Portfolio optimization Definition: Determination of weights of securities in a portfolio such that it best suits a given objective, eg. Maximize return for a given risk. REoptimizer ® ’s lease management software is a CRE optimization tool that allows you to balance your portfolio, save time and money, streamline communication, and manage projects across your entire organization with ease. Key Features. Bank level security to protect your documents and data. Easy collaboration and reporting keeps your entire team on track.
Portfolio optimization is the strategy of arranging the types of financial instruments found in a given portfolio so that the best possible results are attained. Typically, this process calls for assessing several factors, including the percentage that each investment represents of the overall value of the portfolio, and the degree of financial risk associated with each holding. The optimization process also takes into consideration the goals of the investor and what combination of holdings is most likely to move the investor closer to achieving those goals.
Key to portfolio optimization is understanding what types of investments are within the scope of the investor’s comfort level. More conservative investors are likely to go with holdings that have a solid history of providing returns even in difficult economic periods. While the returns from these investments may not be spectacular, they are consistent and allow the investor to incrementally increase the overall value of the portfolio. Investors who are willing to assume more risk may be open to not only investing in emerging markets and new companies, but also taking on investments that are historically volatile, providing the opportunity to post significantly higher gains in return for assuming the greater risk.
The choice of constrained optimization method depends on the specific type of problem and function to be solved. More broadly, such methods are related to constraint satisfaction problems, which require the user to satisfy a set of given constraints. Constrained optimization problems, in contrast, require the user to minimize the total cost of the unsatisfied constraints. The constraints can be an arbitrary Boolean combination of equations, such as f(x)=0, weak inequalities such as g(x)>=0, or strict inequalities, such as g(x)>0. What are known as global and local minimums and maximums may exist; this depends on whether or not the set of solutions is closed, i.e., a finite number of maximums or minimums, and/or bounded, meaning that there is an absolute minimum or maximum value.
Constrained optimization is used widely in finance and economics. For example, portfolio managers and other investment professionals use it to model the optimal allocation of capital among a defined range of investment choices to come up with a theoretical maximum return on investment and minimum risk. In microeconomics, constrained optimization may be used to minimize cost functions while maximizing output by defining functions that describe how inputs, such as land, labor and capital, vary in value and determine total output, as well as total cost. In macroeconomics, constrained optimization may be used to formulate tax policies; this may include finding a maximum value for a proposed gasoline tax that minimizes consumer dissatisfaction or yields a maximum level of consumer satisfaction given the higher cost.