literature by providing evidence of dispersion being priced in the cross-section of stock returns. The forecasting power of aggregate idiosyncratic risk over market 

For instance, correlation may increase / dispersion decrease because the market is pricing in a drastic economic downturn and the earnings power of all companies are all hindered drastically (fundamental). Alternatively, as the market de-risks, Consistent with this model, we find that stocks with higher sensitivities to return dispersion have higher average returns, and that return dispersion carries a significant positive price of risk. In particular, the return dispersion factor dominates the book-to-market factor in explaining cross-sectional expected returns. Return dispersion is also known as cross-sectional volatility: It measures whether stocks’ returns are converging (low dispersion) or diverging (high dispersion) relative to their benchmark. Low dispersion tends to be indicative of excessive groupthink, so it’s usually an indicator of underlying market strain. Earnings Dispersion and Contemporaneous Stock Returns This table reports time series regression results for contemporaneous stock returns. The dependent variables are equal-weighted (R t_ew) and value-weighted (R t_vw) returns in year t. These returns are measured from April of year t to March of year t+1. CRSPvw t is the CRSP

dispersion and stock return volatility has studied periods of earnings announcements, we look at such a relationship on an on-going basis, without reference to formal accounting events and disclosures. Earlier research has also examined the relationship between stock return volatility and forecast dispersion.

literature by providing evidence of dispersion being priced in the cross-section of stock returns. The forecasting power of aggregate idiosyncratic risk over market  We extend Sharpe's argument about the link between the average return on stocks and funds to stock and fund return dispersion. If the cross- sectional variation of  The highest hedge returns obtain for least liquid stocks. •. The dispersion effect's abnormal returns occur in a very narrow time frame. •. Exploiting dispersion  This paper studies three different measures of monthly stock market volatility: the time-series volatility of daily market returns within the month; the cross-sectional  Stock market return dispersion (RD) – defined as the cross sectional standard deviation of returns from either individual stocks or from disaggregate stock  We find that the resulting economic uncertainty betas predict a significant proportion of the cross-sectional dispersion in stock returns. Stocks in the lowest  

This paper studies three different measures of monthly stock market volatility: the time-series volatility of daily market returns within the month; the cross-sectional 

27 Sep 2016 Stock analysts often refer to the “dispersion” of returns in a sector: the degree of variance in returns within that sector over time. In general  Let us construct our own index out of six stocks (none of which pay dividends). Let us call it the “Dew Jeans 6” or DJ6. Here are the 6 stock symbols: Symbol:. Put: an option to sell stock at strike price within a month anytime the stock price goes below the strike price. 1 comment.

11 Jan 2019 Recent literature has documented that equity return dispersion, measured by the cross-sectional standard deviation of stock returns, either at the 

Individual stocks can be found on Morningstar and similar stock rating companies. The dispersion of return on an asset shows the volatility and risk associated with holding that asset. To study how stock returns vary by geographic dispersion, we require cross-sectional variation in dispersion that is independent of other firm characteristics known to be related to returns. Panel B shows, that even within size terciles, there is a significant amount of variation in geographic dispersion. Equity return dispersion is measured as the standard deviation of returns across different stocks or portfolios. Unlike volatility it can be measured even for a single relevant period and, thus, can record changing market conditions fast. Stock market return dispersion (RD) – defined as the cross sectional standard deviation of returns from either individual stocks or from disaggregate stock portfolios – provides a timely, easy to calculate at any time frequency, model free measure of volatility. This paper studies three different measures of monthly stock market volatility: the time-series volatility of daily market returns within the month; the cross-sectional volatility or 'dispersion' of daily returns on industry portfolios, relative to the market, within the month; and the dispersion of daily returns on individual firms, relative to their industries, within the month. Dispersion refers to the cross-sectional standard deviation of stock returns around their mean on a particular day. The VIX, of course, is the CBOE implied volatility measure, computed from the implied volatilities of various S&P 500 index options. dispersion and stock return volatility has studied periods of earnings announcements, we look at such a relationship on an on-going basis, without reference to formal accounting events and disclosures. Earlier research has also examined the relationship between stock return volatility and forecast dispersion.

the long-term. Further, in periods when the dispersion of stock returns within the benchmark has been high, the median manager has delivered strong alpha.

Notice how in each case, while the simple average is 10%, the compound average declines as the dispersion of returns widens. However, half the time the stock market moves up or down by 16% or more in a year. In the last two examples, there were losses in one of the years. For instance, correlation may increase / dispersion decrease because the market is pricing in a drastic economic downturn and the earnings power of all companies are all hindered drastically (fundamental). Alternatively, as the market de-risks, Consistent with this model, we find that stocks with higher sensitivities to return dispersion have higher average returns, and that return dispersion carries a significant positive price of risk. In particular, the return dispersion factor dominates the book-to-market factor in explaining cross-sectional expected returns.

A further study that mostly resembles the current study examines the role of geographic dispersion on stock returns (Garcia & Norli, 2012). Using a large sample of U.S. publicly traded firms dispersion and stock return volatility has studied periods of earnings announcements, we look at such a relationship on an on-going basis, without reference to formal accounting events and disclosures. Earlier research has also examined the relationship between stock return volatility and forecast dispersion. Volatility is measured by realized returns of the stock market with a one-month lookback. Dispersion is calculated as the absolute differences between stock prices and the market average on a daily basis, which is then averaged over a one-month period.