Assuming returns are normally distributed, market risk managers can use characteristics of the normal distribution to quantify market risk. This relates specifically to areas under the normal curve. For example, if this normally distributed bell-shaped curve are the returns for a stock for a specific time period, say one day, then X represents the daily mean or average daily return. The more to the right, the higher the return. The tallest bar is the average return around which the normal distribution is clustered. A risk manager might want to know the probability that on a given day the mean daily return will be greater than one standard deviation below the mean. This is the area to the left of the black vertical line. This can easily be done in Excel using the following function. Norm.S.DIST, this replaces the need for using normal distribution tables to find the area under the curve. The first input is how many standard deviations we need to go from the mean. In this example one, TRUE means we are using cumulative normal function moving from the far left of the curve and totaling the area under the curve to the black vertical line giving the probability of the left hand side tail. Norm.S.INV, this is the reverse of the Norm.S.DIST function. This answers how many standard deviations from the mean you would need to go to get a specified area beneath the curve. In this example you'd need to go one standard deviation away from the mean to have a 15.87% chance of giving the returns in the remainder of the curve.