We examine aftermarket transactions for closed-end fund IPOs and document large sell-to-buy imbalances (“flipping”), extensive price stabilization, and sharp subsequent price drops. The timing of the price drop is related to both the amount of initial flipping, and use of the over-allotment options. The extent of the flipping activity is related to the composition of the syndicate. Moreover, aftermarket buys (sells) are mainly small (large) trades. These findings suggest that lead underwriters price stabilize and manage the supply of shares in the aftermarket, and that closed-end fund IPOs are marketed to a poorly informed public.

In this study, we propose an alternative technique for estimating the cost of equity capital. Specifically, we use a discounted residual income model to generate a market implied cost‐of‐capital. We then examine firm characteristics that are systematically related to this estimate of cost‐of‐capital. We show that a firm's implied cost‐of‐capital is a function of its industry membership, B/M ratio, forecasted long‐term growth rate, and the dispersion in analyst earnings forecasts. Together, these variables explain around 60% of the cross‐sectional variation in future (two‐year‐ahead) implied costs‐of‐capital. The stability of these long‐term relations suggests they can be exploited to estimate future costs‐of‐capital. We discuss the implications of these findings for capital budgeting, investment decisions, and valuation research.

We show that analyst coverage proxies contain information about expected returns. We decompose analyst coverage into abnormal and expected components using a simple characteristic-based model and show that firms with abnormally high analyst coverage subsequently outperform firms with abnormally low coverage by approximately 80 basis points per month. We also show abnormal coverage rises following exogenous shocks to underpricing and predicts improvements in firms’ fundamental performance, suggesting that return predictability stems from analysts more heavily covering underpriced stocks. Our findings highlight the usefulness of analysts’ actions in expected return estimations, and a potential inference problem when coverage proxies are used to study information asymmetry and dissemination.

This study presents a general approach for selecting comparable firms in market‐based research and equity valuation. Guided by valuation theory, we develop a “warranted multiple” for each firm, and identify peer firms as those having the closest warranted multiple. We test this approach by examining the efficacy of the selected comparable firms in predicting future (one‐ to three‐year‐ahead) enterprise‐value‐to‐sales and price‐to‐book ratios. Our tests encompass the general universe of stocks as well as a sub‐population of so‐called “new economy” stocks. We conclude that comparable firms selected in this manner offer sharp improvements over comparable firms selected on the basis of other techniques.

We model the time‐series relation between price and intrinsic value as a cointegrated system, so that price and value are long‐term convergent. In this framework, we compare the performance of alternative estimates of intrinsic value for the Dow 30 stocks. During 1963–1996, traditional market multiples (e.g., B/P, E/P, and D/P ratios) have little predictive power. However, a V/P ratio, where V is based on a residual income valuation model, has statistically reliable predictive power. Further analysis shows time‐varying interest rates and analyst forecasts are important to the success of V. Alternative forecast horizons and risk premia are less important.

ABSTRACT For New York Stock Exchange (NYSE) listed securities, the price execution of seemingly comparable orders differs systematically by location. In general, executions at the Cincinnati, Midwest, and New York stock exchanges are most favorable to trade initiators, while executions at the National Association of Security Dealers (NASD) are least favorable. These intermarket price differences depend on trade size, with the smallest trades exhibiting the biggest per share price difference. Collectively, these results raise questions about the adequacy of the existing intermarket quote system (ITS), the broker's fiduciary responsibility for “best execution,” and the propriety of order flow inducements.

Abstract ABSTRACT: We present evidence on inter-firm differences in the predictive ability of quarterly earnings data for a sample of 109 New York Stock Exchange firms. The sample consisted of large, medium, and small firms after deletion of nonseasonal and volatile growth and inconsistent strata membership firms. Although the structure of the best fitting time-series models was constant across firm-size strata, we did find significant differences in the autoregressive parameters of the Foster and Brown and Rozeff ARIMA models across firm-size strata. One-step-ahead quarterly earnings forecasts were generated by a set of best fitting time-series models. A repeated measure multivariate analysis of variance design indicated that predictive ability differed on the basis of size at the .012 level. Tests also indicated that large-and medium-size firms generated one-step-ahead forecasts that were significantly more accurate than smaller firms at the .05 level. We obtained similar predictive findings on the significance of the size-effect in a supplementary analysis of the nonseasonal and volatile growth and inconsistent strata membership firms.

From 2007 to 2020, unlisted Chinese firms paid an average of over US $500 million to listed firms for their shell value in reverse merger transactions. We show that this large shadow price for a public listing sheds light on other features of Chinese markets, including (i) near-zero mortality rates, (ii) frequent major-asset restructurings (MARs), (iii) insensitivity of small-firm prices to corporate earnings, and (iv) a large size effect. A firm-level measure of expected shell probability (ESP) predicts stock returns, MARs, earnings-to-price sensitivity, and short-window returns to initial public offering-related regulatory news. Furthermore, adding ESP to existing pricing models for Chinese stocks significantly improves model performance.