If you are serious about testing your model, I strongly encourage you do so some academic quality reading on financial engineering. Each of the suggestions above have significant pitfalls that it would behoove you to be aware of.
Monte Carlo simulation is a very popular technique for modelling prices, but it is important to remember that the modelling is always done in conjunction with an underlying pricing model. It can only simulate market behavior if the underlying pricing model is, in fact, a reasonably accurate model of the market. It is often used in financial modeling because market pricing models often involve extremely complex differential equations that are hard to evaluate analytically.
Historical data also has difficulties. The past is not the future and the past is not consistent. Over the last 100 years there have been periods of slow steady rise, sudden falls and jumps. Volatility, short term moving averages (7 years or so), and many other measures of market behavior change in statistically significant ways over time. Models based on historical data often are very sensitive to the time range of data studied. For example, the 30 year moving average is far more steady than the 7 year moving average. The longer time period takes into account one or more full business cycles, whereas shorter time frames might catch just a small part of a single cycle.
I'd also be especially careful about intuitive theories like "markets rise half as fast as they fall" or "we've reached a psychological price point and the market (or stock) will hold steady". I used to hear a lot of that sort of thing on CNBC during the height of the dot.com boom. The problem with those models is that the human eye is a phenomenal pattern maker. Random processes are a lot like coastlines - they seem to go in a defined direction when you look at them up close. Only when you step back and take the aerial view can you see that the raggedness is random. To emphasize this point, one of my biz school professors began his introductory lecture by showing a small bit of a randomly generated curve. Nearly everyone assumed some sort of pattern. Then he flashed the entire series on the board was it clear that the overall picture was simply a bunch of noise.
To discern genuine patterns you'll need to apply some fancy statistics and mathematics to rule out the noise. The researchers that have done this do find strong evidence that markets are responsive to both rational judgments and collective emotions. However, the exact relationship is a matter of much debate among researchers and theorists. Also there are some interesting timing issues. For example, the well-publicized financial report announcements for large companies generally have a much smaller impact on stock prices than do reports for small public companies. It is theorized that the market already knows so much about the Fortune 500 companies that they don't really consider the reports as news. On the other hand, small companies only get attention when on announcement day so their reports have a disproportionate impact on stock prices.
Finally, markets aren't uniform in their behavior even at a single point in history. Each stock in the market can be categorized according to its betas - the real world factors (for example, weather, interest rates, unemployment, commodity prices) whose changes correlate with changes in stock prices. Your model may need to take that into account as well.
Best, beth
In reply to Re: Generating Stock Price Data
by ELISHEVA
in thread Generating Stock Price Data
by TeraMarv
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