Quote of the Day 2009-6-30

If the results disagree with informed opinion, do not admit a simple logical interpretation, and do not show up clearly in a graphical presentation, they are probably wrong. There is no magic about numerical methods, and many ways in which they can break down. They are a valuable aid to the interpretation of data, not sausage machines automatically transforming bodies of numbers into packets of scientific fact.

-FHC Marriott

Interview Tips

A hodgepodge of interview tips from sources in statistics, finance, and Jeff Kimball:

-Don’t be late
-Don’t be early. Get there early, go to a cafe and drink some lemonade, then show up dead on time.
-Eat a good meal beforehand.
-Don’t argue with the interviewer about why they’ve asked you something. They asked because they want to know if you can do it.
-Appear enthusiastic
-Wear a suit
-Be eager to please. They want someone who will do what they want, someone obliging > someone who is difficult
-Don’t be too relaxed, show some ambition
-Don’t tell them that C++ and Excel are stupid because your favorite niche product is better
-Demonstrate interest in the company, the industry, and the field
-Prepare 2 minutes to every line on your resume. Don’t say anything negative.
-Don’t claim experience you don’t have or education you don’t understand. More interviewers are testing your knowledge these days. Nothing is more embarrassing than a supposed genius who doesn’t know the integral of log x.
-Be polite
-Ask for feedback and don’t argue about the results. If they misunderstood you, then figure out why.
-Don’t say you want to work for the money. Most of the time it’s true (especially if you work in banking or finance), but it’s considered bad form to say it out loud.
-Say you’d rather work closely with other people than alone
-Take a break from interviewing and take more time to prep if more than a couple interviews go badly.
-Ask about the group you’ll be working with. You want to know about turnover, where people go when they leave, how old the group is, what they do together, if it’s expanding or contracting, and if you can meet them. What is a typical day like?

The general consensus is that yours is a difficult field to get into, but most hiring managers usually say they interview lots of candidates and most are discarded as awful. It is almost never the case that a hiring manager is choosing between two good candidates, it’s more common to be relieved to finally find someone who’s good enough. The moral is that most candidates fail to reach the requisite level rather than other candidates are fighting you to the death on paper and only one can survive.

The layout of financial jobs

Just for kicks, here is a brief layout of financial employers.

-Commercial banks (i.e. B of A, Citibank)

Commercial banks tend to ask less of their employees, pay less, and give them less interesting work. The plus side is that they offer better job security and there’s more room for shifting around the banking industry to find a better fit.

-Investment banks (i.e. Goldman Sachs, Morgan Stanley)

Investment banks are considered the gold-plated jobs in the financial industry. They pay extremely well and offer some of the most interesting work in the industry. However, they also demand the most of their employees and aren’t afraid to fire anyone who isn’t top quality. Expect to work 15+ hour days and get laid off if you have a bad week.

-Hedge funds

Hedge funds are the cowboys of the financial industry. It is a very volatile industry and most employees can expect to be looking for another job within 6 months. In the meantime, they can make an obscene amount of money. And/Or be investigated by the SEC.

-Accounting firms

Accounting firms often have consulting or financial arms that offer work. Like most accounting jobs, they pay well, have good job security, and are often willing to pay for continuing education. The downside is that you are far from the action and since the top notch are in the previous three areas, it’s difficult to find someone good to learn from.

-Software companies

With a growing focus on complex mathematical models and thus increasingly fewer people who understand finance, statistics, economics, mathematics, and computer programming at the requisite level, financial companies have been outsourcing their computational needs to software companies. They have the same qualities as accounting firms. Software companies also tend to be small so there is even less room for upward movement.

The rule of thumb is that the closer you are to the money changing hands, the better and more interesting the job. Naturally, being close to the action necessarily means the job is very intense and employers demand a lot in time and quality. It should be noted that starting salary and bonuses are not a good measure of the quality of a job, at least not compared to the type of experience one gets, upward mobility, turnover, and the quality of the team. In the financial industry, if you’ve been in the same place for more than 2-3 years and haven’t moved up, you’ve been pigeon-holed and are in danger of stagnating.

Kalbi jjim recipe

Here is a recipe for Korean beef stew. It is wide cut short ribs that are slow cooked so that the meat is soft enough to melt off the bone. Make sure to make a little too much, because leftover stew tastes really good as the sauce continues to reduce and the flavor gets richer. But that’s pretty hard because the stew will go really fast.

Kalbi Jjim

3.5 lb bone-in beef short ribs (1 package from a Korean market of short ribs for stew)
tap water to fill a large pot
2 onions, chopped
4 cloves minced garlic
1 chopped pear or apple (or 2.5 tbsp corn syrup or apple/pineapple juice)
5 tbsp sugar
4.5 cups water
2/3 cup soy sauce
2.5 tbsp sesame oil
3 carrots peeled and chopped
2 potatoes peeled and chopped
salt and pepper to taste

1. Add the ribs to a large pot and fill with tap water. Let the ribs soak for an hour to remove blood. Drain ribs and discard the water.
2. Add the onion, garlic, pear, sugar, water, soy sauce and sesame oil to the pot with the ribs.
3. Bring to a low boil on medium high heat, then reduce to a simmer and heat for 2.5 hours. Stir and skim off any accumulated fat off the top every half hour.
4. Add the carrots and potatoes and simmer for another half hour, or longer if necessary to make them soft.
5. Portion out scoops of meat, vegetables, and sauce, but don’t eat the sauce alone. Add salt and pepper to taste before serving. You can also top with sesame seeds.

Keep your leftovers in the pot with a cover. The fat will congeal over everything, but if you heat it for a few minutes on medium-low heat, it will look normal again.

Lessons Learned 2009-6-14

Another exciting week, time to review those lessons. In honor of my own graduation, I’ll make it a stat-centric post.

1) Statistics is the core of modern science

Statistics tells you whether something is true, false, or merely anecdotal; it draws the line between a habit and random luck. It is the instrument that defines risk and its probabilistic consequences. Like all tools, however, its usefulness is measured by the skill of the practitioner. We unfortunately live in an age where it is very easy to fool people with statistics, because very few people understand the subject. You either have the mathematicians who leave it to a politician to translate the results into plain language or you have an analyst who can’t do the math so they just punch in numbers until they get a result that looks good.

2) The primary question of risk is consequence

It seems obvious on an intuitive level, but people often make mistakes applying this to the real world (i.e. modern finance). When you take risks, the only thought should be of the consequences. In finance, this is called leverage. In some situations, a single error can blow up in your face if you are improperly leveraged. An example is putting all of your money in one stock. In others, you can be totally wrong and face no consequences at all. An example is paying too much for parking.

3) Beware regression. Especially multiple regression

Science has fallen in love with correlation. It’s easy to do, easy to understand, and draws a single convenient line through a very messy cloud of data. The problem is that even very close relationships that you can find in research rarely if ever replicates itself in real life. This is especially true if you’re dealing with the behavior of human beings, such as in psychology, economics, or politics. The other problem is that correlation is a single number that tells you only that you have two relationships that seem to move together. Going from that single number to a policy recommendation that costs millions of dollars and affects many people’s lives (through treatment, policy, etc.) is a leap over a very large abyss. Your correlation number tells you nothing about causation, randomness, or hidden risk.

Just remember, you can find correlations between anything if you put enough variables in. That doesn’t tell you anything meaningful.

4) Know what you don’t know

The biggest problem with students and the ignorant is that they roll their eyes when you tell them about the unknown. They think that if you’re not giving them information, then they are learning nothing. People who know what they’re doing value this immensely. Every other episode of House highlights this.

5) Watch out for charlatans and survival bias

Survival bias is one of the most pervasive and dangerous consequences of statistics. This is how you get trite life tips like “all you have to do is work hard” or “volunteering looks good on your applications”. Most people with “how to” tips are charlatans and owe just as much of their success to luck as they do to skill or knowledge. Thousands of people try every year to follow the advice of someone on how to make their first million by the age of 24 and almost all of them fail (and the ones who succeed usually didn’t follow their advice at all). This is survival bias, that we follow the advice of survivors and assume that their way must be right because they made it. What you don’t see in bookstores is business books with titles like “How I failed in business and in life”. Similarly, academics publish results as though the experiment should run like silk. You never get a “this is how I wasted the last ten years of my life and got nothing” paper, even though it would be vastly more informational and truthful.

Goals from the week:
1) Place was cleaned enough for Yoko and Patrick
2) Ran 5 miles this week, far short of the 15 mile goal
3) Graduated and it was sweet
4) Sent out 22 resumes

Verdict: Getting lazy on the exercise. Fix it.

Quote of the Day 2009-6-14

When someone discovers you are writing a textbook, one (or both) of two questions will be asked. The first is “Why are you writing a book?” and the second is “How is your book different from what’s out there?” The first question is easy to answer. You are writing a book because you are not entirely satisfied with the available texts. The second is harder to answer. The answer can’t be put in a few sentences so, in order not to bore your audience, you try to say something quick and witty. It usually doesn’t work.

-George Casella

Lessons Learned 2009-6-7

What did I learn this week?

1) Being idle is not the same as resting

I spent most of the week doing nothing and you know what? I don’t feel any more rested, if anything I feel tired and lazy now. It’s kind of dumb that I have to remind myself, but relaxation means doing a reduced load and just trying to have fun. Doing nothing really does nothing.

2) Job searching sucks

Nuff said.

3) Good discipline is a matter of structure, ambition, and drive

I need all three in good supply. I’ve been living with two at any given time for a while. This will definitely change.

4) Attention to detail – it separates perfect from merely good

I’ve been doing well. I need to do better.

Goals from the week:
1) Place is cleaned up but not complete
2) Got programs designed, will implement next week
3) Prepped for finals, procrastinating on papers
4) Started job-searching, it is soul crushing

Verdict: Pretty good, but a few incompletes