Mean-variance analysis is a financial framework that helps investors to make decisions about how to allocate their investments in a portfolio. The framework takes into account two important factors: the expected return of each investment and the risk associated with that investment.
The expected return is the average return that an investor can expect to earn over a period of time. It is calculated by multiplying the potential return of an investment by the probability of that return occurring. The risk associated with an investment is typically measured by its variance or standard deviation, which reflects the degree of variability in returns.
Mean-variance analysis seeks to balance the expected return of an investment with the level of risk associated with it. The goal is to construct a portfolio of investments that provides the highest expected return for a given level of risk, or the lowest level of risk for a given expected return.
In practice, mean-variance analysis involves selecting a set of investments that have different levels of risk and return, and then constructing a portfolio that achieves a desired level of risk and return by allocating capital across those investments in an optimal way. This can be done using mathematical models such as the Markowitz portfolio theory, which provides a method for calculating the optimal portfolio based on expected returns and variances.
Overall, mean-variance analysis is a powerful tool for investors to construct diversified portfolios that balance risk and return. However, it is important to note that the framework is based on certain assumptions about the behavior of financial markets, and that actual returns may vary from those predicted by the analysis. Investors should also consider other factors, such as liquidity, fees, taxes, and their own personal investment goals and risk tolerance, when making investment decisions.