## Distribution of stock price returns

The future stock price will always be positive because stock prices cannot fall below \$0. When to Use Normal Versus Lognormal Distribution The preceding example helped us arrive at what really "Distributions of daily and monthly stock returns are rather symmetric about their means, but the tails are fatter (i.e., there are more outliers) than would be expected with normal distributions. (This topic takes up half of Gene's [Fama's] 1964 PhD thesis.)

The future stock price will always be positive because stock prices cannot fall below \$0. When to Use Normal Versus Lognormal Distribution The preceding example helped us arrive at what really "Distributions of daily and monthly stock returns are rather symmetric about their means, but the tails are fatter (i.e., there are more outliers) than would be expected with normal distributions. (This topic takes up half of Gene's [Fama's] 1964 PhD thesis.) Asset returns are often treated as normal—a stock can go up 10% or down 10%. Price levels are often treated as lognormal—a \$10 stock can go up to \$30 but it can't go down to -\$10. Stock Prices. While the returns for stocks usually have a normal distribution, the stock price itself is often log-normally distributed. This is because extreme moves become less likely as the stock's price approaches zero. Cheap stocks, also known as penny stocks, exhibit few large moves and become stagnant. distribution of stock returns is roughly twice as large as the probability that would be expected under a Normal distribution. We consider in this article the normality of the distribution of stock returns of the four

## distribution of stock returns is roughly twice as large as the probability that would be expected under a Normal distribution. We consider in this article the normality of the distribution of stock returns of the four

"Distributions of daily and monthly stock returns are rather symmetric about their means, but the tails are fatter (i.e., there are more outliers) than would be expected with normal distributions. (This topic takes up half of Gene's [Fama's] 1964 PhD thesis.) Asset returns are often treated as normal—a stock can go up 10% or down 10%. Price levels are often treated as lognormal—a \$10 stock can go up to \$30 but it can't go down to -\$10. Stock Prices. While the returns for stocks usually have a normal distribution, the stock price itself is often log-normally distributed. This is because extreme moves become less likely as the stock's price approaches zero. Cheap stocks, also known as penny stocks, exhibit few large moves and become stagnant. distribution of stock returns is roughly twice as large as the probability that would be expected under a Normal distribution. We consider in this article the normality of the distribution of stock returns of the four The distribution of stock returns is important for a variety of trading problems. The scientific portion of risk management requires an estimate of the probability of more extreme price changes. The correct distribution will tell you this. For option traders, the Black-Scholes option pricing model assumes lognormal asset price distributions.

### Asset returns are often treated as normal—a stock can go up 10% or down 10%. Price levels are often treated as lognormal—a \$10 stock can go up to \$30 but it can't go down to -\$10.

Thank you, this is a great article. I noticed a similar distribution for stock returns and similar results when fitting a gaussian distribution. Larger returns (say, 3+ standard deviations away from the mean of approximately 0) were predicted with very low frequencies, while the returns closer to 0 were a good fit to the model.

### Asset returns are often treated as normal—a stock can go up 10% or down 10%. Price levels are often treated as lognormal—a \$10 stock can go up to \$30 but it can't go down to -\$10.

"Distributions of daily and monthly stock returns are rather symmetric about their means, but the tails are fatter (i.e., there are more outliers) than would be expected with normal distributions. (This topic takes up half of Gene's [Fama's] 1964 PhD thesis.) Asset returns are often treated as normal—a stock can go up 10% or down 10%. Price levels are often treated as lognormal—a \$10 stock can go up to \$30 but it can't go down to -\$10. Stock Prices. While the returns for stocks usually have a normal distribution, the stock price itself is often log-normally distributed. This is because extreme moves become less likely as the stock's price approaches zero. Cheap stocks, also known as penny stocks, exhibit few large moves and become stagnant. distribution of stock returns is roughly twice as large as the probability that would be expected under a Normal distribution. We consider in this article the normality of the distribution of stock returns of the four The distribution of stock returns is important for a variety of trading problems. The scientific portion of risk management requires an estimate of the probability of more extreme price changes. The correct distribution will tell you this. For option traders, the Black-Scholes option pricing model assumes lognormal asset price distributions. Thank you, this is a great article. I noticed a similar distribution for stock returns and similar results when fitting a gaussian distribution. Larger returns (say, 3+ standard deviations away from the mean of approximately 0) were predicted with very low frequencies, while the returns closer to 0 were a good fit to the model. According to “ Fama & French Forum : “ Distributions of daily and monthly stock returns are rather symmetric about their means, but the tails are fatter (i.e., there are more outliers) than would be expected with normal distributions. (This topic takes up half of Eugene F. Fama's 1964 PhD thesis.

## "Distributions of daily and monthly stock returns are rather symmetric about their means, but the tails are fatter (i.e., there are more outliers) than would be expected with normal distributions. (This topic takes up half of Gene's [Fama's] 1964 PhD thesis.)

The future stock price will always be positive because stock prices cannot fall below \$0. When to Use Normal Versus Lognormal Distribution The preceding example helped us arrive at what really "Distributions of daily and monthly stock returns are rather symmetric about their means, but the tails are fatter (i.e., there are more outliers) than would be expected with normal distributions. (This topic takes up half of Gene's [Fama's] 1964 PhD thesis.) Asset returns are often treated as normal—a stock can go up 10% or down 10%. Price levels are often treated as lognormal—a \$10 stock can go up to \$30 but it can't go down to -\$10. Stock Prices. While the returns for stocks usually have a normal distribution, the stock price itself is often log-normally distributed. This is because extreme moves become less likely as the stock's price approaches zero. Cheap stocks, also known as penny stocks, exhibit few large moves and become stagnant. distribution of stock returns is roughly twice as large as the probability that would be expected under a Normal distribution. We consider in this article the normality of the distribution of stock returns of the four The distribution of stock returns is important for a variety of trading problems. The scientific portion of risk management requires an estimate of the probability of more extreme price changes. The correct distribution will tell you this. For option traders, the Black-Scholes option pricing model assumes lognormal asset price distributions. Thank you, this is a great article. I noticed a similar distribution for stock returns and similar results when fitting a gaussian distribution. Larger returns (say, 3+ standard deviations away from the mean of approximately 0) were predicted with very low frequencies, while the returns closer to 0 were a good fit to the model.

Asset returns are often treated as normal—a stock can go up 10% or down 10%. Price levels are often treated as lognormal—a \$10 stock can go up to \$30 but it can't go down to -\$10. Stock Prices. While the returns for stocks usually have a normal distribution, the stock price itself is often log-normally distributed. This is because extreme moves become less likely as the stock's price approaches zero. Cheap stocks, also known as penny stocks, exhibit few large moves and become stagnant. distribution of stock returns is roughly twice as large as the probability that would be expected under a Normal distribution. We consider in this article the normality of the distribution of stock returns of the four The distribution of stock returns is important for a variety of trading problems. The scientific portion of risk management requires an estimate of the probability of more extreme price changes. The correct distribution will tell you this. For option traders, the Black-Scholes option pricing model assumes lognormal asset price distributions. Thank you, this is a great article. I noticed a similar distribution for stock returns and similar results when fitting a gaussian distribution. Larger returns (say, 3+ standard deviations away from the mean of approximately 0) were predicted with very low frequencies, while the returns closer to 0 were a good fit to the model. According to “ Fama & French Forum : “ Distributions of daily and monthly stock returns are rather symmetric about their means, but the tails are fatter (i.e., there are more outliers) than would be expected with normal distributions. (This topic takes up half of Eugene F. Fama's 1964 PhD thesis. Some active investors model variations of a stock or other asset to simulate its price and that of the instruments that are based on it, such as derivatives. Simulating the value of an asset on an