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Mean Variance Optimization (MVO) is the framework for maximizing the reward-to-risk ratio of a portfolo. Mean in the numerator refers to the mean expected return and variance in the denominator refers to a measure of expected variability. MVO is a key takeaway from Modern Portfolio Theory as it is assumed that all investors maximize returns relative to risk. A software program called an MVO optimizer generates weights to securities while maximizing the reward-to-risk ratio.
Synonyms: Mean-Variance, MVO, Mean-Variance Optimization
For context, recall that nearly every individual views the world differently. And there are about as many investment processes as there are people. Yet, in academia, scholars have settled on the idea that investors find an expected return on all risky assets and the correlation among those assets. From there, investors create a covariance matrix and utilize mean variance optimization to find the weights of all holdings.
The objective function in the optimizer is to maximize the expected return, or mean in MVO, while minimizing expected risk using the measure variance.
In a plain vanilla optimization case some investors instead use the square root of variance, which is standard deviation. In this case, the MVO optimizer maximizes the ratio called the Sharpe Ratio. In the end, the mean variance optimizer returns the weights to each security. Constrants can be added to fine-tune the resulting portfolio so as to not short stocks, to limit the number positions or to restrict the weights allocated to sectors.
To visualize on a scatterplot, risk is typically charted on the x-axis and return is charted on the y-axis, so portfolios up and to the left, or in the northwest quadrant are preferred over portfolios in any other quadrant.
Doc: One MPT assumption is that all investors
are MVO aware.
Doc: In theory, right? My stockbroker says that almost no one follows MPT.
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