How CMPAS Can Help You

Lack of control. Inflexible models. Limited choices. If you are a portfolio manager, you may be frustrated with the limitations of your current asset allocation software. Until now, it has been difficult to find solutions that offer more control over key assumptions about the market and the ability to seamlessly  incorporate your firm’s philosophy and process. 

Now you can chart a better course. Caravel’s mission is to empower portfolio managers and advisors with more control, better models, and more choices through our transformative portfolio construction software, CMPAS.

At Caravel, we believe the CMA decision is the most important part of the asset allocation and planning process. We support our clients by providing the option to use Price-Driven CMAs in addition to the industry standard Static CMAs. Caravel is the first asset allocation and planning system to offer a Price-Driven CMA option. We also allow you to upload proprietary CMAs and CMAs licensed from third party providers, and upon request, we are happy to produce CMA models customized to the specific requirements of a client firm.

The image above shows the past 100 years of large cap stock returns in the US. As that steady green trend line suggests, long term returns have averaged about 6.5%-7% for more than 100 years.

Current planning tools assume a static expected return—usually static around that long term average. Monte Carlo assumes a wide range of potential outcomes around that average, but plan results are driven by that static input assumption.

Price-Driven capital market assumptions assume that as prices go up, returns start coming down, and that a long run of extraordinary returns is eventually followed by a period where some of those gains are given back. Similarly, the Price-Driven framework assumes that market crashes are usually followed by a period of above average returns.

Monte Carlo ensures that both Static and Price-Driven assumptions explore results above and below the average, but the range of assumed potential outcomes can be very different under the two approaches.

Bonds can be even more different under Static and Price-Driven assumptions.

This chart shows historical 10-year bond returns for the past 80 years graphed against the starting yield.

Although Static CMAs assume some average historical return, the chart suggests that starting yield could be the primary driver of long-term returns.

Again, Monte Carlo assumes outcomes above and below the input expected return, but Static and Price-Driven assumptions provide a very different view of potential future returns.

Price-Driven assumptions can also provide a very different client experience across time as markets fluctuate. If we go through a market crash similar to the one we experienced in 2008, your clients may find that everything they planned for a few months ago suddenly looks unachievable because the software applies the same return assumptions to a portfolio with a dramatically lower value. On the flip side, we are currently experiencing the opposite extreme, with the same static return assumptions being applied to portfolios that have jumped 50% over the past 18 months. Price-Driven CMAs smooth out those extremes by increasing expected returns after a market crash and dropping expected returns after a huge market surge.

In addition to providing a more realistic picture during times of significant change, using Price-Driven assumptions can also encourage productive conversations with clients. At current prices, Price-Driven assumptions are saying that the two most important asset classes (large cap stocks and investment grade bonds) appear priced for disappointing returns.

This doesn’t mean every asset class is priced for disappointing returns. Major asset classes like Low Vol and Dividend stocks are close to average valuations and offer much better potential returns. The key to identifying these opportunities is having the technology and systems that will show you how different asset classes impact client plans and potential risks.

 This is an example of a portfolio strategy that does a slight tilt away from the most overvalued equity asset classes, adds a bit to Low Vol and Dividend, and barbells the bond portfolio.

The result is a portfolio that looks better under Static and Price-Driven (PD) assumptions, picking up 160 bp under PD with basically the same overall investment risk. That doesn’t mean it is a free lunch.

Tilting away from Growth means that this portfolio risks underperforming the current strategy by about 50 bp if Growth continues to outperform.  And that isn’t a theoretical number. We look at the past 30 years of market history and let you know the worst this proposed portfolio has done relative to your comparison portfolio, so you know what the downside is and can make an informed decision.

If you are willing to take more tracking error risk, you can build portfolios and plans with even more potential return advantages.

The point of this example is not for us to tell you how to position client portfolios and plans—our planning software will work seamlessly with your existing asset allocation strategies. The point is simply to provide you with the advanced technology and analytics that allow you to explore the strategies you want to explore, and provide you with the information that helps you determine the best strategy for your clients and your practice.

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