Portfolio optimization is a technique designed to balance the risks plus returns of an investment portfolio over long holding periods. Read more about this technique in this short article.
Since each investment (stocks, bonds, commodities, real estate, etc.) or line of business you have in your portfolio has a number of return profiles, your portfolio will useually have some diversification benefit built in already. The goal of portfolio optimization is to add to the benefits of diversifying without decreasing long term returns. In different cases this technique will actually multiply returns. At a minimum, your overall variability in returns will likely go down, producing a steadier, more reliable profit curve.
One of the things to look for in a portfolio optimization tool is the ability to specify a variety of inputs to the model. Inputs might be target return, maximum return volatility, minimum return, maximum or minimum weighting per investment or sector, discount rate meant for discounting future returns, lookback period of historical return calculations, etc. You would like to start with the current portfolio or perhaps a benchmark portfolio to gauge against. You must specify the individual stocks, bonds, futures, commodities, etc. in the portfolio, plus historical cash flows for a sufficient period to calculate historical volatility plus returns. Normally this means daily or weekly price history for securities, as well as monthly cash flow history for investments in real estate, businesses, or business units.
Once the inputs are specified, the model should automatically calculate correlations between the individual
investment returns. If your group of investments are highly correlated, moving together when the market moves up as well as down, then you may be able to optimize the portfolio only so far. On the other hand, if your portfolio is done up of a variety of investments in several assets and various sectors of the economy, then the model has a lot more to work with from the start by adjusting the weightings of each investment.
An important consideration when doing portfolio optimization is the capability of the calculation model to ensure the simulated return of the optimized portfolio is at least as high as the current or baseline portfolio. There should be a tick box to select before you run the model. This makes sure that you don't run an optimization which creates a cut risk portfolio, but ends up significantly reducing long term profits. This is particularly important when there is a long period of time between the initial investment also future distributions, such as a retirement account where a sizeable amount of cash will be needed at the end of the investment horizon and the primary interest is in high growth at the beginning.
The output of the optimization should include a Monte Carlo histogram distributions of returns showing how the starting portfolio and the optimized portfolio stack up against each other across thousands of simulations. The expected return meant for each portfolio is expressed as the mean of the distribution. The exact number of units or amount of capital to invest in each component is required to guide the portfolio reallocation. In addition, statistics such as standard deviation, Sharpe Ratio, Treynor Ratio, as well as probability of achieving the target with the portfolio time horizon should be displayed in the results. A graphical display of the baseline plus target portfolios versus the efficient frontier is highly desireable. These items are critical in gathering the statistical data to support an efficient allocation of investments.
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