I recently decided to investigate udinh some alternate technology; dpecifically, I was considering a change in my financial planning software. After much thought and investigation, however, I’ve decided to stay with the planning software that I have been using. In this post, we’ll discuss some of the reasons behind this decision. 

Technology & Integration

As I’ve mentioned before, eMoney is an attractive offering, but falls short in one key area. This area is Monte Carlo simulation. Before we discuss the particulars, let’s begin with an overview. First, eMoney has an edge in terms of integration. For example, it includes an online document storage tool, an account aggregation software, financial planning software and a user portal where the client can easily generate a variety of reports on demand. Because the data is updated daily, generating an updated financial plan is relatively easy. This integration will greatly increase an advisor’s efficiency. In addition, there are a number of marketing videos which are tailored to various industries. All of this makes eMoney highly enticing. However, there is one issue which is of enough concern to me to keep me on the sidelines for now. I find eMoney’s Monte Carlo Simulation (MCS) tool is lacking. 

Monte Carlo Simulation

Here’s the problem in a nutshell. To run a MCS, you need four things: returns, standard deviation, correlation and an appropriate distribution curve. Most financial planning tools are deficient (with respect to MCS), in their return and risk assumptions. For instance, there are three to five major asset categories—stocks, bonds, cash, alternatives and other. There are also many subcategories. For example, with stocks, there are size differentials, value versus growth, geographic differences, etc. In eMoney (and many others) it categorizes a specific holding, but rather than use the data from the specific investment, it will use data from its category. In short, it will use category average data. Hence, if the holding does not track well with its category, the output will be less meaningful. It would be preferable to use the data from each specific holding rather than a category average. 

Here’s my bottom line. A financial plan contains numerous assumptions, many of which are uncertain, and thus should be modeled using MCS. In a closed-architecture such as eMoney (and most others), MCS can only be applied to certain assumptions. The two most important assumptions in the majority of all financial plans are the investment returns and expenses. There are other important items, but these are the most important. I don’t want to give the impression that eMoney is a poor quality product. To the contrary, I find it is one of the best I’ve seen in many respects. However, it is lacking in its MCS application. Therefore, I’ll stay with what I have been using. 

By the way, I use Microsoft Excel with Crystal Ball Professional from Oracle. CB is an add-in which costs a couple thousand dollars, but is worth every penny. It provides MCS, decision analysis, optimization, time-series forecasting,and much, much more. In fact, it is also used in Six-Sigma analysis. Perhaps one day I’ll dive into this and add business consulting and efficiency analysis to the services I offer cliets. However, I’ve spoken to a few Six-Sigma practitioners and it’s quite technical. We’ll see.


Thanks for reading and have a great week!