A Fast Literature Search Engine based on top-quality journals, by Dr. Mingze Gao.

• Bagnoli, M., & Khanna, N. (1992). Insider Trading in Financial Signaling Models. The Journal of Finance, 47, 1905–1934.

  The authors study the impact of voluntary trade by the manager. They find that, in contrast to standard signaling models, an action is good news for some firms and bad news for others, depending on observable characteristics of the firm, its managers, and their compensation plans. Further, voluntary trade eliminates separating equilibria and, thus, the possibility of exactly inferring the manager's private information. This may cause the manager to take inefficient actions so as to earn trading profits. Such undesirable behavior can be more effectively constrained by compensation contracts based on phantom shares or nontradeable options instead of large stockholdings.

• Balachandran, B. V., & Chandra, R. (1992). More Powerful Portfolio Approaches to Regressing Abnormal Returns on Firm-Specific Variables for Cross-Sectional Studies. The Journal of Finance, 47, 2055–2070.

  Ordinary Least Squares regression ignores both heteroscedasticity and cross-correlations of abnormal returns; therefore, tests of regression coefficients are weak and biased. A portfolio ordinary least squares (POLS) regression accounts for correlations and ensures unbiasedness of tests, but does not improve their power. The authors propose portfolio weighted least squares (PWLS) and portfolio constant correlation model (PCCM) regressions to improve the power. Both utilize the heteroscedasticity of abnormal returns in estimating the coefficients; PWLS ignores the correlations, while PCCM uses intra- and inter-industry correlations. Simulation results show that both lead to more powerful tests of regression coefficients than POLS.

• Bessembinder, H., & Seguin, P. J. (1992). Futures-Trading Activity and Stock Price Volatility. The Journal of Finance, 47, 2015–2034.

  The authors examine whether greater futures-trading activity (volume and open interest) is associated with greater equity volatility. They partition each trading activity series into expected and unexpected components, and document that while equity volatility covaries positively with unexpected futures-trading volume, it is negatively related to forecastable futures-trading activity. Further, though futures-trading activity is systematically related to the futures contract life cycle, the authors find no evidence of a relation between the futures life cycle and spot equity volatility. These findings are consistent with theories predicting that active futures markets enhance the liquidity and depth of the equity markets.

• Bittlingmayer, G. (1992). Stock Returns, Real Activity, and the Trust Question. The Journal of Finance, 47, 1701–1730.

  Periodic antitrust attacks on corporations may have influenced stock prices. For the period 1904 to 1944, each antitrust case filed is associated with a 0.5 to 1.9 percent drop of the Dow and each unexpected case with even larger drops. Other aspects of antitrust besides actual filings may help account for other movements, in particular the 1929 Crash. Historical evidence bears on the question of whether antitrust is exogenous and also links antitrust and the "corporation problem." These results illustrate the sorts of real factors, aside from changes in concurrent output, that may account for stock price volatility.

• Brock, W., Lakonishok, J., & Lebaron, B. (1992). Simple Technical Trading Rules and the Stochastic Properties of Stock Returns. The Journal of Finance, 47, 1731–1764.

  This paper tests two of the simplest and most popular trading rules–moving average and trading range break–by utilizing the Dow Jones Index from 1897 to 1986. Standard statistical analysis is extended through the use of bootstrap techniques. Overall, their results provide strong support for the technical strategies. The returns obtained from these strategies are not consistent with four popular null models: the random walk, the AR(1), the GARCH-M, and the Exponential GARCH. Buy signals consistently generate higher returns than sell signals, and further, the returns following buy signals are less volatile than returns following sell signals. Moreover, returns following sell signals are negative, which is not easily explained by any of the currently existing equilibrium models.

• Cheung, Y.-W., & Ng, L. K. (1992). Stock Price Dynamics and Firm Size: An Empirical Investigation. The Journal of Finance, 47, 1985–1997.

  The authors show that after controlling for the effects of bid-ask spreads and trading volume the conditional future volatility of equity returns is negatively related to the level of stock price. This "leverage effect" is stronger for small, as compared to large, firms. The authors also document that while the essential characteristics of the relations between stock price dynamics and firm size are stable, the strengths of the relationships appear to change over time.

• Clark, R. A., Mcconnell, J. J., & Singh, M. (1992). Seasonalities in NYSE Bid-Ask Spreads and Stock Returns in January. The Journal of Finance, 47, 1999–2014.

  Using end-of-month bid-ask spreads for 540 NYSE stocks over the period 1982-87, the authors document a seasonal pattern in which both relative and absolute spreads decline from the end of December to the end of the following January. Cross-sectional regressions do not, however, provide evidence of a significant correlation between changes in spreads at the turn of the year and January stock returns. Either there is no cause and effect relation between the coincidental seasonals in bid-ask spreads and January returns for NYSE stocks or the data are too "noisy" to reveal any relation.

• Deangelo, H., Deangelo, L., & Skinner, D. J. (1992). Dividends and Losses. The Journal of Finance, 47, 1837–1863.

  An annual loss is essentially a necessary condition for dividend reductions in firms with established earnings and dividend records: 50.9 percent of 167 NYSE firms with losses during 1980-85 reduced dividends, versus 1.0 percent of 440 firms without losses. As hypothesized by Merton H. Miller and Franco Modigliani, dividend reductions depend on whether earnings include unusual items that are likely to temporarily depress income. Dividend reductions are more likely given greater current losses, less negative unusual items, and more persistent earnings difficulties. Dividend policy has information content in that knowledge that a firm has reduced dividends improves the ability of current earnings to predict future earnings.

• Gerety, M. S., & Mulherin, J. H. (1992). Trading Halts and Market Activity: An Analysis of Volume at the Open and the Close. The Journal of Finance, 47, 1765–1784.

  This paper analyzes how the daily opening and closing of financial markets affect trading volume. The authors model the desire to trade at the beginning and end of the day a a function of overnight return volatility. NYSE data from 1933-88 indicate that closing volume is positively related.to expected overnight volatility, while volume at the open is positively related to both expected and unexpected volatility from the previous night. The authors interpret the symmetric response of trading at the open and the close to expected volatility as being due to investor heterogeneities in the ability to bear risk when the market is closed. This desire of investors to trade prior to market closings indicates a cost of mandating marketwide circuit breakers.

• Green, R. C., & Hollifield, B. (1992). When Will Mean-Variance Efficient Portfolios Be Well Diversified? The Journal of Finance, 47, 1785–1809.

  The authors characterize the conditions under which efficient portfolios put small weights on individual assets. These conditions bound mean returns with measures of average absolute covariability between assets. The bounds clarify the relationship between linear asset pricing models and well-diversified efficient portfolios. The authors argue that the extreme weightings in sample efficient portfolios are due to the dominance of a single factor in equity returns. This makes it easy to diversify on subsets to reduce residual risk, while weighing the subsets to reduce factor risk simultaneously. The latter involves taking extreme positions. This behavior seems unlikely to be attributable to sampling error.