1) What other ideas we can work on in those fields?
Find some problem that you are passionate about, will learn something from by tackling it, and is within the scope of your time, effort, and ability. Projects like this are relevant not only for grad school but also when applying for entry-level jobs (even if a few years off still after doing a masters degree)l. It helps to pick something you can put on a resume that shows your level of accomplishment and ability to complete a task.
2) How much will my choice of graduation project affect my application for a masters degree?
The topic choice probably won't matter significantly except perhaps for top-tier programs or if you have notable weaknesses in other admissions criteria. If the latter is true, then a good project may help, but even the latter is uncertain. Masters program admissions I think is generally handled by administrative staff, so they are probably more interested in whether or not you did a project than what the topic is.
3) Is a stocks price prediction expert system too advanced for us?
Yes, a stock price prediction system is far too difficult if you want a system that actually can work reasonably well over anything other than a small training data set.
The market is neither a natural system, a machine, nor even a system of rational collective behavior. Its pricing mechanism is in general irrational: investors/traders may make transactions at prices that are reasonable for them relative to their own decision criteria, but the market as a whole is generally not rational. The market is more an aggregation of behavior rather than collective behavior.
The above alone would make for an intensively difficult problem to solve with AI methods, but beyond that there are issues of problem scale, the amount of training data which is needed, etc.
There are of course a large number of Wall Street trading firms using quantitative methods for high-frequency trading, etc. They are effective, however, because they are focused on narrow problems (price trends over the next few seconds-to-minutes in highly-liquid stocks, S&P index futures, etc.), they put a lot of work into their models and generally are constantly rebuilding the latter on a daily/weekly basis, and they understand the market's nature, i.e., it's largely irrational as a whole and is a competitive, shifting landscape of exploiting the pricing inefficiencies inherent to large money flows.
I would only recommend this problem domain if you have an intense personal interest in financial markets and have already spent a lot of time studying them, are prepared to fail, and are interested in learning a lot. Trying to work on this problem is certainly a good learning opportunity, but it will be hard to achieve any real success except for small problems unless you have many years to devote.