Alpholio™ has recently added the Dynamic Portfolio Analysis (DPA) service to its platform. The first post in this three-part series described how the DPA can be used with OFX/QFX files. The second post covered the use of DPA with Transaction CSV files. This final installment focuses on using the DPA with Return CSV files.
Let’s start with the analysis of a simple buy-and-hold equity portfolio that contained only one ETF, the SPDR® S&P 500® ETF (SPY), with all distributions reinvested. Here is how the Return CSV file for such a portfolio looks like:
As in the Transaction CSV, the first line starting with the # character is a comment that is ignored during processing of the file. The CSV has only two columns: the date of a trading day and the numerical return of the analyzed portfolio on that date. The return figure, which is typically a fraction, may also be expressed as percentage by appending the % sign. The majority of lines in this sample CSV were replaced by a single … line for visual brevity. The sample returns start on the first trading day in 2005.
The following chart with related statistics shows the cumulative RealAlpha™ for the portfolio analyzed with a regular fit:
The From date in the chart is the last trading day of the first full month of the portfolio’s lifespan. The To date is determined by the availability of historical data, through June 2016 as of this writing.
Not surprisingly, Alpholio™ determined that the portfolio had virtually no RealAlpha™; any non-zero values resulted from the limit of computational precision. The RealBeta™ of the portfolio was slightly lower than one because Alpholio™ uses a broad-based equity ETF, which includes mid- and small-cap stocks, as a proxy for the equity market.
The following chart and statistics illustrate the constant membership and weights of the reference ETF portfolio:
As could be expected, the reference ETF portfolio consisted of just one ETF (SPY), the same that constituted the entire analyzed portfolio. Please note that Alpholio™ constructed this reference portfolio only based on periodic returns, i.e. without any knowledge of the actual investment strategy, trades, positions or dollar amounts. This way, the confidentiality of investments was fully preserved.
For the second example, let’s use a diversified buy-and-hold balanced portfolio that contained multiple ETFs:
- 40% of the iShares Core U.S. Aggregate Bond ETF (AGG)
- 5% of the iShares MSCI Emerging Markets ETF (EEM)
- 15% of the developed foreign markets iShares MSCI EAFE ETF (EFA)
- 5% of the SPDR® Gold Shares (GLD) for further diversification
- 35% of the SPDR® S&P 500® ETF (SPY) as a core domestic equity holding.
In the portfolio, each ETF had its distributions reinvested. At the end of each month, the portfolio was rebalanced to the original ETF weights. Here is how the abbreviated Return CSV looks like for the portfolio:
As in the previous example, the first line of the file is a comment, the second line is the CSV header, and subsequent lines contain trading dates and numerical returns of the portfolio.
The following chart and statistics show the cumulative RealAlpha™ for the analyzed portfolio:
The portfolio also had a negligibly small amount of RealAlpha™. Thanks to a broader asset allocation and periodic rebalancing, the portfolio’s RealBeta™ was slightly lower than the 0.6 of a traditional 60/40 portfolio.
The following chart depicts the constant composition of the reference ETF portfolio:
As anticipated, the reference ETF portfolio contained exactly the same positions and weights as the analyzed portfolio did. Again, the reference portfolio was built without any knowledge of the original individual investments. This proves the correctness and viability of this analytical approach. Of course, the DPA can evaluate any investment portfolio, not just one that solely contained ETFs.
If you would like to apply the new Dynamic Portfolio Analysis service to your investment portfolio, please register on our website.