How do you interpret skewness in stock returns?

How do you interpret skewness in stock returns?

Understanding Skewness The mean of positively skewed data will be greater than the median. In a distribution that is negatively skewed, the exact opposite is the case: the mean of negatively skewed data will be less than the median.

Are stock returns skewed?

Abstract. Aggregate stock market returns display negative skewness. Firm stock returns display positive skewness. The large literature that tries to explain the first stylized fact ignores the second.

Are stock returns positively or negatively skewed?

Among the findings is that at the firm level, the expected skewness negatively affects stock returns – high idiosyncratic skewness is associated with low expected returns. However, as skewness increases and becomes positive, the positive relation between volatility and returns becomes a negative relation.

What does skewness tell us about returns?

Applied to financial markets, skewness measures the degree of return asymmetry in terms of the probability distribution around the mean. In English, skewness tells us if returns have been extreme or not. A relatively high positive skewness reading indicates returns deep in the right tail of the distribution.

Why is negative skewness bad?

A negative skew is generally not good, because it highlights the risk of left tail events or what are sometimes referred to as “black swan events.” While a consistent and steady track record with a positive mean would be a great thing, if the track record has a negative skew then you should proceed with caution.

Why is skewness important?

The primary reason skew is important is that analysis based on normal distributions incorrectly estimates expected returns and risk. Knowing that the market has a 70% probability of going up and a 30% probability of going down may appear helpful if you rely on normal distributions.

Is skewness good or bad?

Skewness provides valuable information about the distribution of returns. However, skewness must be viewed in conjunction with the overall level of returns. Skewness by itself isn’t very useful. It is entirely possible to have positive skewness (good) but an average annualized return with a low or negative value (bad).

Is a positive skew good?

A positive mean with a positive skew is good, while a negative mean with a positive skew is not good. If a data set has a positive skew, but the mean of the returns is negative, it means that overall performance is negative, but the outlier months are positive.

How do you deal with skewed data?

Dealing with skew data:

1. log transformation: transform skewed distribution to a normal distribution.
2. Remove outliers.
3. Normalize (min-max)
4. Cube root: when values are too large.
5. Square root: applied only to positive values.
6. Reciprocal.
7. Square: apply on left skew.

What is a good skewness value?

The rule of thumb seems to be: If the skewness is between -0.5 and 0.5, the data are fairly symmetrical. If the skewness is between -1 and – 0.5 or between 0.5 and 1, the data are moderately skewed. If the skewness is less than -1 or greater than 1, the data are highly skewed.

What value of skewness is acceptable?

Acceptable values of skewness fall between − 3 and + 3, and kurtosis is appropriate from a range of − 10 to + 10 when utilizing SEM (Brown, 2006).

What is the impact of skewness on returns?

There is strong evidence of a negative cross-sectional relationship between realized skewness and future stock returns – stocks with negative skewness are compensated with high future returns for higher volatility. Realized skewness is a significant indicator of returns across commodities, government bonds, and equity indices.

How does idiosyncratic skewness affect stock returns?

Among the findings is that at the firm level, the expected skewness negatively affects stock returns – high idiosyncratic skewness is associated with low expected returns.

What happens to the mean of a skewed distribution?

In a distribution that is negatively skewed, the exact opposite is the case: the mean of negatively skewed data will be less than the median. If the data graph symmetrically, the distribution has zero skewness, regardless of how long or fat the tails are.

Which is an example of a negative skew?

Negative skewness occurs when the values to the left of (less than) the mean are fewer but farther from it than values to the right of (greater than) the mean. For example, the return series of -30 percent, 5 percent, 10 percent and 15 percent has a mean of 0 percent. There is only one return less than zero and three that are higher.