Testing the Duo-factor-model of Return and Volume

Recent theoretical work by Lo and Wang (2000) shows that a multi-factor assetpricing model not only imposes factor restrictions on stock returns but on trading volume as well. We explicitly test their theoretical result using individual stock return and turnover data from NYSE and AMEX from 1962 to 1996. We introduce a recently developed consistent statistic by Bai and Ng (2001a) to determine the number of factors in a duo approximate multi factor model for return and turnover. While we find that the duo-factor model captures a great deal of common variation of trading volume, the data rejects a model restriction that excess return and turnover should have the same number of systematic factors. Using the duo-factor-model, we decompose excess return and turnover into systematic and idiosyncratic components. Our empirical work discovers a significant increase in the variation of idiosyncratic turnover through time, analogous to the discovery of a noticeable increase in firm level volatility by Campbell, Lettau, Malkiel and Xu (2001). We also find significant co-movement between volatility and turnover at the systematic levels. Our findings support the view that trading volume is not purely random but driven by trading activities associated with macroeconomic and firm news

Turning Over Turnover

The methodology of Bai and Ng (2002, 2003) for decomposing large panel data into systematic and idiosyncratic components is applied to both returns and turnover. Combining this with a GLS-based principal components approach, we demonstrate that their procedure works well for both returns and turnover despite the presence of severe heteroscedasticity and non-stationarity in turnover of individual stocks. We then test Lo and Wang's (2000) theoretical model's restriction that returns and turnover should have the same number of systematic factors. This is songly rejected by the data, suggesting stock price and trading volume may not be compatible under the existing multi-factor asset pricing-trading framework. We also demonsate that several commonly used turnover measures may understate the price impact of stock trading

TURNING OVER TURNOVER

The methodology of Bai and Ng (2002, 2003) for decomposing large panel data into systematic and idiosyncratic components is applied to both returns and turnover. Combining this with a GLS-based principal components approach, we demonstrate that their procedure works well for both returns and turnover despite the presence of severe heteroscedasticity and non-stationarity in turnover of individual stocks. We then test Lo and Wang’s (2000) theoretical model’s restriction that returns and turnover should have the same number of systematic factors. This is strongly rejected by the data, suggesting stock price and trading volume may not be compatible under the existing multi-factor asset pricing-trading framework. We also demonstrate that several commonly used turnover measures may understate the price impact of stock trading

Institutional Investors, Corporate Governance, and Firm Value

In the corporate governance debate, the short-term versus longterm contention has grown into perhaps today’s most controversial topic. In this debate, descriptions of institutional investors tend to present a dichotomic nature. These investors are alternatively portrayed as homogenously short-termist or as consistent “forces for good,” focused on targeting underperforming companies. This Article moves beyond this dichotomy. It shows empirically that aggregate institutional investor behavior presents nuances that depend on a variety of factors, including individual firm characteristics, institutional ownership levels, and institutional propensity toward activism

The Shareholder Value of Empowered Boards

In the last decade, the balance of power between shareholders and boards has shifted dramatically. Changes in both the marketplace and the legal landscape governing it have turned the call for empowered shareholders into a new reality. Correspondingly, the authority that boards of directors have historically held in U.S. corporate law has been eroded. Empirical studies associating staggered boards with lower firm value have been interpreted to favor this shift of authority, supporting the view that protecting boards from shareholder pressure is detrimental to shareholder interests. This Article presents new empirical evidence on staggered boards that not only exposes the limitations of prior empirical studies, but also, and more importantly, suggests the opposite conclusion. Employing a unique and comprehensive dataset covering thirty-four years of board staggering and destaggering decisions—from 1978 to 2011—we show that staggered boards are associated with a statistically and economically significant increase in firm value. In light of these novel empirical results, we then show theoretically that a corporate model with staggered boards emerges as a rational institutional response to market imperfections that are more complex and more significant than shareholder advocates have realized. Boards that retain their historical authority—empowered boards—benefit, rather than hurt, shareholders. This Article concludes with a normative proposal to revitalize the authority of U.S. boards.

The Shareholder Value of Empowered Boards

In the last decade, the balance of power between shareholders and boards has shifted dramatically. Changes in both the marketplace and the legal landscape governing it have turned the call for empowered shareholders into a new reality. Correspondingly, the authority that boards of directors have historically held in U.S. corporate law has been eroded. Empirical studies associating staggered boards with lower firm value have been interpreted to favor this shift of authority, supporting the view that protecting boards from shareholder pressure is detrimental to shareholder interests. This Article presents new empirical evidence on staggered boards that not only exposes the limitations of prior empirical studies, but also, and more importantly, suggests the opposite conclusion. Employing a unique and comprehensive dataset covering thirty-four years of board staggering and destaggering decisions—from 1978 to 2011—we show that staggered boards are associated with a statistically and economically significant increase in firm value. In light of these novel empirical results, we then show theoretically that a corporate model with staggered boards emerges as a rational institutional response to market imperfections that are more complex and more significant than shareholder advocates have realized. Boards that retain their historical authority—empowered boards—benefit, rather than hurt, shareholders. This Article concludes with a normative proposal to revitalize the authority of U.S. boards.

TURNING OVER TURNOVER

The methodology of Bai and Ng (2002, 2003) for decomposing large panel data into systematic and idiosyncratic components is applied to both returns and turnover. Combining this with a GLS-based principal components approach, we demonstrate that their procedure works well for both returns and turnover despite the presence of severe heteroscedasticity and non-stationarity in turnover of individual stocks. We then test Lo and Wang’s (2000) theoretical model’s restriction that returns and turnover should have the same number of systematic factors. This is strongly rejected by the data, suggesting stock price and trading volume may not be compatible under the existing multi-factor asset pricing-trading framework. We also demonstrate that several commonly used turnover measures may understate the price impact of stock trading

Turning Over Turnover

The methodology of Bai and Ng (2002, 2003) for decomposing large panel data into systematic and idiosyncratic components is applied to both returns and turnover. Combining this with a GLS-based principal components approach, we demonstrate that their procedure works well for both returns and turnover despite the presence of severe heteroscedasticity and non-stationarity in turnover of individual stocks. We then test Lo and Wang's (2000) theoretical model's restriction that returns and turnover should have the same number of systematic factors. This is songly rejected by the data, suggesting stock price and trading volume may not be compatible under the existing multi-factor asset pricing-trading framework. We also demonsate that several commonly used turnover measures may understate the price impact of stock trading