Applicability 3
Workload 3
Difficulty 3
Exams 4
Cohort 3
Engagement 4
Professor: Wei Yang
Bottom line: A Fixed Income class that quickly moves beyond bonds and into structured products. Yang's exams always extend beyond the material and can have some very challenging questions.

Accounting For Management and Control - Required
Content 3
Pace 2
Real World Applicability 3
Workload 3
Difficulty 3
Exams 3
Cohort 2
Engagement 2
Professor: Jerold Zimmerman
Bottom line: A required managerial accounting class that can easily be switched out for another elective if the student wishes. The prof is good, but the class is almost entirely geared towards corporate management problems.

Entrepreneurial Finance - Optional
Content 5
Pace 4
Real World Applicability 4
Workload 3
Difficulty 3
Exams 3
Cohort 4
Engagement 5
Professor: Boris Nikolov
Bottom line: A very good follow up to Cases in Finance. The prof is young and the content covered in class is refreshingly technical. The class picks up after the business plan has been completed and goes all the way to IPO, focusing on forecasting and financing stages.

Thinking about Market Timing Strategies

In my 4th year undergrad I tried to create a crash predicting model based off of asset correlations. The idea was that as markets crash, unrelated securities correlate. The model failed for two reasons, by the time correlations really got underway the market had lost a lot, and markets correlate on the way up too.

But now that it's mid term week, I have a lot of "free time" on my hands and the bug bit again. This time I was going to make a much simpler model, based off of only variance. If I wasn't in the market I would be investing at the risk free rate, but as soon as I hit my buy signal I would plow everything I had in marketable securities into the market and keep it there. When my buy signal was tripped off I wouldn't sell any securities, but any new cash would be invested at the risk free rate until I hit a buy signal again.

Thesis
I predict that I can accurately enter market bottoms, by buying only on occasions where volatility is at its highest. I predict that these timed buys will yield profits that outweigh the opportunity cost of not investing in the market at all times.

Methodology
I have two time periods, from 1955 to present, and from 2000 to present. I have monthly S&P 500 and risk free rate data. I assume no transaction costs (or that I have a lot of capital). For presentation purposes I assume I have $1 of capital to invest each month, it can either be invested at the risk free rate, or buy a $1 stake of the S&P 500.

Variance has been calculated at the 6 month average Z score (average S&P value over last 6 months / standard deviation of S&P over last 6 months). The Z score is needed to standardize the non-randomly dispersed sample data (the index increases over the time period).

The buy signal is the manipulated variable, and is compared against the ratio (current 6 month Z score / average 6 month Z score for entire period). For instance if [buy signal] < Zcurrent/Z_ave then buy the market. Note that when markets are volatile the ratio is small, and when calm the ratio is large, bounded at 0. Also note that because we use the average 6 month Z from the whole period this is an ex post study, although it wouldn't be hard to estimate ex ante.

As stated above, if we don't have a buy signal we put $1 in a risk free investment. This $1 will be compounded monthly and receive additional $1 investments each month until a buy signal is reached. When a buy signal is reached the entire money market balance is invested in the S&P never to be sold. If the following month is also a buy signal, $1 is invested in the index at that months value, never to be sold. The first period that there is no buy signal, we invest $1 into a now empty money market account and the process starts again.

Results

1955 to Present
Before we get to the model results it is useful to look at the comparative returns between the S&P and the risk free rate.

This graph shows what dollar return you would get if you invested in either the S&P or the risk free rate some time in history. For instance in 1965 you would actually make more money on a dollar invested at the risk free, than you would in the S&P if you held them both to today. Thus although it is clear equity returns trump risk free return, there are exceptions, and abstaining from the market may provide good returns if timed correctly.

So let's put the strategy to work.

It sucked. The red line shows the number of trades, the blue line shows the return minus what you would make if you bought and held from 1955 ($6645 when investing a dollar a month). This is what a lot of the literature says, you can't beat the market, you need to be in it all the time. The horizontal access shows the buy signal value; because we only buy when the market is risky, the far left returns are when we hardly buy at all, and the far right returns are when we are almost always in the market (buy signal almost always on).

Just for fun I flipped the buy signal methodology. Now I want to buy when the market is at it's stablest, and invest in the risk free rate when it's at it's riskiest. Theoretically this doesn't make sense, risk should bring reward; but sometimes investments is about being contrarian so let's see what happens.

Would you look at that. It works! This graph works backwards to the one above, results on the left are when we are in the market the most, and the results on the right are when we are in the market the least. It looks like somewhere on the right hand side we hold off till an optimal moment, and then invest making an extra $3351 over the market return of $6645. I dug into it and below we see where that transaction actually takes place.

It just so happened that the last 6 months of 1983 were incredibly stable, Z/Z_ave peaked at 131, and we hit a buy signal in January of 1984 after investing only in risk free securities for 29 years. We dumped $1208 of compounded assets into the S&P, invested a further $1 in the S&P the next month, and then stuck to the money markets for the next 27 years to get a total return of $9995. This looks great but it is a data mining result in my opinion. If we drop our buy signal for 3.6 to 3.0 we make a $10 loss as shown below. It's to jumpy to be predictable.

We see here that buying earlier, in 1974, and later in 1993, although visually benign, totally wreaks the return profile with a slightly negative abnormal return.

2000 to 2011
So if it doesn't work in the last 55 years, how about the last 11?

Expected results this time, the low volatility strategy that worked above never significantly outperforms and the high volatility strategy that didn't work above, does create major positive returns with buy signal thresholds between 0.1 and 0.5. On the graph this looks like a very narrow window of opportunity, but we must remind ourselves that ratios compress outcomes between 1 and 0 and so there is a fair bit of room to work with.(As the numerator shrinks the marginal change in the ratio is less per unit change in the numerator.)

So what does the buy timing look like.

Graph error here and below. It should be "less than" and it's not 3.5% is 3.3%

We see in this strategy we abstained from the market up until volatility reached it's peak in the crash of 2009. The 6 month window worked in our favor here, if we were using a shorter window we would have likely bought in higher. This resulted in a $53 dollar over performance against the buy and hold which yielded $146 after 11 years of $1 monthly investments. This works out to a cumulative annual return of ~3% over the benchmark, not bad at all. However again this is a pretty optimal data set. It is surprising how much return we have to give up in order to grab 2002 as well.

We lose more than 1% in annual return, by grabbing both dips (which is what we want in an ex ante mindset). This loss is of course the direct result of the 2009 crash, even the best market timer was behind in 09; risk free would have been better.

Takeaways
It's not time to set up a hedge fund, the results are mined and ex post. It is however an interesting result that we can think about in the future. When the 6 month Z score is 3 times smaller than the historical Z score (30.74) we have a historical suggestion that we have reached a bottom.

Timing strategies - when they work - are chunky, you fall from a winning position to a losing position with small deviations of your buy signal. Considering this buy signal will need to be derived from ex ante data, the margin for estimation error is very slim. 

Contrarian strategies may work. Surprisingly a 1955 investor who waited for extremely stable markets to invest, and avoided rocky markets, could have done extremely well if their buy signals were just right.

Implied Distribution Option Model - Project Stalled

It was fun to try, but I don't think I will be finishing the Implied Distribution Model. There are several practical constraints; the biggest being the lack of liquidity in far out of the money options, and significant breaks in implied volatility when switching from the put smile to the call smile. With huge bid ask spreads and a chunky Z=0 switch, the model lost predictability in both extreme return likelihoods, and expected returns.

I still find the idea interesting, and I will be quick to pester future quant side co-workers to see what they use for their distribution assumptions.

For now I'll just keep to drawing it free hand, and just this once, in MS paint.

Think you know Financial Statements?

I'm currently studying for my Financial Statements Analysis class and I'm having more trouble than I thought I would one particular type of question. It has to do with Common Size Analysis, where both the balance sheet and income statement of a company are common sized with respect to revenue. The prof puts a whole bunch of different companies from different industries in a spreadsheet, and you need to figure out which firm matches which common size column in the sheet. Some are easy, but differentiating a tobacco company from a brewery really tests your understanding of how exactly each type of business works.

I've designed an easy version for you to try. It has (in no particular order) a grocery firm, a bank, an auto company, a pharmaceutical company, a oil company, and a telecommunications company. Try it. (Blank means N/A)

I'll put the results at the end of this post in backwards typing. First some tips I've learned from class:

• Look for outliers. Think about why a firms line item might be 0 or very large.
• Group 'like companies', find them on the table and then pull out all the differences and think about which might belong to whom. This is especially important when the table has several companies from the same industry, the above table is much more straight forward. 
• When in doubt to your ratios, D/E, Days A/R A/P and Inventory, ROE decomposition.
• Think about GAAP. Who gets to record R&D, who uses the other assets accounts.

And the results. In backwards type to keep you from getting it at a glance
Firm 1 - knab: knab fo avon aitocs
Firm 2 -sag dna lio: rocnus
Firm 3 - moclelt: tta
Firm 4 - yrecorg: regork
Firm 5 - amrahp: kcrem
Firm 6 -otua: drof

Electronic and Jazz songs you should check out.

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

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.

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.