What Is Z-Score?
Z-score is a statistical measurement that describes a value's relationship to the mean of a group of values. Z-score is measured in terms of standard deviations from the mean. In investing and trading, Z-scores are measures of an instrument's variability and can be used by traders to help determine volatility.
Key Takeaways
- A Z-Score is a statistical measurement of a score's relationship to the mean in a group of scores.
- A Z-score can reveal to a trader if a value is typical for a specified data set or if it is atypical.
- In general, a Z-score of -3.0 to 3.0 suggests that a stock is trading within three standard deviations of its mean.
- Traders have developed many methods that use z-score to identify correlations between trades, trading positions, and evaluate trading strategies.
Understanding Z-Score
Z-score is a statistical measure that quantifies the distance between a data point and the mean of a dataset. It's expressed in terms of standard deviations. It indicates how many standard deviations a data point is from the mean of the distribution.
If a Z-score is 0, it indicates that the data point's score is identical to the mean score. A Z-score of 1.0 would indicate a value that is one standard deviation from the mean. Z-scores may be positive or negative, with a positive value indicating the score is above the mean and a negative score indicating it is below the mean.
The Z-score is sometimes confused with the Altman Z-score, which is calculated using factors taken from a company's financial reports. The Altman Z-score is used to calculate the likelihood that a business will go bankrupt within the range of one to 10 years, while the Z-score can be used to determine how far a stock's return differs from it's average return—and much more.
Fast Fact
Z-score is also known as the standard score.
Z-Score Formula
The statistical formula for a value's z-score is calculated using the following formula:
z = ( x - μ ) / σ
澳洲幸运5官方开奖结果体彩网: Where:
- z = Z-score
- x = the value being evaluated
- μ = the mean
- σ = the standard deviation
How to Calculate Z-Score
Z-Score
Calculating a z-score requires that you first determine the mean and 澳洲幸运5官方开奖结果体彩网:standard deviation of your data. Once you have these figures, you can♒ calculate your z-score. So, assume you have th☂e following variables:
- x = 57
- μ = 52
- σ = 4
You would use the variables in the formula:
- z = ( 57 - 52 ) / 4
- z = 1.25
So, your selected value has a z-score that indicates it is 1.25 standard deviations from the mean.
Spreadsheets
To determine z-score using a spreadsheet, you'll need to input your values and determine the average for the range and the standard deviation. Using the formulas:
=AVERAGE(A2:A7)
=STDEV(A2:A7)
You'll find that the following values have a mean of 12.17 and a standard deviation of 6.4.
A | B | C | |
---|---|---|---|
1 | Factor (x) | Mean (μ) | St. Dev. (σ) |
2 | 3 | 12.17 | 6.4 |
3 | 13 | 12.17 | 6.4 |
4 | 8 | 12.17 | 6.4 |
5 | 21 | 12.17 | 6.4 |
6 | 17 | 12.17 | 6.4 |
7 | 11 | 12.17 | 6.4 |
Using the z-score formula, you can figure out each factor's z-score. Use the following formula in D2, then D3, and so on:
Cell D2 = ( A2 - B2 ) / C2
Cell D3 = ( A3 - B3 ) / C3
A | B | C | D | |
---|---|---|---|---|
1 | Factor (x) | Mean (μ) | St. Dev. (σ) | Z-Score |
2 | 3 | 12.17 | 6.4 | -1.43 |
3 | 13 | 12.17 | 6.4 | 0.13 |
4 | 8 | 12.17 | 6.4 | -0.65 |
5 | 21 | 12.17 | 6.4 | 1.38 |
6 | 17 | 12.17 | 6.4 | 0.75 |
7 | 11 | 12.17 | 6.4 | -0.18 |
How the Z-Score Is Used
In it's most basic form, the z-score allows you determine how far (measured in standard deviations) the returns for the stock you're evaluating are from the mean of a sample of stocks. The average score you have could be the mean of a stock's annual return, the average return of the index it is listed on, or the average return of a selection of stocks you've picked.
Some traders use the z-scores in more advanced evaluation methods, such as weighting each stock's return to use 澳洲幸运5官方开奖结果体彩网:factor investing, where stocks are evaluated based on specific attributes using z-scores and standard deviation. In th🌠e forex markets, traders use z-sco♍res and confidence limits to test the capability of a trading system to generate winning and losing streaks.
Z-Scores vs. Standard Deviation
In most large data sets (assuming a normal distribution of data), 99.7% of values lie between -3 and 3 standard deviations, 95% between -2 and 2 standard deviations, and 68% between -1 and 1 standard deviations.
Standard deviation indicates the amount of 澳洲幸运5官方开奖结果体彩网:variability (or dispersion) within a given data set. For instance, if a sample of normally distributed data had a standard deviation of 3.1, and another had one of 6.3, the model with ♈a standard deviation (SD) of 6.3 is more dispersed and would graph with a lower peak than the sample with an SD of 3.1.
A distribution curve has negative and positive sides, so there are positive and negative standard deviations and z-scores♏. However, this has no relevance to the value itself other than indicating which side of the mean it is on. A negative value means it is on the left of the mean, and a positive value indicates it is on the right.
The z-score shows the number of standard deviations a given data point lies from the mean. So, standard deviation must be calculated first because the z-score uses it to communicate a data point's variability.
What Is Z-Score?
The Z-score is a way to figure out how far away ജa piece of data is from the average of a group, measured in standard deviations. It tells us if a data point is typical or unusual compared to the rest of the group, which is useful for spotting unusual values and comparing data between different groups.
How Is Z-Score Calculated?
The Z-score is calculated by finding the difference between a data point and the avera൩ge of the dataset, then dividing that difference by the standard deviation to see h🙈ow many standard deviations the data point is from the mean.
How Is Z-Score Used in Real Life?
A z-score is used in many real-life applications, such as medical evaluations, test scoring, business decision-making, and investing and trading opportunity measurements. Tr🌱aders that use statistical measures like z-scores to evaluate trading opportunities are called quant traders (quantitative traders).
What Is a Good Z-Score?
The higher (or lower) a z-score is, the further away from the mean the point is. This isn't necessarily good or bad; it merely shows where the data lies in a normally distributed sample. This means it comes down to preference when evaluating an investment or opportunity. For example, some investors use a z-score range of -3.0 to 3.0 because 99.7% of normally distributed data falls in this range, while others might use -1.5 to 1.5 because they prefer scores closer to the mean.
Why Is Z-Score So Important?
A z-score is important because it tells where your data lies in the data distribution. For example, if a z-score is 1.5, it is 1.5 standard deviations away from the meanꦕ. Because 68% of your data lies within one standard deviation (if it is normally distributed), 1.5 might be considered too far from average for your comfort.
The Bottom Line
A z-score is a statistical measurement that tells you how far away from the mean (or average) your datum lies in a normally distributed sample. At its most basic level, investors and traders use quantitative ana🍨lysis methods such as a z-score to determine how a stock performs compared to other stocks or its own historical performance. In more advanced z-score uses, traders weigh investments based on desirable criteria, develop other indicators, or even try to predict the outcome of a trading strategy.
Related Articles
:max_bytes(150000):strip_icc()/Standard-Deviation-ADD-SOURCE-e838b9dcfb89406e836ccad58278f4cd.jpg)
:max_bytes(150000):strip_icc()/credit-card-security-1178047783-ff600a7907ba44e4a0ef413fc92fbedf.jpg)
:max_bytes(150000):strip_icc()/GettyImages-607477465-ae2b32d9776f4f269bbd36fb39ac3962.jpg)
:max_bytes(150000):strip_icc()/Cash_Ratio_Final-0e3a6c15d4c840228a8df7deba91c672.png)
:max_bytes(150000):strip_icc()/GettyImages-1158801022-3724612b7ded4395b850801b27ff12c1.jpg)
:max_bytes(150000):strip_icc()/Variance-TAERM-ADD-Source-464952914f77460a8139dbf20e14f0c0.jpg)
:max_bytes(150000):strip_icc()/144295606-5bfc3d8bc9e77c0026b9791a.jpg)
:max_bytes(150000):strip_icc()/acid-test-ratio-4202141-3x2-final-1-5f096aa45aaa44089789c36aa4a5d661.png)
:max_bytes(150000):strip_icc()/NetProfitMargin_Final_4192396-87840ab825f349d487260e345c0cc95f.jpg)
:max_bytes(150000):strip_icc()/GettyImages-1299599846-a577f02048194b328249035e4cd097ad.jpg)
:max_bytes(150000):strip_icc()/Current_Ratio-100b8e8f3c4e496d965d8dbe0dbfd53d.png)
:max_bytes(150000):strip_icc()/expenseratio-Final-0ec56abb4fde4c30a850007d090f24d0.jpg)
:max_bytes(150000):strip_icc()/Total_Debt_Total_Assets_Final-c0a9f0766f094d77955d0585842eba21.png)
:max_bytes(150000):strip_icc()/Investopedia_InformationRatio_Final-4e1ab60497024938912202865a8a5072.jpg)
:max_bytes(150000):strip_icc()/bank-5bfc2f35c9e77c00263108ea.jpg)
:max_bytes(150000):strip_icc()/DDM_INV_mean_final-c0e58f709b52422ba40cd9bd2728752e.jpg)