Okay, full disclosure, I don't have any real evidence that ladies "love" simulation or stochastic calculus. But they probably do, I mean, who doesn't?!? Amirite!
At any rate, for those of you who are new to stochastic calculus and Monte Carlo methods, hopefully this will serve as a starting point. Because of the varying levels of mathematical backgrounds here on WSO, I'll try to keep it simple, very simple, this is tricky stuff from a theoretical standpoint, and I'm not smart enough to explain it with any rigor.
Monte Carlo simulation is particularly useful in the modeling of stock price movements, and is especially flexible for practitioners. One of it's primary advantages is that it allows for the random "wiggles" seen in a stock's price movements. These wiggles are caused by what is called "Brownian Motion" (or, for you math guys, a Wiener process), and for a stock's price in particular, "Geometric Brownian Motion" (which does not allow for negative values). Now, if a stock's price is to follow Geometric Brownian Motion, it must satisfy the stochastic differential equation:
dSt = μSt + σStdWt
with Wiener process Wt, and constant drift μ and volatility σ
Then, blah, blah, blah, Ito's lemma, yadda, yadda, don't worry about it (or just read the wikipedia page) and we end up with the following relationship:
St = S0e(μ - 1/2 σ2) t + σWt
I've probably created more questions then I've answered at this point, but bear with me as I get into what each of these pieces mean from a practical standpoint.
Your step size, t, denotes the size of a given step between iterations of the simulation (for our purposes, t represents a fraction of a year). You will have to modify μ and σ by the step size (i.e. μt and σ√t) if they're not in terms of t already (i.e. step size of one day and annual σ). μ and σ are drift and volatility, and while I'm sure most all of you are familiar with volatility, drift may still be something of a mystery. Drift is effectively your rate of return on whatever you are modeling. Put another way, drift is the general direction your assuming the stock will go. How you calculate drift is a whole other question entirely, and if you're unsure, you should consider looking into financial modeling as it's likely much more relevant.
Wt is your Wiener process. This is what causes your "wiggles" in the model. Within the confines of stochastic calculus, this is a lognormal or Gaussian random variable but, the reality is that since we're not concerned with theoretical issues (i.e. that the Wiener process is an almost surely continuous martingale), you can feel free to use whatever distribution you prefer (warning: you can't do this with abandon as you'll have to alter how drift and volatility will interact, also, you may give your neighborhood quant a heart attack; also there's already methods that allow you to do this like the Datar-Mathews method).
Let's get into some mechanics so you can see how this works in a more concrete fashion. Assume you have a stock with a constant drift (μ) of 0.5 and volatility(σ) 0.2, a starting price (S0) of 10, and because I'm lazy, assume drift and volatility are in terms of t:
S1 = 10 e(0.5 - 0.5 * 0.22) + 0.2 * W1
Since W is a random process, let's randomly call it 1 (typically, you'd use a random number generator for the Gaussian distribution). Now we have:
S1 = 10 e(0.5 - 0.5 * 0.22) + 0.2 * 1 = 19.74
We can now run this through several iterations using random numbers W2 = 0.5, W3 = -0.5, and W4 = -3.
S2 = 19.74 e(0.5 - 0.5 * 0.22) + 0.2 * 0.5 = 35.25
S3 = 35.25 e(0.5 - 0.5 * 0.22) + 0.2 * -0.5 = 51.55
S4 = 51.55 e(0.5 - 0.5 * 0.22) + 0.2 * -3 = 45.72
Obviously, a good Monte Carlo simulation runs thousands and thousands of these price paths to even begin to be usable. But, this should suffice for a very basic understanding of the concept.
Hopefully this has been helpful and/or interesting. These types of models occupy a wide range and seemingly static parts of the model, like volatility, can in fact be turned into stochastic processes themselves. Some of the pros on this site can go in depth into ARCH & GARCH stochastic volatility models which are definitely interesting and widely used.
Needless to say, there's a lot going on here, and if you like modeling, this is good stuff to know but be warned, this is a very small introduction and while it'll work as starting point, I'm sure I've missed some details. So, make sure you brush up before you try picking up any chicks. Remember, ladies love it (probably) so don't screw it up.
Note to the ladies: I have no idea if "the fellas love it." Sorry, and between you and me, I'm actually kinda "iffy" on what you ladies like, too.