How to analyze data( NIFTY50)
chartSeries(Cl(NSEI))
> ret <- dailyReturn(Cl(NSEI), type='log')
Warning message:
In to_period(xx, period = on.opts[[period]], ...) :
missing values removed from data
> par(mfrow=c(2,2))
> acf(ret, main="Return ACF");
> pacf(ret, main="Return PACF");
> acf(ret^2, main="Squared return ACF");
> pacf(ret^2, main="Squared return PACF")
> par(mfrow=c(1,1))
> m=mean(ret);s=sd(ret);
> par(mfrow=c(1,2))
> hist(ret, nclass=40, freq=FALSE, main='Return histogram');curve(dnorm(x,
+ mean=m,sd=s), from = -0.3, to = 0.2, add=TRUE, col="red")
> plot(density(ret), main='Return empirical distribution');curve(dnorm(x,
+ mean=m,sd=s), from = -0.3, to = 0.2, add=TRUE, col="red")
> par(mfrow=c(1,1))
> kurtosis(ret)
[1] 12.6355
attr(,"method")
[1] "excess"
> plot(density(ret), main='Return EDF - upper tail', xlim = c(0.1, 0.2),
+ ylim=c(0,2));
> curve(dnorm(x, mean=m,sd=s), from = -0.3, to = 0.2, add=TRUE, col="red")
> plot(density(ret), xlim=c(-5*s,5*s),log='y', main='Density on log-scale')
Warning message:
In xy.coords(x, y, xlabel, ylabel, log) :
75 y values <= 0 omitted from logarithmic plot
> curve(dnorm(x, mean=m,sd=s), from=-5*s, to=5*s, log="y", add=TRUE,
+ col="red")
> qqnorm(ret);qqline(ret);
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Analyzing time series data for NIFTY50 involves examining historical price movements to identify trends, seasonality, and patterns. By applying statistical methods and visualization techniques, investors can make informed decisions based on past performance and potential future movements. Utilizing tools like moving averages and ARIMA models can enhance the analysis.
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To analyze time series data for NIFTY50, you can follow these steps:
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2. **Preprocessing**: Clean the data by handling missing values, outliers, or irrelevant information.
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The article explains how to analyze time series data, specifically focusing on the NIFTY50 stock index. It covers steps like data retrieval, using tools such as R to calculate returns, visualize autocorrelations (ACF, PACF), and assess data distributions with histograms and density plots. The goal is to identify trends, seasonality, and other patterns for making informed financial decisions.
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