Frequently Asked Questions about Quantext Monte Carlo Planning Tools 1. What do I need in terms of computer resources to use these tools You will need Microsoft EXCEL and at least 512MB of RAM.  A number of users have reported higher memory requirements when using versions of EXCEL prior to EXCEL 2002.  If you have trouble with memory, try increasing virtual memory. 2. Why are these tools written in MS EXCEL? EXCEL is an incredibly powerful tool.  You can perform full blown Monte Carlo analysis and a range of sophisticated functions in EXCEL.  Using EXCEL helps to keep development costs down and to make it easier to update the tool with new features, etc. 3. What make Quantext's Monte Carlo tools different from other packages? Many systems use shortcuts that assign investments to broad classes (style analysis), but ignore specific details of a position.  Two funds of similar 'style' might be projected to have exactly the same future risk and return even if they have historically been quite different in that respect.  Our approach of accounting for each asset individually also means that our approach will tend to be better for portfolios that are concentrated.  - Quantext's tools model individual stocks and funds, not just indices or asset classes - Quantext's tools account for employee stock options and their impact on portfolio planning - Quantext's tools calculate portfolio dividend yields automatically to help income investors - Quantext's two versions are simple enough for the knowledgeable individual investor but sophisticated enough for professional advisors. - Quantext's tools are very fast--much faster than comparable tools - Quantext's basic Monte Carlo portfolio tool, Quantext Portfolio Planner, can be set up and run very easily - Quantext's Monte Carlo tools generate clear and intuitive reports that can be easily printed or emailed. - Quantext's Monte Carlo risk projections have been extensively tested and validated (many Monte Carlo models have not been benchmarked)--See paper here .  New paper for 2006 here . - Quantext's Monte Carlo tools capture non-market correlations between portfolio assets--something other systems do not do as well . - The user can easily see the projected risk and return for each asset in the portfolio and adjust (if they wish). - Our reports show you short-term risk, potential loss, etc. on a user-defined time horizon (days to months), as well as the long-term projections. 4. What are the more recent features in QPP / QRP? Since the start of 2006, we have made two major additions to QPP and QRP capabilities.  The first of these is that QPP and QRP now calculate dividend yields.  This feature makes it much easier for income investors to design their best portfolios.  The second new feature--and one that is unique as far as we know--is the ability to account for correlations between investments that are not captured by Beta.  This can be very important. In late May of 2007, QPP and QRP have had a new feature added that allows totally customized investing and income draws--this feature is discussed and demonstrated here . 5. Why do QPP and QRP currently limit you to 20 positions? The research shows that 20 individual stocks (properly chosen) will get you the all of the available diversification benefits.  Any more than this have essentially no benefit.  See the following link: http://www.investopedia.com/articles/01/051601.asp This issue is also discussed in A Random Walk Down Wall Street--a great book.  This is true if you have 20 individual stocks.  If you have funds, which are already an aggregate of many stocks and/or bonds, you need even fewer positions.  This is one of the best-established results of portfolio theory.  You will get far more benefit from choosing the 20 portfolio components carefully than simply having many different positions.  Just adding more choices to a portfolio will not get you much in terms of diversification effects.  6. What is portfolio autocorrelation? QRP and QPP both calculate an historical statistic called portfolio autocorrelation.  This is a recent feature and is not yet included in the user manual / textbook.  Portfolio autocorrelation is the correlation in portfolio returns from one month to the next.  If it is positive then high returns tend to be followed by high returns and vice versa.  If portfolio autocorrelation is negative, then the portfolio returns tend to be 'mean reverting' which means that very high return months tend to be followed by returns closer to the mean--the portfolio tends to damp out periods of very high or very low returns.  Portfolio theory generally assumes that autocorrelation is zero--the random walk.  QPP and QRP model the market as though autocorrelation is zero, and the metric shown is for historical performance.  If you have a portfolio that shows a lot of positive autocorrelation, this is a flag--this means that big swings get amplified.  These effects are widely debated, but there is evidence that they can be meaningful: Stock Return Autocorrelation is Not Spurious, Robert Anderson et al, U-Cal Berkeley, 2005 7. What does the Diversification Metric (DM) mean? In our latest release of the software, we have added a new analytical function that accounts for non-market correlation between portfolio components.  This is important because many asset classes have correlation to one another beyond what can be captured by Beta.  This is a major challenge for many portfolios, but especially those with concentrations in a sector.  This problem is described in a recent article.   Our sotfware generates a statistic that measures how effectively the non-market component of returns actually diversify one another.  In the best possible case, the non-market component of returns would be totally uncorrelated with one another.  In the worst case, they would be highly correlated.  The diversification metric (DM) measures how un-correlated the non-market returns are across the portfolio.  Higher values of DM mean that the non-market component of returns shows low correlation across the portfolio.  Higher DM means that your are getting more real diversification out of your portfolio.  8. What are the differences between QRP and QPP? QPP and QRP use the same basic underlying analytics for calculating forward-looking Monte Carlo analysis.  There are some differences between the two packages, however.  The main difference is that QRP allows the user to run analysis using 1) pure historical data, 2) automatically-generated parameters, or 3) customized parameter sets.  This means that you could run a portfolio with the basic parameters and then adjust any of the parameters by hand.  QPP allows the user to adjust many parameters, but it is not as flexible in terms of user adjustments to parameters.  QRP also allows the user to specify a shift in the projected risk and return of a portfolio (say, at retirement) and look at the impact of that shift on long-term outcomes.  If you want to be able to adjust every parameter and see the impacts, QRP is the right choice.  If you want to use largely automated parameter sets for risk/Beta/ etc., QPP is a cheaper alternative.