The main benefit to Simulated Portfolios is the level of information regarding the backtest. We’ll examine that here.
As soon as a Portfolio123 finishes running a simulation, you are taken to the summary page, as depicted in Figure 1.
This material is self-explanatory. It gives you a quick sense of what the simulation has accomplished. Sometimes, you can look quickly at the performance graphs in the upper left and see right away that you need to go back to the drawing board.
In this example, it looks like we might have something which, although very much imperfect, is at least worthy further study. (In Part 12, we’ve already seen that we could improve results with a 15% trailing stop, and that we could likely have mitigated the 2008 drawdown via some very simple hedging.) But for purposes of illustration, I’ll assume we want to look more closely at what we have right now.
There are three kinds of information available to you:
- Information about the stocks that make it into the portfolio
- Information about the transactions executed by the simulation
- Information about performance (returns, risk, and trading data)
We’ll examine all of these items in the order presented by the menu that runs down the left side of the page.
This section provides information about the stocks that have made it into your simulated portfolio.
If you click on “Current,” you’ll see a list of stocks you’d now be holding if you were actually investing with this model.
There are two especially noteworthy aspects of this display. First, note the download link just above the upper right corner. As is often the case in Portfolio123, you can download all of this information into an Excel spreadsheet. Notice, too, the “Yahoo! Quotes . . .” and “MSN Quotes . . .” buttons just above the table. Clicking either of these will open a separate browser window in which all of your current holdings are displayed in the portfolio module of the site you selected. If you like, you can save there and continue to monitor progress on the site you chose based on the content it offers.
Return and Fundamental
Like “Current,” these next two choices can be used to show the simulation’s current holdings, but using different report formats.
Figure 3 – Current
Figure 4 – Fundamental
This presents a different view of your portfolio.
Recall from Part 12 that one of our Buy rules capped sector weight at 30%. We see in Figure 5 that Technology, at 28%, is bumping up against that limit. It’s possible that one more tech stock might would put us over the top.
I decided to go back to the Buy rules, turn off the sector-limit rule, and re-run the simulation to see exactly what our diversification mandate did. I found that Technology jumped to 61% but the overall return increased very modestly: The present market value rose from $186,902.94 to $192,284.95. Speaking for myself, I’m not impressed with the overall gain. I don’t think it’s worth the extra risk inherent in the now-huge exposure to just one sector.
This gives you an opportunity to see what stocks were included in the portfolio previous points in time.
Here, you can see the information about the rank for each stock that makes it into your portfolio.
Notice the drop-down menu in the upper left area. This is similar to the one you’ve already seen in the Ranking area, which allows you to determine how much detail you’d like to see regarding the ranks.
Notice, too, the date menu in the upper right. You can use this to see rank information about groups of stocks previously held in the simulated portfolio.
Here, you can get good answers to the most basic question you’re likely to have about your simulation: How did it do?
We can start the way investors often like to start when the how-did-it-do question surfaces: by clicking on Graphs. Figure 8 shows what we’ll see.
As you can see, the graphs present the value of a $100 investment, the % drawdown, and the % cash invested over time.
You can use the interface at the top to make adjustments that should already be familiar from other areas of Portfolio123; the time period and the benchmark. Another familiar feature to the right of that interface is the download link, that lets you create an Excel spreadsheet containing the data upon which the top graph is based.
Now let’s look at some less-familiar features.
Notice the check box to the right next to “Log Scale.” You can check this to see the charts presented according to a logarithmic scale. Figure 9 shows what a logarithmic price chart looks like.
With an ordinary numeric price chart, the distance between, say, 100 and 200 would look the same as the distance between 400 and 500. Numerically, both differences amount to 100.
With a logarithmic price chart, the difference between 400 and 500 (which amounts to 25%) would appear much smaller than the difference between 100 and 200 (100%). That is atypical of the way many news organizations and web sites present charts, but it is a more accurate reflection of performance.
Notice, now, the input box to the left labeled “Additional (search).” You can chart another ticker for comparison purposes, as a supplement to you chosen benchmark.
Figure 10 shows what this would look like if I choose to compare my simulation to, say, Berkshire Hathaway (BRK.A).
Notice that the drop-down menu next to the label “Position” is set, by default, to “Behind.” Figure 11 shows what you’d see with the other choice, “Above.”
Whichever view you prefer, I think we can say the result is intriguing. Berkshire seems to get the edge because it delivered comparable return with what appears to be less volatility. But the fact that we’re even in the ballpark makes for an impressive conversation piece considering this simulation was patched together quickly just for demonstration purposes.
The Stats menu choice starts out by giving us a numerical presentation that should be self explanatory and familiar to anyone who has looked at a report of mutual fund performance.
Scrolling further down, on that page, we can see the distribution of monthly (the default choice), weekly, or annual returns for our simulated portfolio and the S&P 500.
Notice the download link on the upper right. Figure 14 shows the Excel histogram you’ll get if you use this feature.
It’s hard to say how widely-used this feature was when it was introduced on Portfolio123, but the 2007-08 financial crisis and the increased attention it brought not just to overall returns but the way they are distributed (particularly increased awareness of negative extremes) definitely brought this sort of inquiry to the forefront. As you can see, this particular simulated portfolio has been a bit more prone to particularly poor months, but not enough so to suggest cause for worry.
The monthly returns graph at the bottom of this page (Figure 15) and the spreadsheet we can download based on it (Figure 16) support this assessment.
The next group of items fulfill what can be described as an audit function. They allow us to look in depth at what, exactly, the simulation has done.
This does exactly what the label suggests: It provides a record of all transactions executed in our simulated portfolio. Figure 17 shows a small portion of the total, those executed on the most recent re-balancing date.
We see what was bought, what was sold, and probably most interestingly, why the sales occurred. ANN, for example, ran afoul of Sell rule 4 which, as we learned in Part 12, was an analyst rating downgrade. KYO was sold because its rank fell below 80, the sell threshold we chose.
There’s a lot of information here. Most users find it more convenient to use the download link at the top of the table to create an Excel spreadsheet.
This feature allows us to see all realized transactions.
Notice the drop-down menu at the top. It allows us to aggregate the individual items by symbol (often, individual stocks are bought and sold on several separate occasions), by Industry, by Sector or by Transaction Note or Sell Rule.
Figure 20 shows that the most frequently invoked sell rule was Sell3, the one involving a deteriorating moving average trend.
We also see, though, that the moving average sell rule wasn’t overly productive. The winner-loser ratio is unimpressive as is the average return. Perhaps we should consider eliminating that rule. The high-PEG ratio sell rule (Sell2) was invoked so rarely as to hardly be worth considering. The sell rules based on rank deterioration, recommendation downgrade and reduction in estimate seemed reasonably productive.
Figure 21, which depicts the sector aggregation, shows that while tech was important in terms of portfolio allocation, it’s not what drove positive performance. Services and Basic Materials were our primary winners.
This provides the ultimate drill-down. We can examine individual stocks, see the time(s) when they entered and exited the portfolio, and how well they did while they were held.
Here’s an example with Tyco Electronics (TEL), one of the stocks appearing in this simulation as of this writing.
The first time TEL entered the portfolio, August 16, 2010, it didn’t accomplish much. It was sold at the next (September 13th) re-balancing based on a diminishing moving-average trend and as often happened with that particular sell rule (see Figure 20), the experience was not productive. The stock came back at the next re-balancing (October 11th) and stayed through this writing (early 2011) and so far in its second tenure, it appears to be contributing well to portfolio performance.
Although this is at the bottom of the menu, this section is arguably the most intriguing, offering up some of the most sought-after information.
This section, illustrated by Figure 24, presents information many traders cherish. It evaluates the simulation in terms of winners versus losers.
Generally, this content is self- explanatory. But there are a few points worth noting in terms of what all this says about the demonstration simulation.
Some may dream of getting a bigger percentage of realized winners but often, 50-50 is what most achieve over time. Here, the realized winning percentage is 54.6%, is actually quite reasonable. Superior returns tend to come when average-winner gain dwarf average-loser declines. But among realized transactions, that’s not heavily present: We see an average gain of 11.09% for winners versus an average 11.08% loss for losers. This simulation’s performance was dependent, perhaps unusually so, on the win-loos percentage.
The balance among unrealized positions raises an eyebrow. The winning percent here is quite high, 72%, and the average percent gap is more appealing; +12.39% on average for winners, versus -4.06% on average for losers.
What happens between Realized and Unrealized? Is the present situation involving Unrealized transactions a fluke? Clearly, although the simulation has some good qualities, we’re not yet ready to pronounce if a finished product.
With this content, all the way at the bottom of the menu, Portfolio123 arguably saves the best for last.
Notice, here, the A-word: Alpha. It’s what every portfolio manager today dreams of. It’s a measure of how much annualized return the portfolio exhibits above and beyond the level of risk assumed by the portfolio manager. A manager who subjects the portfolio to above-average risk would have no right to brag about above-average returns unless the return was higher than what investors had a right to expect based on the excess risk. Conversely, a manager with below-average return would still earn respect if the level of conservatism was such as to generate an expectation that the returns would come in lower than what we saw.
In interpreting the data, be aware that Standard Deviation (a measure of the volatility of portfolio returns) is computed by Portfolio123 based on daily returns. Many other sources tend to use monthly numbers, which are typically much lower. It’s important to recognize this lest you be shocked by what at first might look like an unduly high standard deviation. The key is to focus on the comparison between the Model and the S&P 500. The same holds true of the Sharpe Ratio, return divided by standard deviation, and the Sortino Ratio, return divided by the negative portions of standard deviation (NOTE: Strictly speaking, these are Sharpe-like and Sortino-like ratios since, which differ from the classic versions in the we do not subtract the risk-free rate from the portfolio return). Use of daily computations will often make those ratios appear lower than they might if they were calculated using monthly returns. So again, the portfolio-versus-market return is the one upon which you should focus on.
Viewed from inception, this demonstration simulation produces good results in terms of Alpha, Standard Deviation, Sharpe Ratio and Sortino Ratio. The last three years, however, were not favorable, suggesting a need for more refinement, and possibly some use of market timing and hedging.
Correlation with the S&P 500 and R-Squared (the percent of portfolio changes that can be associated with movements of S&P 500) are unremarkable. Many like to see lower numbers, which would suggest a portfolio that tends to a larger extent to chart its own path as opposed to largely imitating the market. Beta is another indication of the extent to which the portfolio mimics the market. Our above-1.00 Betas tell us the portfolio is more volatile than the S&P 500 (only moderately so), which would be fine as long as we’re getting enough additional return to offset the increased risk (the Alphas tell us we have seen this on a long-term basis, but not during the past three years).
As you have seen from this material as well as Parts 11 and 12, Portfolio123 simulation is a very powerful tool. It’s more intricate than the testing we do with screens and ranking systems, but the payoff is great in terms of the realistic way simulated portfolios mimic the market, the flexibility they embrace (hedging, sell protocols, stop loss) and the depth of information you get regarding your strategy.