Computing Portfolio Returns: A Comprehensive Guide Using R and Performance Analytics

As we delve into the world of portfolio analysis, it becomes apparent that computing returns over multiple periods is essential for understanding the performance of an investment. In practice, this requires computing returns for many periods, with the longer the history of returns, the more information we have about the underlying portfolio performance. Real-life analysis of portfolio returns necessitates a loop over different dates, and this is where the Performance Analytics package in R comes into play.

The Performance Analytics package has been written by two quants from the city of Chicago, Peter Karl and Brian Peterson, making it an ideal choice for analyzing portfolio returns. The package offers a straightforward approach to computing portfolio returns using two functions: `return` and `returnPortfolio`. The `return` function is used to calculate returns, while the `returnPortfolio` function computes the time series of portfolio returns.

The primary argument for the `return` function is a time series of n of P prices of the different investments. This should come as an object of the XTS time series class, meaning that it represents a rose or art and time (year-year year-month month-day day). By default, the function transforms the time steals of prices into time C love returns, where each row has a percentage change in value over a period. It is worth noting that the first row of the obtained return data consists of an ace, indicating that for the first date, the returns are not available. This is normal since for the first date, there is no previous price available to compare with the current price. The first row can simply be removed as shown on the slide, leaving us with a time series of returns on the individual investments.

To compute the time series of portfolio returns, we also need to define the time series of initial portfolio weights. There are several possibilities for this, and the default choice is to set only the initial weights for the first date and have the subsequent weights be automatically determined by the price dynamics. Alternatively, a dynamic approach can be pursued, where full allocation occurs, and buying and selling assets are used to actively change the portfolio weights. This is called rebalancing. With returns and portfolio weights defined, we can then finally compute the time series of portfolio returns using the `returnPortfolio` function.

The `returnPortfolio` function is a powerful tool with at least three arguments that need to be specified: the return data argument, the weights argument, and the rebalancing argument. Defining these arguments will allow us to specify how often the portfolio needs to be rebalanced and how it should be done. While we won't delve into the details of these arguments, understanding them is essential for getting the most out of this function.

In practice, computing portfolio returns over multiple periods requires a loop over different dates. This can be achieved using the Performance Analytics package in R, which offers a comprehensive approach to analyzing portfolio returns. By following the steps outlined above and utilizing the functions provided by the package, we can gain a deeper understanding of our investment's performance and make more informed decisions.

"WEBVTTKind: captionsLanguage: enat this point you know the ins and outs of computing a portfolio return over a single period running from a start date to an end date in practice we will need to compute the returns for many periods in fact the longer the history of returns the more information we have about the underlying portfolio performance real-life analysis of portfolio returns this requires a loop over the different dates in this video I will show you how to do this using the our package performance analytics this is the go-to package for analyzing portfolio returns in our that has been written by two quants from the city of Chicago Peter Karl and Brian Peterson so how to compute a time series of portfolio returns and are well this is made easy by using two functions in R namely the function return that calculate and the function return portfolio the main argument for the function return what calculate is a time series of n of P prices of the different investments this should come as an object of the XTS time series class meaning that a rose or art and time the corresponding dates or preferably indicate as year year year year - month month - day day by default the function return of calculate transforms the time steals of prices into time C love returns where each of salvation has a percentage change in value over a period note that the first row of the obtained return data consists of an ace this means that for the first date the returns are not available this is normal since for the first date there is no previous price available to compare with the current price this first row can just be removed as shown on the slide as such we have a time series of returns on the individual investments to compute the time series of portfolio returns we also need to define the time series of initial portfolio weights there are several possibilities the default choice is to set only the initial weights for the first date and then have the subsequent weights be automatically determined by the price dynamics the alternative is to pursue a dynamic approach to put full allocation in which the action of buying and selling assets to actively change the portfolio weights is called rebalancing given the returns and portfolio weights we can then finally compute the time series of portfolio returns using a function with total portfolio this is a powerful function with at least three arguments that need to be specified the return data argument the weights argument and the rebalancing argument defining whether and how frequent the portfolio needs to be rebalance without going into details let's go to the exercises and learn by doing about these argumentsat this point you know the ins and outs of computing a portfolio return over a single period running from a start date to an end date in practice we will need to compute the returns for many periods in fact the longer the history of returns the more information we have about the underlying portfolio performance real-life analysis of portfolio returns this requires a loop over the different dates in this video I will show you how to do this using the our package performance analytics this is the go-to package for analyzing portfolio returns in our that has been written by two quants from the city of Chicago Peter Karl and Brian Peterson so how to compute a time series of portfolio returns and are well this is made easy by using two functions in R namely the function return that calculate and the function return portfolio the main argument for the function return what calculate is a time series of n of P prices of the different investments this should come as an object of the XTS time series class meaning that a rose or art and time the corresponding dates or preferably indicate as year year year year - month month - day day by default the function return of calculate transforms the time steals of prices into time C love returns where each of salvation has a percentage change in value over a period note that the first row of the obtained return data consists of an ace this means that for the first date the returns are not available this is normal since for the first date there is no previous price available to compare with the current price this first row can just be removed as shown on the slide as such we have a time series of returns on the individual investments to compute the time series of portfolio returns we also need to define the time series of initial portfolio weights there are several possibilities the default choice is to set only the initial weights for the first date and then have the subsequent weights be automatically determined by the price dynamics the alternative is to pursue a dynamic approach to put full allocation in which the action of buying and selling assets to actively change the portfolio weights is called rebalancing given the returns and portfolio weights we can then finally compute the time series of portfolio returns using a function with total portfolio this is a powerful function with at least three arguments that need to be specified the return data argument the weights argument and the rebalancing argument defining whether and how frequent the portfolio needs to be rebalance without going into details let's go to the exercises and learn by doing about these arguments\n"