What is the CASE Management System?
The BetaVest CASE (Cash Flow, Asset Allocation, Sequence of Returns, Expectation Management) Management System is the complete software package includes the CEM Multimedia Tool and the desktop application. It will allow you to input and analyze client data, optimize and create cash flow scenarios, optimize with up to 7000 portfolio allocations, and generate a full set of detailed reports. Includes additional modules such as a Retirement Quick Planner, Lump-Sum Pension Analyzer, and an Annual Review Module.

Do you provide training?
Yes, we provide many forms of training:

Training DVD shipped with the software

Online Multimedia Tutorials

WebEx Training Sessions

Workshops*

On-Site or In-Office Training*

The BetaVest Advisor Guide 3-Disc DVD Set*

Phone Support available during regular business hours

*Fees may apply

Is your product FINRA Compliant?
Our software has been submitted to the FINRA and is currently under review. Please see our Compliance Section for further details.

What is the difference between the CASE Management System and Monte Carlo?

Monte Carlo Analysis randomly selects thousands of samples relative to your client's timeline. Statistically, based on a bell curve, Monte Carlo tries to predict a future unknown outcome of portfolio performance.

In contrast, the BetaVest CASE Management System, uses historical rolling period analysis. Using historical data, BetaVest places your client in history and allows them to live through every relative time period in sequential order.

To further illustrate, most Monte Carlo simulators available on a commercial level create mean variance distributions that create a normal bell curve distribution of returns where 67% of annual returns used to create Monte Carlo sequences fall within one standard deviation of the mean (average return). Therefore, there is an tendency to inadequately represent the actual effects of tail end returns of the normal distribution.

Although there is less probability of tail end returns occurring on a normal distribution, Monte Carlo does not create a fair representation of tail end returns actually occurring over a meaningful analysis period such as 25 years.

Rolling period analysis using actual historical returns reflects the probability of experiencing tail end returns that can materially impact actual outcomes. For example all 25 year rolling periods examined for large, mid, small and international stocks, reveals a 100% probability of experiencing the tail end returns during each of the 25 year periods analyzed. Rolling period analysis also accurately represents the historical sequence of these tail end returns.

What good are 10,000 randomly generated sequences of returns if history and actual economic cycles eliminate the probability of most never occurring.

Above all, currently the FINRA has not approved the use of pure Monte Carlo simulation because it does not reflect actual historical asset class sequence of returns. (See FINRA Rule 2210 and IM-2210-6)

All things considered, both Monte Carlo and historical rolling period analysis provided better assessments of outcomes when compared to static return forecasting models.