Before delving into details on a new way to view expected structured product performance, I feel I need to make a retraction. It encompasses all of my previous articles where I placed judgment on whether a product was good or bad based on its expected relative value. I now realize I had no basis for passing judgment. You see, that judgment was made on only one possible market scenario which was not based on my view of future market behavior. It was based only on projecting the past performance of the underliers into the future. Obviously, that is not me taking a view; it is just a simple assumption, and thus, not a valid basis upon which to pass judgment.
More importantly, what if you, the investor, disagreed with that historically based future market scenario. While I believe I am very capable at analyzing structured products, I lay no claim at being good at predicting future markets. And, as you will see, having a view on future market behavior, especially that of the underliers of a given structured product, is crucial in determining whether a product is a good investment. Thus, a judgment should not be made by me, the analyst, but instead by you, the investor, after you form a view of how you expect the market and the structure's underliers to perform. So, I retract all my past judgments. And leave those judgments to the investors.
But now you see there are a couple of problems. How can I analyze all possible future market scenarios? And more importantly, how can I present that information to allow you to make a judgment? Well, coming up with an answer to those questions is what motivated the invention of product performance mapping (patent pending).
Though this method is new and the type of graphs used are unfamiliar in their use as a financial tool, the graphical method it is based upon should be familiar in other contexts (e.g. in meteorology, daily temperature and radar maps). Using colors to indicate values, these product performance maps display simulated expected values such as the product's relative value, annualized return or probability of returning all of the investor's initial principal over a range of possible future market scenarios. The various markets are represented by two parameters -- overall market return and volatility. The maps are centered at the average historical values for these parameters with generalized ranges of bear to bull market returns and high to low market volatilities.
Now, the easiest way to understand this technique is to see it used on an actual structured product. For that, I selected a relatively simple product, a buffered digital return note. The details of that product are covered in the next section, followed by a historical analysis of the product which is needed in order to generate the product performance maps discussed in the last section.
This analysis is based on the preliminary prospectus filed with the SEC on 09/29/2016. The following charts and figure summarize the structure of the product detailed in the aforementioned prospectus.
The product underlier is the Russel 2000. The shorthand notation used in "payment features" indicates that this note has a digital return while possible loss of principal is buffered. As the following payoff logic indicates, the digital return is 17.5% of the investor's original notional while the buffer is 20% of said notional.
This figure shows how the payoff behaves as a function of the underlier return.
Three types of simulations have been performed to generate this product analysis, issuer pricing, historical and scenario simulations.
The issuer pricing simulations use industry standard models related to risk-neutral pricing to generate the estimated issuer valuation and related metrics. Though not necessary for an investor evaluation of this product, the results of the issuer pricing simulation are included here for comparison purposes, as this valuation is an estimate of the issuer's cost of hedging the given product and thus indicative of the best price that a seller could receive in the secondary market as of the given analysis date.
The historical simulations consist of evaluating the performance of the product as it would have performed if issued at monthly intervals covering a full economic cycle. Displaying the results of these simulations serves two purposes. The first is to show product performance in markets that should be familiar to the investor. The second purpose is to be able to obtain a historical product return and volatility. Using this information, a product discount curve can be generated based on the capital asset pricing model (CAPM). With this discount curve in hand, the historical and scenario simulated cash flows can be discounted to arrive at a risk versus reward valuation which can then be compared to the investor's relative investment cost of 100%.
While the historical simulations are useful to gain intuition into how the product would perform in an actual market, to truly gain an understanding of the risks and possible rewards of a market-linked product, you need to see how it would perform in a representative sample of possible future markets. This is what product performance mapping allows you to see. The scenario simulations are done over a range of possible future market behaviors in terms of the product underlier returns and volatilities.
Over 1.2 million possible future market simulations have been performed to allow the generation of the product performance mappings. As you will see, these product performance mappings are simply a graphical representation of investment performance metrics. This method is designed to have an intuitive appeal allowing easy interpretation of how the product is expected to perform under different market conditions. This will allow you to quickly ascertain whether a given product is a good investment based upon your views of how you expect the market and the market-linked product's underliers to perform. The product performance mapping will also allow you to understand what to expect should your market views not come to fruition.
In the next section, the historical simulations are discussed to build an understanding of how the product performs over the last economic cycle. Then follows the product performance mapping section where the product metrics of the issuer pricing and historical simulations are compared with the product metrics of simulations for 5 possible market scenarios. Those five possible markets will serve as reference points in the product performance maps and thus assist your understanding of how the product should perform based upon your expectations of the actual market and the products underliers.
The graph below shows how the product would have performed if offered at various points in time over the past economic cycle.
As you can see, the only times when the Russell 2000 experienced more than a 20% drop over a 3.5-year period occur in the period starting 3.5 years before the market drop beginning at the end of 2008. The maximum loss of original investor principal is almost 30% while most losses are less than 10%.
Thus, you can see that for this given market scenario, the structure performed quite well. But these are only 120 structure simulations over a single economic cycle. To get a true idea of how this structure can be expected to behave, a large number of simulations must be done over a representative sample of possible market conditions. This is what product performance mapping does, and the results of those 1.2 million+ simulations are given in the next section.
Product performance mapping
To best understand the product performance mappings given in this section, you need to reference the underlying markets and corresponding underlier expected returns and volatilities shown in the table below. These 5 reference markets are the center points of regions of the graphs. Those 5 regions are the center of the graph itself and the centers of the 4 quadrants of the graph. In each performance mapping, these regions are easily identified as the intersections of the grid-lines at the center of each region.
As the above chart indicates, there are 3 types of market returns --normal, bull and bear. The normal is based on historical averaging over the period given in the previous section. The bull returns are computed by starting with the normal return and adding a weighted underlier volatility factor. The bear is computed similarly except its volatility factor is subtracted. Thus, underliers with higher normal volatilities will experience larger expected return swings when going from normal markets to both bull and bear markets. For the volatilities, the normal uses historical averaging while the low and high volatilities have been computed by respectively adding and subtracting a multiplicative factor of the normal volatility.
Now, in regards to the product performance metrics at the center points of the 5 map regions, the following chart gives those results along with issuer pricing and historical simulation results which have been included for comparison. When viewing the performance maps, you can use these results to get a bearing on how the product should perform over a given region.
As for the definitions of the listed performance metrics, here is a brief description of each:
- Relative value is relative to the initial investor's principal. As stated previously, its calculation is based on the application of the CAPM and thus represents an estimate of the risk versus reward valuation of the product -- with values near 100% indicating fair market value for the given amount of risk the investor assumes.
- Expected annual return is based on the total payments of each simulation.
- Probability of full return of principal is the percent chance that under the given market conditions, the investor will have their initial principal returned to them at an early exit or final maturity date.
- Bottom 10% annual return is the average annualized return of the worst performing simulated structures. This metric should be viewed as a reminder that underlier performance can deviate from general market performance.
As you can see, the values peak in the bull return low volatility region, dropping off as returns move down -- with a precipitous drop in the bear return markets. In comparison, the historical values are similar to the bull return high volatility results. Of course, that is based on one market history and not the 10,000 simulations taken at each market scenario which should serve as a reminder that simply using past returns is not a good way to try to predict the future.
Examining the product performance maps below, the first thing to note is the plateau region at high returns. The contour lines have a negative slope in these regions, indicating that market volatility plays a relatively important role in determining the expected amounts, with the lower volatilities leading to better expected results. That negative slope decreases for all but the bottom 10 percent annual returns map. Thus, the effect of volatility diminishes as the markets move into the bear returns region, where market returns become truly dominant in determining expected values. For the percent chance of full investor notional being returned map, you can see that the slope of the contours actually turns positive at and below the bear returns -- thus indicating that markets with highly negative returns and low volatilities actually put investors in a quagmire where there is little hope of the product having positive returns.
1. Relative value ppm:
2. Expected annual return ppm:
3. Probability of full return of principal ppm:
4. Bottom 10% annual return ppm:
After thorough examination of the product performance maps, you should have a good idea of how the given market-linked product will perform. These expectations should be based upon how you expect the market to behave in general as well as the product's underliers. You should also have a good feel for how the product will perform if the underlier does not perform as you expect. Of course, to really get a feel for product performance it would be better if you could click on a given point to see the performance value and underlying returns and volatilities for that point. Unfortunately, that cannot be done here.
Finally, I hope this article also makes it clear how important it is to have a view on how you expect the market and the underliers to behave over the lifetime of a product when judging whether that product is a good investment. Obviously, that is something I did not appreciate when I first started writing these articles.
Disclosure: I/we have no positions in any stocks mentioned, and no plans to initiate any positions within the next 72 hours.
I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it. I have no business relationship with any company whose stock is mentioned in this article.