Other Retirement Planning Capabilities
- Healthcare Insurance – Before and after age 65.
- RMD Requirements – What happens at age 70 ½ and later.
- Tax Analysis – What am I paying and what should I expect to pay in retirement?
- 401k Recommendations – Are you invested in alignment with your goals and risk tolerance?
- Social Security Analysis – What is the best choice for your situation?
- Pension and Survivorship Analysis – Considering your financial position and personal goals, what option should you choose.
- Roth Conversion – Is it beneficial to me for the long term?
- Type of Retirement Account – Am I taking advantage of the best plan? Are there other accounts I should use?
- Monte Carlo Simulation* – This analysis looks at the probability of your money lasting through different environments (varying levels of returns and inflation)
- Retirement Income – Do you have a funding gap and how best to fill the gap?
- The RV life or maybe it is traveling abroad – Let’s design your retirement
- Long-Term Care – Do you have a plan?
*About Monte Carlo: Monte Carlo Simulation is a risk and decision analysis technique used to evaluate the outcome of portfolios over time using a large number of simulated variables to generate possible future returns.
There are many variables that can affect a financial plan. Two of the most volatile variables are inflation and investment returns, both of which, historically, vary on a daily basis. Even with this knowledge, most financial projections use constant inflation and investment rates over the period of the analysis. The use of these averages is used as a start for the planning process, since the actual values are unknown. Unfortunately, however, this type of analysis illustrates only one outcome, thereby requiring that simulation be used to imitate real-life situations. In order to produce meaningful results, these simulations are processed many times. By varying the rates of return and inflation to simulate the fluctuations that can be experienced in the marketplace, a more realistic reflection of the anticipated ups and downs of the investment environment is presented.
In order to create a Monte Carlo simulation model, historical performance of the securities market must be analyzed. This analysis does not utilize historical data for any specific securities. Rather, it uses the historical data for broad asset classes, such as "Small Cap. Equities" and "Long Term Bonds." This analysis takes into account not only the historical values of various investment factors (prices, inflation, etc.), but also the interrelation of these values and the correlation between investment periods. The econometric modeling method is used to generate the capital market data used in the simulations. This involves modeling the movements of yields through time and then layering on various equity risk information to derive stock returns. This results in many economies being simulated for a given time period.
These multiple simulations produce a range of results. These results are then analyzed and probabilities are associated with the outcome. Due to the random nature in which the simulations are generated and the regular updating of historical asset class data, the results may vary with each use and over time, even if the underlying assumptions are not changed.
Important: The projections or other information generated by Monte Carlo Simulations regarding the likelihood of various investment outcomes are hypothetical in nature, do not reflect actual investment results and are not guarantees of future results. An investment cannot be made directly into a Monte Carlo Simulation. There are limitations in using a Monte Carlo Simulation, including the analysis is only as good as the assumptions, and despite modeling for a range of uncertainties in the future, it does not eliminate uncertainty.
The results can be presented various ways, but the ultimate goal of a Monte Carlo Simulation is to educate and communicate about the uncertainty of the future, so you can make educated decisions about your specific situations.