Nicki Potts, Director of Product Management at Morningstar (former COO & SVP, Product Development at FinaMetrica Pty Ltd), shares historical data that shows FinaMetrica risk scores across the globe remain relatively stable through stock market volatility. This research demonstrates that Advisors who perform a valid risk tolerance profile for their clients are more likely to see their clients stay the course during severe market downturns. -Mike Barad, Head of Financial Planning Software, Morningstar, Inc.
Our industry is filled with conflicting information. For both advisors and clients it is almost impossible to sort out “truth” from “fiction”.
While a distribution portfolio’s exposure to sequence risk changes over time, sequence risk never really goes away unless the withdrawal rate is constrained considerably.A practical method for advisers to measure this exposure to sequence risk is through evaluation of the current probability of failure rate.The fundamental withdrawal rate formula is portfolio value ($X) times a withdrawal rate (WR%) to equal the annual distribution amount ($Y).
Financial advisors commonly use three methodologies to determine client life insurance needs: income replacement, expense analysis, or hybrid models. Expense analysis is generally recognized as the more robust methodology, but requires greater discovery and data to perform. This paper compares these techniques to determine to what extent the simpler approaches identify greater or lesser insurance “needs” and to determine whether the differences are significant.
The FPSC Foundation has engaged PlanPlus to carry out an environmental scan of research carried out between 2003 and 2011, from Australia, Canada, United Kingdom and the United States, in the field of financial planning. Article by Fagan, Brayman will be published in the Journal of Personal Financial Counselling, Summer 2011.
Although investment challenges like those experienced in 2008 and early 2009 can result in consumers losing substantial amounts of their investment capital for a time, many consumers lost their homes in the same period as a result of questionable lending practices or a failure by consumers to understand and plan their borrowing decisions.
New generation models of assessing a client’s profile for investment purposes differentiates among factors like the client’s tolerance for risk which is a psychological trait, the client’s required risk which is a how much risk a client might be required to take to achieve desired goals, and risk capacity which is how much risk the client can afford to take.
The current evolution of “suitability assessment” of investment portfolios has differentiated between risk tolerance, a psychological construct; risk required, a financial construct of the return required to achieve specified goals; and risk capacity, the risk that the investor can afford in the event of a downturn. Application is usually for advisors to recommend more conservative portfolios in the event that the risk capacity of the investor is deemed to be less than the risk tolerance and risk required.
There is a strong desire in the financial services sector to be able to address the uncertainty of a projection or forecast for a client’s financial plan based on two primary factors – mortality risk and rate of return risk. The primary methodology for handling uncertainty has been and remains Monte Caro Simulations for investment returns and randomization of mortality assumptions based on mortality tables. This paper hypothesizes that it is possible to construct an algorithmic replacement to Monte Carlo Simulations
The Common Rule (CR) for order of withdrawal of investment accounts prescribes deferring tax as long as possible. Research has shown that it is possible, in some cases, to reduce taxes and maximize terminal wealth by withdrawing tax deferred funds earlier at lower tax rates, rather than realizing this taxable income at later times and at higher marginal tax rates. This approach is often referred to as ‘topping up to band’ (1B).