Definition
A Positively Skewed Distribution, also known as right-skewed distribution, in finance refers to a type of distribution where, on a graph, the right side (tail) is longer or fatter than the left side. It suggests that there are a number of outlier values that are greater than the mean, hence dragging the curve to the right. Most of the data, including the median, falls on the left of the mean in such a distribution.
Key Takeaways
- Positively Skewed Distribution, often referred to as right-skewed distribution, is a type of distribution where the values on the right side of the histogram (tail) stretch more towards the right while the majority of the data points fall to the left.
- In a Positively Skewed Distribution, the mean and median are greater than the mode. This is due to the influence of the outliers (extreme high values) that tend to pull the mean towards the right.
- Positively Skewed Distributions are very common in the world of finance, specifically in areas dealing with stock returns, real estate values or company earnings, where outliers can dramatically skew the data to the right.
Importance
Understanding the concept of a Positively Skewed Distribution is important in finance because it provides valuable insights into the nature of a dataset or a probability distribution.
It helps in determining the performance or return on an investment, where most of the values fall to the left (lower side) of the mean and the tail of the distribution stretches towards the right (higher side) indicating rare instances of extremely high returns.
A positively skewed distribution represents a scenario where the possibility of obtaining a return higher than the average return would not be frequent, but the potential magnitude of such a return could be large.
Hence, it influences the decision-making process of investors by highlighting the potential risks and rewards associated with an investment.
Explanation
Positively Skewed Distribution plays a significant role in finance by offering crucial insights into the asymmetry of data distribution. In a practical context, it aids financial analysts, statisticians, and economists in understanding the risk and patterns associated with a particular set of financial data.
For example, an investment with a positively skewed distribution of returns implies that the likelihood of larger gains is higher than the likelihood of large losses. Thus, the positively skewed distribution assists in determining risk-return tradeoffs and making informed decisions about where to invest.
Additionally, it is used to measure discrepancy and volatility in market data. For instance, in stock market analysis, a positively skewed distribution generally means that the most frequent returns are less than the average return, and there are occasional periods with extremely high returns.
This helps analysts in forecasting future trends and identifying investment opportunities with potentially higher profitability. Moreover, it helps in interpreting economic data, such as income distributions in an economy, where a positive skew might indicate that a significant portion of the population falls below the mean income level, with a few people earning significantly more – a common scenario in many economies.
Examples of Positively Skewed Distribution
Real Estate Prices: In many cities, real estate prices tend to be positively skewed. This is because while most properties fall within a certain standardized price range, there are always a few ‘luxury’ properties that have significantly higher prices. These few exceptionally high-priced houses skew the distribution to the right creating a longer right tail.
Stock Market Returns: If we analyze the annual returns of the stock market, the distribution of the returns is not symmetrical. Instead, they often exhibit positive skewness. Most years, the returns will be average or negative, but there will be some ‘boom’ years when the returns are significantly high, skewing the distribution to the right.
Household Income: If you were to plot the distribution of income in many countries, you will find a positively skewed distribution. A majority of the population fall in the middle to low-income category, but a small percentage earn extremely high income. These outlying high-income earners create a longer right tail in the distribution, making it positively skewed.
FAQs for Positively Skewed Distribution
What is Positively Skewed Distribution?
A positively skewed distribution, sometimes called right-skewed distribution, refers to a type of distribution where most values are clustered around the left tail, while the right tail is longer. This indicates that the mean, median, and mode of the data are not equal, where the mean is greater than the median, and the median is greater than the mode.
What causes a Positively Skewed Distribution?
A positively skewed distribution often occurs when the data has a range that is bounded to the right, such as height, weight, or age of people. In these cases, a natural or physical barrier prevents outcomes on the upper end, causing a clustering on the lower end thus the longer right tail.
How is skewness measured in a Positively Skewed Distribution?
Skewness in a distribution can be measured by a skewness coefficient, denoted as g1. Positively skewed distributions have a g1 greater than zero. The larger the number, the more skewness is present in the direction.
What is the significance of Positive Skewness in finance?
In finance, positive skewness is commonly seen in the distribution of stock returns. Companies most often generate small positive returns, while negative returns are not as frequent but can be extremely large when they happen – such as during a financial crisis. Understanding skewness allows investors, economists, and policy makers to make better decisions about risk management.
Related Entrepreneurship Terms
- Normal Distribution
- Kurtosis
- Right-Skewed Distribution
- Skewness
- Statistical Asymmetry
