We examine the effects of market making and intermittent trading on estimates of stock price volatility. When observed price changes are correctly tied to a stock's true price dynamics, it is found that non-trading per se causes a loss of efficiency but no bias in traditional volatility estimates. Non-trading induces substancial inefficiency in the extreme value estimator of volatility which it biases downward. Market making's effects add to the non-trading induced inefficiency in the traditional estimator, while information trading causes a downward bias, and liquidity trading a potentially removable upward bias, in that estimator.

HD28 .M414 no.1584-, 84, Stocks Prices,

This paper is concerned with the size-related anomalies in stock returns reported by Banz (1981) and Reinganum (1981). They showed that small firms have tended to yield returns greater than those predicted by the traditional CAPM. We find that the size effect is linear in the logarithm of size, but reject the hypothesis that the ex ante excess return attributable to size is stable through time. We briefly analyze the Seemingly Unrelated Regression Model (SURM) and a two-step procedure as two alternative estimators of the size effect. Due to the instability of the effect, we find that the estimates are sensitive to the time period studied.

A ‘tax-loss selling’ hypothesis has frequently been advanced to explain the ‘January effect’ reported in this issue by Keim. This paper concludes that U.S. tax laws do not unambiguously predict such an effect. Since Australia has similar tax laws but a July–June tax year, the hypothesis predicts a small-firm July premium. Australian returns show pronounced December–January and July–August seasonals, and a premium for the smallest-firm decile of about four percent per month across all months. This contrasts with the U.S. data in which the small-firm premium is concentrated in January. We conclude that the relation between the U.S. tax year and the January seasonal may be more correlation than causation.