Predicting long-term stock return volatility: implications for accounting and valuation of equity derivatives

Article Abstract:

A study was conducted to test the prediction of long-term stock return volatility. Volatility was computed using monthly continuously compounded stock returns over the five years after the date of forecast. Results showed that, when historical volatility is used to predict five-year monthly volatility, returns should be measured either weekly or monthly and the historical period should be about five years. Comparable firms should be chosen based on industry and firm size when forecasting is based exclusively on historical volatilities of comparable firms. Lastly, it was found that a shrinkage forecast performed through adjustments in a historical forecast toward a comparable-firms forecast is more precise than either a historical or a comparable-firms forecast. These findings imply that errors in pricing employee stock options as a result of errors in the prediction of long-term volatility would not have a significant material effect on net income.

Stock options

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Using analysts' forecasts to measure properties of analysts' information environment

Article Abstract:

A study based on a model of expectations examined the relationship between analysts' forecasts and their information environment. In the model, each analyst monitors common information and idiosyncratic information, which serve as the two signals of future earnings. These two types of information generate forecast errors and dispersion. Moreover, hidden properties of the information environment are observed through observable constructs. Forecast dispersion was found to arise only from idiosyncratic error. Error in individual forecasts was indicative of common and idiosyncratic error while error in the mean forecast indicated common error although it may also represent idiosyncratic error. The findings showed that the expected forecast dispersion is an increasing function of uncertainty and a decreasing function of consensus. Expected squared error is an increasing function of both.

Investment advisers, Stock price forecasting, Information theory

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Trading volume and belief revisions that differ among individual analysts

Article Abstract:

The relationship between trading volume and belief revisions that differ among individual analysts is examined. Two predictions are tested in this study. The first one predicts that differential belief revisions spur trading volume. The other holds that differential belief revisions have a different influence on trading volume than diversity in past beliefs. Results show that differential belief revisions and prior dispersion in beliefs are factors both affecting trading volume, similar to what is seen in Karpoff's (1986) study. If the influence of the average belief revision made by investors is controlled for by the magnitude of price changes, then the prediction that both average and differential components of belief revisions influence trading volume.

Stock-exchange, Stock exchanges, Investments

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