Stochastic modeling predicts the likelihood of different outcomes under different conditions using random variables.
Stochastic modeling represents data and predicts outcomes given certain levels of unpredictability or randomness.
In the financial services sector, planners, analysts and portfolio managers use stochastic modeling to manage their assets and liabilities and optimize their portfolios.
The opposite of stochastic modeling is deterministic modeling, which gives the same exact results every time for a certain set of input data.
Monte Carlo simulation is one example of a stochastic model; it can model the behavior of a portfolio based on the probability distribution of returns on individual stocks.
Performance Based Management (ABM) is a means of analyzing a company’s profitability by looking at every aspect of its business to determine its strengths and weaknesses.
A ballpark figure is a rough estimate of what something might mean in numerical terms when a more precise number is estimated, such as the cost of a product.
The binomial distribution is a probability distribution that generalizes the probability that a value will take on one of two independent values given a set of parameters or assumptions.
Share capital is the number of ordinary and preferred shares that the company has the right to issue and which are accounted for on the balance sheet as part of share capital.
The Central Limit Theorem (CLT) states that the distribution of sample means approaches a normal distribution as the sample size increases, regardless of the distribution of the population.