Quant’s and Probability

I am studying financial engineering and there has been one topic that has been bothering me; Probability.

Here is a question I found at the quant forum.

If a family has two children and there is a boy in the family, what is the probability that there is a girl?

What’s the answer? 2/3.

Or how about coin flipping?

What’s the probability of getting 5 heads in a row before getting 2 tails in a row? fairness is assumed.

What’s the answer? 3/34.

I don’t agree with the answers. Ok, let me reiterate this clearly I don’t agree with the mathematics that came up with the answer. To me calculating the odds using this type of probability is nothing more than a mental game.

Here are some interesting facts. With respect to the children did you know that there is a bias towards having more males born than female? And with respect to the coin flipping did you know that there is a bias towards the coin landing on the same face that it started? If you read the article there is 5% more males than females and 1% bias towards a coin flip.

I even argue if you could perform the coin flipping experiment in a vaccum under controlled circumstances then the 50/50 probability theory would fall flat on its face. What you should notice is that often when problems involving coin tosses are created they say the coin toss is fair. In other words you are jigging the conditions to get the response you want. Very few times in reality have I been able to say “fair conditions.”

I am a bigger fan of the scientific method and statistical probability. The math differences between theoretical probability and statistical probability is quite a bit. With statistical probability your main concern is about figuring out whether or not your experiments are consistent and whether or not they have a bias in them.

I especially like the following comment from the scientific method HTML page:

Statistics: How much of a difference is really a difference? If you flipped a coin 100 times, it should turn up heads half the time and tails the other half. However, it seldom actually turns out this way. If a person claimed to have psychokinesis (the ability of the mind to directly effect matter) and attempts to use the power of their mind to control the toss of a coin to make it turn up heads, would you conclude that they really could control the coin toss if they tossed it 100 times and it came up heads 51 times and tails only 49? What if it turned up heads 60 times and tails 40 times? What if it turned up heads 90 times and tails only 10? All of these outcomes could occur by chance. To determine what that chance is, scientist use statistics.

If something can occur one time in 55 million, it probably will. This is the probability that a person will win the Power Ball. Over time, one ticket in 55 million will win the Power Ball. If a scientist is conducting an experiment and the results can turn out a certain way one time in 55 million, it just might occur. Repeating the experiment will show if the results you obtained were just lucky results. Statistics will also show this.

For the past several months I have been writing my own trading software and I very quickly realized what works and did not work. I found it out by trial and error, but much of what I learned by stumbling around is written in the book Evidence-Based Technical Analysis.

The book helped me express my thoughts in my software in a structured manner. I knew that probability and the classical mathematics were not working. And I knew that I had to use statistics, but I kept bumping into a wall when I tried to put everything together. Reading this book with the following other books will help you get an overview of the maths; Options, Futures, and Other Derivatives, and Inside Volatility Arbitrage.

I recommend Evidence-Based Technical Analysis for anybody who is writing software that helps you trade.

Hello There Mr Roboto!
(the song and era says it all... http://www.devspace.com)

Friday, Jan. 26, 2007 by Christian

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3 Comments Add your ownSubscribe

  • 1. Phil  |  January 27th, 2007 at 4:24 pm

    Financial engineering is all about mental games!

    Nice link to the coin tossing article. Problem though - the face that will be face up is random. Bias gone… unless you use a penny it seems.

    :)

    Phil

  • 2. crni  |  January 28th, 2007 at 7:46 pm

    You make a valid point, but I think it’s essentially the GIGO principle.

    The math examples that you gave are supposed to teach about how to use the values for the probability of single events to calculate cumulative probabilities, nothing more. The probabilities are assumed to be known. In real life, of course the probabilities are not known, they are assessed. But that does not make the principle of cumulative probability or the examples that illustrate it any less valid.

    As far as the scientific method is concerned, I am sure it can be useful. However, one needs to be aware of certain things, like the assumptions made in the scientific model - in your case I presume the Black-Sholes model. The conditions under which it is valid are rigorously defined and hold for some sort of perfect market. I view it all like the ideal gas equation - it’s not really valid most of the time, but it gives good approximations under certain conditions. The real danger lies inplugging the numbers wihtout being aware of those conditions all the time.

  • 3. Christian Gross  |  January 29th, 2007 at 4:44 am

    My beef is that while the probabilities are supposed to teach some basic principles they are not really teaching real life scenarios.

    Jason referenced a very interesting article that I read at lengths which summed up my problem with such probability arguments http://www.straightdope.com/classics/a3_189.html. The original Monty and which door argument boils down into, well, yeah, what if, this or that.

    If we have to teach probability then I would like it to be taught in the context of a computer. Talk about randomizing and then creating the algorithms to illustrate what the probabilities are. That way you can plug in real life found statistics and see what the probability actually is. Students have the power to simulate situations and scenarios that are nearly impossible to calculate using continous maths.

    The Black-Sholes model is not really what I had in mind. I don’t like to use Black-Sholes because it makes too many assumptions and ignores the volatility smile. The Black-Sholes model also reminds me of the era where we did not have computing power. I tend to prefer discrete math like binomial, tri-nomial or jump pricing of options. Granted they make some assumptions, but at least you could simulate conditions and tweak the tree to test other thesis.

    I guess what bugs me is the same thing that bugs people like Wolfram (A New Kind of Science) in that we are using continous mathematics that makes many assumptions. We have the power of computers so why not base our maths on discrete mathes that are more accurate and can be used to simulate? This is why I find the book Evidence Based Technical Analysis absolutely amazing.

    For those wondering what discrete and continous math is read the wikipedia entry: http://en.wikipedia.org/wiki/Discrete_math.

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