A portfolio in simple terms is a group of assets or a pool of investment opportunities for an investor. On the other hand, Portfolio optimization is the method of selecting the most suitable portfolio which, in the upcoming future, yields the most possible return for the investor. The portfolio and the return it generated depends on various factor such as risk appetite, expected rate of return, cost minimization, etc. Although, investing in a portfolio is very advantageous for the investor but it has some limitations too.
Limitations of Portfolio Optimization
Portfolio optimization is based on the modern portfolio theory which has some assumptions. One of its assumptions is that the market in which two users interact is frictionless. The term frictionless means that there is no transactional cost, constraints, etc. prevailing in the market. But, in the real world, these assumption does not hold any space as there is a presence of friction in the market. Here, the modern portfolio theory contravenes itself.
Complex and hard
In portfolio optimization, the use of spreadsheets is a must. Using a spreadsheet leads to several problems when there is a lot of data. Further, the use of data in spreadsheets makes it complex and hard. Even if you are using some other methods, still the calculation, estimation or valuation is quite complex and hard.
Normal distribution of returns
One more assumption of modern portfolio optimization is that there is a normal distribution of returns. But in the real world, there is no normal distribution of return. There are always fluctuations. So this assumption not only contravenes the real world but also ignores the concepts of skewness, kurtosis, etc.
Change in coefficient
The data for portfolio optimization is calculated by using various coefficients such as the correlation coefficient. But these coefficient keeps changing with a change in market situations. This affects and contravenes the assumption of modern portfolio theory that the coefficient always remains the same.
The portfolio optimization theory ignores the estimation error and inherent other inputs such as expected return. This error maximization theory affects the results of portfolios and leads to the selection of an inefficient portfolio.
Restrictive mean-variance optimization
There is an assumption that when returns are normally distributed (which can never happen in the real world), then mean-variance optimization is purposeful. But, the fact is that when returns are normally distributed, only mean-variance optimization can be used and other optimization methods are useless. Moreover, if returns are not distributed normally, mean-variance cannot be used and it becomes restrictive. So, to summarize, the mean-variance optimization method is hard to use in the practical world.