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A lot of students think what is the point of studying Brownian motion and stochastic process in a real-world analytics? Especially when you are analysing a business.

So, in this blog post, we will see how this assignment is going to be answered. Are you all ready? Get us to go down!

What is Brownian Motion?

You all know that everything is made up of molecules and stuff, right? In science, the guys believe that these molecules go round and round in a fluid. And that they do this in a totally unpredictable manner. You cannot judge where this molecule is going to be the next second. This happens because every molecule is hitting each other.

This type of movement is called Brownian motion.

How is Brownian Motion Related To Business Analytics?

It is assumed that the market also works in a similar fashion. The share prices and other business parameters are assumed that they move up and down irregularly.

But to use Brownian motion in financial and business analysis, there are two assumptions that are made –

  1. The assets of the company are perfectly divisible. This means that the assets of the organisation can be perfectly distributed among the shareholders.

  2. The market has to be frictionless. This means that the organisation does not bear any transaction cost, either in buying or selling.

  3. The last is that there is no jump in the asset’s prices, i.e. there are no surprises in the market.

Stochastic Modelling for Share Prices

The investors say that stock prices are one of the most volatile components of the stock market. The constant fear of the stock prices to change without a warning, that too frequently, makes the investors call for concern.

The researchers then take a dive into the behaviour of an unstable market. They then advise the investors on convenient ways of raising money by issuing shares of stocks.

Scottish botanist Robert Brown gave a theory of random motion. On the basis of that, a stochastic model was developed.

That’s how he must feel every time his name is taken.

A stochastic model is used to calculate the probability distribution of the possible outcomes when the variables are modified. Where do you get this random variation from? From historical data! All the collected and derived information is fed to what is called a simulation running on stochastic projections. The projections show variation in the input. On the basis of this, the possible results are seen.

If you have heard about what is called an Efficient Market Theory, you would know that it is assumed that the stock prices of any organisation show everything about it. If this is true, the stock prices can be predicted using fundamental analysis.

The world is more advanced from the one few years ago. The financial engineering principles (yes, it is a thing) has revamped the way we see a financial model. We now use Monte Carlo simulation, sample path simulation. The stochastic differential equations are now being referred to study the variation in the stock prices.

There are many authors who have found that the stock prices show a Brownian motion trait. This trait is of randomness. This is because both, microeconomic and macroeconomic parameters also play a role in the stock prices. A few factors can be –

  • Firm’s book value, i.e. the value of the firm as per the ledgers maintained

  • Dividend per share, i.e. the sum of all the dividends declared by the organisation for outstanding shares

  • Earning per share, i.e how much is the earning of every outstanding share of the company’s common stock.

  • Dividend cover, i.e. the number of times a company can pay dividends to the shareholders.

Real World Analytics Help at TutorVersal

The assignments are more detailed than what was discussed in the blog post above. We cannot tell you everything right here, yes? So why don’t you do one thing. Reach out to our business analytics assignment help experts for a personalised session.

Or if you want detailed SIT718 assignment answers or SIT718 assessment questions solved, you can order your assignment and we will assist you all we can!

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