Creating Accurate Rolling Performance Charts for ETF Returns in R

Understanding the Rolling Performance Chart in R

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In this article, we will delve into the world of financial data analysis using R. We will explore how to create a rolling performance chart for ETF returns and discuss common pitfalls that can lead to incorrect results.

Introduction to Rolling Performance Charts

A rolling performance chart is a type of chart used to visualize the performance of an investment over time. It typically shows the return on investment (ROI) or return per unit invested (RPU) over a specified period, such as 1 year, 3 years, or 5 years.

Reading in Financial Data

To create a rolling performance chart, we first need to read in our financial data. In this case, we have four ETFs: VOO, QQQ, XBI, and VYM. We use the getSymbols() function from the xts package to retrieve their historical prices.

mystocks <- new.env(hash = TRUE)
getSymbols(c("QQQ", "XBI", "VYM", "VOO"), env = mystocks, from = "2016-01-04", to = "2020-10-22")

Next, we calculate the return on investment for each ETF using the Return.calculate() function.

etf <- do.call(cbind, apply(mystocks, 2, Cl))
str(etf)
head(etf)

The resulting etf object is an xts object containing the historical prices of the four ETFs. We can convert it to a data frame using the as.data.frame() function.

Creating the Rolling Performance Chart

Now that we have our financial data, we can create the rolling performance chart. However, there’s a common pitfall when creating such charts: annualizing returns using only 12 days of data. This can lead to incorrect results, especially when dealing with high-frequency data.

The default value for width in charts.RollingPerformance() is set to 12, which means that the chart will show the performance over a rolling window of 12 business days. However, if we use this setting with annualized returns, we can get results greater than 100% at some point. To avoid this, we need to adjust our width parameter.

Adjusting the Width Parameter

The correct way to create a rolling performance chart is by using a width parameter of 252 business days, which corresponds to roughly one year.

charts.RollingPerformance(R = etf_returns_discrete,
                          width = 252,
                          Rf = 0,
                          "Rolling 12-Month Performance")

By adjusting the width parameter, we can ensure that our rolling performance chart accurately reflects the return on investment over time.

Conclusion

In this article, we explored how to create a rolling performance chart for ETF returns in R. We discussed common pitfalls that can lead to incorrect results and provided an example of how to adjust the width parameter to get accurate results. By following these steps and using the correct parameters, you can create informative rolling performance charts that help you make better investment decisions.

Additional Resources

For further information on financial data analysis in R, we recommend checking out the following resources:

The xts package documentation: https://cran.r-project.org/package=xts
The Return.calculate() function documentation: https://www.rstudio.com/blog/2019/06/return-calculate/
The charts.RollingPerformance() function documentation: https://www.rstudio.com/blog/2018/11/rolling-performance-plot/

Commonly Asked Questions

Q: How do I create a rolling performance chart for daily returns?

A: To create a rolling performance chart for daily returns, you need to adjust the width parameter in the charts.RollingPerformance() function. A common setting is width = 252, which corresponds to roughly one year.

Q: Why am I getting incorrect results with my rolling performance chart?

A: Incorrect results can occur when using only 12 days of data to annualize returns, especially with high-frequency data. Adjusting the width parameter can help avoid this issue.

Q: What is the difference between return on investment (ROI) and return per unit invested (RPU)?

A: ROI measures the gain in value of an investment over time, while RPU measures the percentage increase in value for each unit invested. Both metrics are important when evaluating investment performance.

Last modified on 2023-11-01