#174 | Proprietary Index Performance – Part 1

Over the last few years, proprietary indices have been cropping up in increasing numbers in Fixed Indexed Annuity (FIA) products to the point where most, if not all, newly released FIA products have an index with an investment bank or asset manager’s brand on it. I wrote about these indices at length in a series last year because life insurers have been increasingly exploring adding these indices to their Indexed UL products. As of right now, the major players with proprietary indices in their life insurance products are AIG, Allianz and Symetra. Securian also offers a proprietary index in BGA IUL, a product they built with Annexus and is only available through a limited number of distributors. Nationwide just filed an allocation form for a New Heights Indexed UL, which might not be anything of note for folks in the life insurance world but anyone with even a casual tie into the annuity side of the business knows it’s a huge deal. New Heights is the brand applied to the blockbuster selling, Annexus-created FIA products sold by Nationwide that have catapulted them to being a top 5 seller in the space of just a few years. The proprietary index tide is just starting to roll into Indexed UL. Given the broad application of proprietary indices in FIAs, it’s only a matter of time before they’re pervasive in Indexed UL products as well.

The primary appeal of proprietary indices is that they can be engineered to deliver stable and predictable hedging costs. As we’re all well aware at this point, hedge prices for the S&P 500 can change quite a bit over a short period of time and based on three primary factors – interest rates, the volatility level and volatility skew. Life insurers can’t control those factors for S&P 500 options and that leaves them in the constant bind of trying to manage their actual hedge prices against their option budget while maintaining competitive positioning because, in the bizarre world of life insurance, short-term cap changes are reflected in long-term illustrated performance. To solve that problem, proprietary indices typically have internal mechanisms that allocate between risky and non-risky assets in order to manage both volatility and interest rate risk. The net effect of these factors is that counterparties can offer guaranteed hedge prices to life insurers for 5-10 year terms, which essentially means that the only moving part for setting participation in the index is the option budget at the life insurer. It’s a fantastic bargain for insurers and solves the perceived problem of regularly changing index participation. But is it a fantastic bargain for consumers?

The creators of all of these proprietary indices certainly think so. The best case for a proprietary index is that the volatility control mechanism produces low risk but the risk-on index component produces very high returns, so the net result is an index that produces outsized risk-adjusted returns. If it sounds magical, that’s because it is. The magic is in the fact that these indices are created out of thin air and their performance attributes are described based on what they do in a backtest. It’s very difficult, if not impossible, to tell whether or not a proprietary index has just been form-fitted to the historical data or if it actually has a shot at performing well in the real world. It’s a bit like obsessively watching tape before a football game – it’s good for strategizing, but it doesn’t predict the outcome. Neither does backtesting.

Fortunately (or unfortunately, depending on your perspective) these proprietary indices have now been in the market long enough for folks to get a taste of how they actually perform in the real world. Performance for many of the original crop of proprietary indices has been lackluster over the last 5 years. Security Benefit introduced the Total Value Index (TVI) back in 2014 to blockbuster sales and much fanfare on its TVA product, only to watch the TVI produce a negative return over 5 years against the S&P 500’s nearly 50% increase. Even Nationwide’s New Heights has taken its lumps. The original product’s flagship index, JP Morgan’s Mozaic, has produced less than a total 20% return since 2014 and has spent the last 2.5 years floating around the 10% mark. Many other indices have experienced the same pattern. If advisors pitched these proprietary indices as high performance, low risk indices then they’re probably licking their wounds.

But the results have not been completely unexpected. Proprietary indices have generally done what they’re supposed to do in terms of reducing volatility. When volatility picks up, which is usually the result of a quick drop in the market, the index allocates more to cash. To go back to the football analogy, it’s as if the team has spent so much time watching tape that it is pre-programmed to react in specific ways to the plays run by the opposing team. Historically, reacting according to the program would have won the game, but the real-world is such that the other team is constantly creating new variations of old plays. Despite their surface level differences, you can see proprietary indices running similar plays against the market over the past 5 years. Take a look at the chart below, which shows daily price data for:

  1. The S&P 500 (SPX)
  2. JP Morgan ETF Efficiente 5 (EEJPUS5E), which is in Symetra’s products. The 5 signifies a 5% volatility target.
  3. Merrill Lynch Strategic Balance (MLSB), which is AIG’s flagship and default index option.
  4. Bloomberg US Dynamic Balance Index II (BXIIUDB2), which has been in Allianz’s products for years under both the Bloomberg and Barclays brand, although the second version is fairly new.
  5. JP Morgan Mozaic II, which will be in the upcoming Nationwide New Heights IUL.

If you look long enough, you’ll see the clear story of risk-on/risk-off allocations for all four of the indices – like I said, they’re running similar plays even if they don’t produce exactly the same results. When SPX drops, the prop indices also drop, but not by as much and not in the same way every time. For example, take a look at the first quarter of 2018. The prop indices were obviously almost fully invested in risk-on assets because the SPX had been going up consistently since the end of 2017 with very low volatility. The swift drop in SPX caught them completely off guard, which is why they all took a beating along with SPX. For example, SPX peaked on 1/28/18 at 5,606 and JP Morgan ETF Efficiente 5 peaked on the same day at 253. Just 11 days later, SPX had dropped by 10% and JPM was down 7%. That drop, combined with subsequent peaks and drops over the next few weeks, pushed JPM into a primarily risk-off position. The other indices ran their own versions of the plays and produced different results. Mozaic II, for example, had delivered stellar performance prior to January of 2018 and then subsequently flatlined whereas MLSB and Bloomberg managed to eek out some growth, with the latter ultimately catching up to Mozaic II. Confused? Not sure which strategy you feel the best about? Unsure of the real differences between the indices? A little disturbed about the fact that they all perform differently even if they share similar characteristics? Concerned about your ability to correctly articulate all of the nuances of these different indices in the real world? Welcome to the world of proprietary indices. Don’t worry, you’ll get used to it.

But there’s more to the story. The really curious thing about these indices is that they are used primarily for application in structured products or indexed insurance products, both of which use derivatives to create their performance linkage. These products don’t provide continuous exposure to proprietary indices – instead, they slice it up into crediting segments. And as it turns out, that means the team is watching the wrong tape. or applicat