Explain the problem of kurtosis in estimating expected return. Give at least 2 arguments.

Kurtosis is a statistical measure that describes the distribution of observed data around the mean. It is used in finance to understand the volatility and risk associated with a particular investment. However, there are several problems associated with using kurtosis in estimating expected return.

1. Sensitivity to Outliers: Kurtosis is highly sensitive to outliers. This means that a few extreme values can significantly affect the kurtosis of a dataset. In the context of estimating expected return, this could lead to overestimation or underestimation of potential returns. For example, if an investment has historically had a few periods of extremely high returns, the kurtosis of the return distribution would be high. This could lead an investor to expect high returns in the future, even though these extreme returns may not be likely to occur again.

2. Assumes Normal Distribution: Kurtosis assumes that data is normally distributed. However, this is often not the case in financial markets. Returns can be skewed due to a variety of factors, such as market sentiment, economic conditions, and company-specific news. This can lead to a misestimation of expected return. For example, if returns are negatively skewed (i.e., there are more extreme negative returns than positive ones), an investor might underestimate the risk of loss and overestimate the expected return.

In conclusion, while kurtosis can provide some insight into the risk and volatility of an investment, it should not be used in isolation to estimate expected return. Other factors, such as the overall economic environment, company performance, and market sentiment, should also be considered.