Stephen R. Foerster

The Effects of Market Segmentation and Investor Recognition on Asset Prices: Evidence from Foreign Stocks Listing in the United States

Non-U.S. firms cross-listing shares on U.S. exchanges as American Depositary Receipts earn cumulative abnormal returns of 19 percent during the year before listing, and an additional 1.20 percent during the listing week, but incur a loss of 14 percent during the year following listing. We show how these unusual share price changes are robust to changing market risk exposures and are related to an expansion of the shareholder base and to the amount of capital raised at the time of listing. Our tests provide support for the market segmentation hypothesis and Mertons (1987) investor recognition hypothesis. Copyright The American Finance Association 1999.

Finite sample properties of the generalized method of moments in tests of conditional asset pricing models

Abstract We develop evidence on the finite sample properties of the Generalized Method of Moments (GMM) in an asset pricing context. The models imply nonlinear, cross-equation restrictions on predictive regressions for security returns. We find that a two-stage GMM approach produces goodness-of-fit statistics that reject the restrictions too often. An iterated GMM approach has superior finite sample properties. The coefficient estimates are approximately unbiased in simpler models, but their asymptotic standard errors are understated. Simple adjustments for the standard errors are partially successful in correcting the bias. In more complex models the coefficients and their standard errors can be highly unreliable. The power of the tests to reject a single-premium model is higher against a two-premium, fixed-beta alternative than against a conditional Capital Asset Pricing Model with time-varying betas.

Trading Volume and Stock Investments

Previous studies suggest that trading-volume measures may proxy for a number of factors, including liquidity, momentum, and information. For relatively illiquid (typically smaller) stocks, investors may demand a liquidity premium, which can result in a negative relationship between trading volume (as a proxy for liquidity) and stock returns. For relatively liquid (typically larger) stocks—the focus of this article—momentum and information effects may dominate and result in a positive relationship between trading volume and stock returns. Portfolios of S&P 500 Index and large-capitalization stocks sorted on higher trading volume and turnover tend to have higher subsequent returns (holding periods of 1–12 months) than those with lower trading volume. Previous studies suggest that trading-volume measures may proxy for a number of factors, including liquidity, momentum, and information. For relatively illiquid (typically smaller) stocks, investors may demand a liquidity premium that results in a negative relationship between trading volume (as a proxy for liquidity) and stock returns. But for relatively liquid (typically larger) stocks—the focus of our study—momentum and information effects may dominate and result in a positive relationship between trading volume and stock returns. We back test simulations of historical data of the S&P 500 Index and the largest 1,000 U.S. stocks as measured by market capitalization (Largest 1,000). We derive measures of trading volume (i.e., average daily trading volume over the past three months) and turnover (i.e., annualized trading volume as a percentage of shares outstanding) from January 1991 to December 2007. We show that two trading-volume measures—trailing three-month trading volume (i.e., shares) and turnover—are monotonically related to price-to-book ratio (PB) and market capitalization (MKT). We also discover a U-shaped relationship with momentum strategies (MOM) (i.e., past six-month “winners” and “losers” both tend to experience high trading volume and turnover). We next focus on the potential profitability of long–short portfolios sorted on trading volume and turnover. We form portfolio deciles based on the trading-volume measures and compare returns for the subsequent 1-month, 3-month, 6-month, and 12-month periods. Contrary to much of the existing literature, for our sample of larger stocks, we find generally monotonic patterns, with the less (more) traded stocks (i.e., on the basis of trading volume and turnover) having lower (higher) returns. For the trading-volume measure, we find that when we regress excess (of T-bill) returns on market excess returns (i.e., the traditional capital asset pricing model), the alpha is significant for the most heavily traded portfolio. These results are even stronger when we use the three-factor Fama–French model (RmRf, SMB, and HML) and the four-factor Fama–French model (with a momentum factor, UMD, added). The alpha is also positive and significant for the highest-turnover portfolio. Results are sensitive, however, to the nature of the market (i.e., bull or bear). Finally, we create new measures (“trading-volume factors”) in the spirit of the Fama–French factors and investigate their properties. We find that their betas are generally significant when added to the Fama–French four-factor model and regressed against portfolio quintile returns based on PB, MKT, and MOM sorts. Our trading-volume factors may be related to some of the findings in the behavioral finance literature. Regardless of what a trading-volume measure might be a proxy for, it is an important consideration in any quantitatively based investment strategy. Thus, our results suggest that we may look at trading volume not only as a cost of trading (i.e., related to liquidity) but also as a source of information.

Are Cash Flows Better Stock Return Predictors than Profits

Although various income statement–based measures predict the cross section of stock returns, direct method cash flow measures have even stronger predictive power. We transform indirect method cash flow statements into disaggregated and more direct estimates of cash flows from operations and other sources and form portfolios on the basis of these measures. Stocks in the highest-cash-flow decile outperform those in the lowest by over 10% annually (risk adjusted). Our results are robust to investment horizons and across risk factors and sector controls. We also show that, in addition to operating cash flow information, cash taxes and capital expenditures provide incremental predictive power.

Don't Cover Me: Analyst Innate Ability and Insider Trading

This paper employs a novel measure of sell-side financial analyst innate ability or natural talent and explores its effects on insider trading. When a firm is covered by high-ability analysts, we find significantly less insider trading prior to positive earnings news, but not prior to negative earnings news. The results mostly reside in opportunistic trades by insiders rather than routine trades. When a firm is initially covered by an analyst, we find decreased subsequent insider trading prior to earnings news and the change in subsequent insider trading is also strongly associated with analyst innate ability. We also document an association between analyst ability and insider trading profitability. Overall, our results suggest that (1) high-ability analysts contribute more than lowability analysts to a firm’s information environment and reduce both the intensity and the profitability of insider trading; and (2) high-ability analysts may help impound firm-specific information possessed by corporate insiders. Unscrupulous insiders attempting to expropriate profits from their trades may prefer to not be covered by high-ability analysts.