30 years: Financial markets trader

Standard normal curves and Z-scores are concepts that are often covered at school or college. Abdulla provides a refresher on the subject by outlining an example of a standard normal curve and identifying some useful excel functions to calculate a Z-score.

Standard normal curves and Z-scores are concepts that are often covered at school or college. Abdulla provides a refresher on the subject by outlining an example of a standard normal curve and identifying some useful excel functions to calculate a Z-score.

4 mins 49 secs

Overview

Z-scores are a crucial aspect of understanding probability distributions. The z-score can be defined as the distance from the mean as measured in standard deviations.

Key learning objectives:

Define z-score

Learn how to calculate z-scores

Summary#### What are z-scores?

#### How are z-scores represented on the curve?

#### How do we calculate z-scores?

One thing they all have in common though is that the area of the curve that lies between two equidistant points from the mean is identical. Approximately 68% of the area under the curve lies between -1 and +1 standard deviations, and about 95.00% between -2 and +2 standard deviations.

Given that there are an infinite number of curves and they all have the same characteristics, we can represent all those curves using just one curve, the standard normal curve. In the standard normal curve the x axis scale is in standard deviations. We can look up probabilities in terms of how many standard deviations any chosen point is away from the mean. The mean is zero because the distance of the mean from the mean is zero. The distance from the mean as measured in standard deviations is the z-score.

Here’s a standard normal curve. As we can see, the scale is in z-scores or standard deviations. For any point x we have to determine its z-score. It’s simply x minus the mean divided by the standard deviation. From there we can look up the area of the curve to the left of that point.

Example 1 - Assume the following:

- Mean = 500
- Standard deviation = 40
- X = 520

**z= (520-500)/40 = 0.5**

Example 2 - Assume the following:

- Mean = 500
- Standard deviation = 40
- X = 480

**z= (480-500)/40 = -0.5**

Abdulla’s career in the financial markets started in 1990 when he entered the trading floor of the London International Financial Futures Exchange, LIFFE, and qualified as a pit trader in equity and equity index options. In 1996, Abdulla became a trainer for regulatory qualifications and then for non-exam courses, primarily covering all major financial products.

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