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Welcome to the stockanalytics wiki!
- Default R script:
mean(x)
- Default R script:
median(x)
orquantile(x, 0.5)
Rarely used in Finance, but can be referred as the 'height' of distribution, representing the most frequent observation value.
- Default R script:
mode(x)
- Default R script:
sd()
- Advanced R script:
apply(x,2, function(col) sd(col))
Comparing to previous statistical measures, skewness can be applied to only one set at the time. However, this functional limitation is possible to overcome without any libraries using apply()
. Otherwise, try timeSeries library with colSkewness()
.
- Default R script:
skewness()
- fBasics library:
colSkewness()
- Advanced R script:
apply(x,2, function(col) skewness(col))
Same story as with Skewness.
- Default R script:
kurtosis()
- fBasics library:
colKurtosis()
- Advanced R script:
apply(x,2, function(col) kurtosis(col))
R code: cor()
R code: summary(lm(formula = y ~ x)
Mostly used by economists.
- R script:
jarqueberaTest()
orjbTest()
- R script:
shapiroTest()
- My R script: https://github.com/vladislavpyatnitskiy/stockanalytics/blob/main/Normality%20Tests/Shapiro-Wilk%20Test.R
Often used in Life Sciences.
- R script:
ksnormTest()
Here are represented scripts to calculate ratios that enable to assess performance of the financial instruments.
The most popular ratio to assess asset and portfolio performance. It is a ratio of market premium (Rp - Rf, difference between asset/portfolio return and risk free rate) and standard deviation of the asset/portfolio. The higher Sharpe, the better the performance has been for the selected period.
Alternative to Sharpe ratio, where denominator is beta of the asset/portfolio.
The least popular ratio due to its complexity to calculate.
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