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  1. I was reading some stuff, and I was wondering, how would being able to compute the mean, variance and regression be related to stock prices? What can I actually do with those tools?

    Get a good book on Statistics and be able to compute the usual measures of mean, variance, and regression in it.

  2. When suggesting to buy a stock or not, what would one exactly mean by "Have you counted and tested that?" What could I possibly test/count (using statistical tools)?

    You'll not only have a proper basis for making statements about the relations that matter in markets, but even more important, you'll have a basis for rejecting those that don't. When someone suggests a trade or procedure, always ask the question, "Have you counted and tested that?"

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closed as off topic by Norbert, J. M., Austin Mohr, Micah, Jim Apr 26 '13 at 4:22

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This is a cross-post from Quant-Stackexchange (where it would belong anyway): quant.stackexchange.com/questions/4387/…. There it was down-voted and closed already. –  vonjd Oct 22 '12 at 19:05

1 Answer 1

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  1. Assuming some model for the probability distribution of the value of the stock in the future, you can use historical data to calibrate it. E.g., assuming the price of IBM on March 1, 2013 follows a log-normal distribution, what would the parameters (mean, variance) need to be to make it match some historical data segment.

  2. The discussion is of statistical tests. E.g., if you have a strategy to buy the stock now (at a known price), and sell it on March 1, 2013, you will make money if your sell price is above the purchase price. But your sell price is governed by some probability distribution from (1) so you can make statistical statements about where it will be with what level of confidence. For more info, look up statistical hypothesis testing on the web.

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Thanks. 1. Regarding regression, does it mean being able to compute a regression of variable one vs variable two for stocks/stock markets? Any suggestions for the variables? 2. Thanks, will look it up. –  Richard80 Oct 22 '12 at 18:34
    
@Richard80 I did not mention regression anywhere. There are many methods of calibrating model parameters to historical data. –  gt6989b Oct 22 '12 at 20:44
    
@Richard80 Variable suggestions are a tractate by themselves. Lognormal comes from the Black-Scholes model in literature. Usually we don't model individual variables, but try to capture how these variables will behave over time (i.e. think of modeling all future values of IBM, not just the one for March 1, 2013). That places you in a framework of stochastic processes. Typical models (e.g. Black-Scholes, local volatility, Heston) explore stochastic differential equations. –  gt6989b Oct 22 '12 at 20:47
    
Thanks, guess I will have to research further into this , and try to explore calibrating model parameters to stock data. Do you have any suggestions (point in the right direction) to where I can start reading more about calibrating model parameters to historical stock market data, and also for SDEs, stochastic processes in regards to "modeling all future values of a stock"? –  Richard80 Oct 23 '12 at 13:50
    
@Richard80 SDE as a mathematical object -- look up Bernt Oksendal's book "Stochastic Differential Equations." Modeling finance with such things -- "Financial Calculus: An Introduction to Derivative Pricing" b Baxter & Rennie is a good intuition builder. Another great intro book to the field of options pricing -- "The Mathematics of Financial Derivatives: A Student Introduction" by Wilmott... –  gt6989b Oct 23 '12 at 15:57

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