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Log-Normal Distribution


A Log-Normal Distribution is a statistic term applied in financial analysis, where the log of a variable has a normal distribution. In finance, it often models stock prices because they can’t become negative and they have the potential for large upward swings. The distribution is skewed to the right, characterized by a larger tail on the right side.


The phonetics of “Log-Normal Distribution” is: lɒg-nɔːrməl dɪstrɪˈbjuːʃ(ə)n

Key Takeaways

<ol> <li>Log-Normal Distribution represents data that is positively skewed. It describes a situation where values are concentrated on one side and are sparse on the other side. In other words, it characteristically deals with values that do not follow a symmetric distribution and have a long tail on the right.</li> <li>Unlike Normal Distribution, Log-Normal Distribution does not include negative values as it deals with logarithmic values of normal distribution. This means the values in a log-normal distribution must be positive.</li> <li>It has numerous applications across various fields such as economics, biology, and engineering. It’s used to model a wide range of phenomena, including stock prices, survival rates, oil reserves, rainfall rates, and many others.</li></ol>


The Log-Normal Distribution is pivotal in finance and business due to its unique properties in modeling a variety of natural phenomena, especially those that cannot fall below zero, like stock prices or house values. This statistical distribution only takes positive values, making it an excellent tool for variables that cannot go into the negatives. Additionally, the log-normal distribution is skewed to the right, which matches common financial scenarios where many small losses are paired with a few extreme gains. It is also a key element in the Black-Scholes model for options pricing, a cornerstone of modern finance. Therefore, understanding the log-normal distribution is critical for effective financial modeling and business forecasting.


The Log-Normal Distribution is commonly used in the finance and business sectors for various purposes. It’s particularly relevant for modeling what’s known as asset prices or returns on these assets over time. This is because the log-normal distribution, unlike a normal distribution, does not allow for negative values. This characteristic makes it more suitable for financial modeling as asset prices and returns can’t generally be less than zero.Furthermore, due to its skewed nature, it also accurately represents financial phenomena where there are commonly greater probabilities of smaller increases in prices or returns, but with the existence of a potential for significantly larger increases, albeit less frequently. This concept aligns well with the performance of many investments, where small gains frequently occur, but over time there could be considerable gains. Therefore, applying log-normal distribution in financial modeling allows for a realistic and practical reflection of market conditions and trends.


1. Stock Prices: In finance, the log-normal distribution is widely used to describe the price changes of financial assets, such as stocks. The reason why stock prices follow a log-normal distribution is that they can’t fall below zero and have the potential to rise indefinitely. Also, stock prices tend to change in percentage terms. For example, a $5 rise in a $10 stock is a bigger event than a $5 rise in a $100 stock. Thus, when plotting these changes on a graph, they take on a log-normal distribution.2. House Prices: Similarly, the prices of houses/real estate can also be modeled by a log-normal distribution. This is because, like stocks, house prices are positive quantities that show a wide range of variation. This model can be particularly useful when analyzing or predicting house prices, for example during a housing market boom or recession.3. Wage Distribution: In the labor market, wages are frequently distributed in a log-normal model. Workers’ incomes tend to increase by a fixed percentage every year rather than by a fixed dollar amount. Just like stock prices, wages can’t fall below zero, they have potential for unlimited growth, and small variations can make a big difference. So, if we plot wage increases using a logarithmic scale, the graph will display a normal distribution, hence depicting a log-normal distribution.

Frequently Asked Questions(FAQ)

What exactly is a Log-Normal Distribution?

A Log-Normal Distribution is a statistical distribution of logarithmic values from a related normal distribution. This means that while the original values are not normally distributed, their logarithms are. Log-Normal Distributions are commonly used in the financial industry to model price distributions of assets like shares or commodities.

What distinguishes a Log-Normal Distribution from a Normal Distribution?

Log-Normal Distributions differ from Normal Distributions in their shape. Normal Distributions are symmetrical around the mean while Log-Normal Distributions are skewed to the right, with a long tail on the positive side. This is because values in a Log-Normal Distribution can only be positive.

Could you give an example of when a Log-Normal Distribution might be used in finance?

Sure, Log-Normal Distributions are commonly used to model stock prices because such prices can’t fall below zero but have unlimited upside potential. In this sense, the use of a Log-Normal Distribution appropriately reflects the skewed nature of such financial data.

Can Log-Normal Distributions be used to calculate probabilities?

Absolutely. Log-Normal Distributions permit calculations of the probability that a value will exceed a certain level, or that it will fall within a specified range, much like other distributions.

Is there a certain form of equation for Log-Normal Distributions?

Yes, much like the normal distribution, there exists a probability density function for the Log-Normal Distribution. While it is conceptually similar to the normal distribution, its mathematical form is considerably different, being distinguished by the use of the logarithm of the variable.

How are means and variances calculated in a Log-Normal Distribution?

The mean, or expected value, and variance of a log-normal distribution are calculated from the mean and variance of the underlying normally distributed random variable. The exact formulas involve exponentiation and may not be straightforward; it is usually easier to perform calculations on the natural logarithms of the numbers in question.

Why are Log-Normal Distributions significant?

Log-Normal Distributions play a crucial role in the world of finance and business because they give us a better representation of various financial scenarios and variables such as stock prices and commodities. Their unique characteristics allow us to model complex and real-world situations more accurately.

Related Finance Terms

  • Geometric Brownian Motion
  • Black-Scholes Model
  • Stochastic Processes
  • Volatility
  • Stock Market Returns

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