We document several factors that help explain cross-sectional variations in the post-revision price drift associated with analyst forecast revisions. First, the market does not make a sufficient distinction between revisions that provide new information (“high-innovation” revisions) and revisions that merely move toward the consensus (“low-innovation” revisions). Second, the price adjustment process is faster and more complete for “celebrity” analysts (Institutional Investor All-Stars) than for more obscure yet highly accurate analysts (Wall Street Journal Earnings-Estimators). Third, controlling for other factors, the price adjustment process is faster and more complete for firms with greater analyst coverage. Finally, a substantial portion of the delayed price adjustment occurs around subsequent earnings-announcement and forecast-revision dates. Collectively, these findings show that more subtle aspects of an earnings revision signal can hinder the efficacy of market price discovery, particularly in firms with relatively low analyst coverage, and that subsequent earnings-related news events serve as catalysts in the price discovery process.

Using a database of more than 1.85 million retail investor transactions over 1991–1996, we show that these trades are systematically correlated—that is, individuals buy (or sell) stocks in concert. Moreover, consistent with noise trader models, we find that systematic retail trading explains return comovements for stocks with high retail concentration (i.e., small‐cap, value, lower institutional ownership, and lower‐priced stocks), especially if these stocks are also costly to arbitrage. Macroeconomic news and analyst earnings forecast revisions do not explain these results. Collectively, our findings support a role for investor sentiment in the formation of returns.

Trading halts increase, rather than reduce, both volume and volatility. Volume (volatility) in the first full trading day after a trading halt is 230 percent (50 to 115 percent) higher than following “pseudohalts”: nonhalt control periods matched on time of day, duration, and absolute net‐of‐market returns. These results are robust over different halt types and news categories. Higher posthalt volume is observed into the third day while higher posthalt volatility decays within hours. The extent of media coverage is a partial determinant of volume and volatility following both halts and pseudohalts, but a separate halt effect remains after controlling for the media effect.

Trading halts increase, rather than reduce, both volume and volatility. Volume (volatility) in the first full trading day after a trading halt is 230 percent (50 to 115 percent) higher than following “pseudohalts”: nonhalt control periods matched on time of day, duration, and absolute net-of-market returns. These results are robust over different halt types and news categories. Higher posthalt volume is observed into the third day while higher posthalt volatility decays within hours. The extent of media coverage is a partial determinant of volume and volatility following both halts and pseudohalts, but a separate halt effect remains after controlling for the media effect.

We develop and evaluate an accounting-based Loan Portfolio Risk (LPR) variable that captures time-varying contagion effects in default risk for a portfolio of bank loans. Our results show that an Equity-to-LPR ratio (ELPR) is additive in predicting bank failure up to five years in advance, after controlling for all the capital adequacy, asset quality, management experience, earnings, liquidity, and sensitivity to market risks (CAMELS) variables as well as other fundamental-based bank risk measures from prior studies. Further, we find that publicly listed banks with higher ELPR have lower market-implied costs of capital, especially under market stress conditions. We conclude that ELPR captures key aspects of bank risk that are missing in current Basel Committee risk-weighted-asset calculations.

Applying a “co-search” algorithm to Internet traffic at the SEC׳s EDGAR website, we develop a novel method for identifying economically related peer firms and for measuring their relative importance. Our results show that firms appearing in chronologically adjacent searches by the same individual (Search-Based Peers or SBPs) are fundamentally similar on multiple dimensions. In direct tests, SBPs dominate GICS6 industry peers in explaining cross-sectional variations in base firms׳ out-of-sample: (a) stock returns, (b) valuation multiples, (c) growth rates, (d) R&D expenditures, (e) leverage, and (f) profitability ratios. We show that SBPs are not constrained by standard industry classification, and are more dynamic, pliable, and concentrated. We also show that co-search intensity captures the degree of similarity between firms. Our results highlight the potential of the collective wisdom of investors — extracted from co-search patterns — in addressing long-standing benchmarking problems in finance.

Employing a classic measure of technological closeness between firms, we show that the returns of technology-linked firms have strong predictive power for focal firm returns. A long-short strategy based on this effect yields monthly alpha of 117 basis points. This effect is distinct from industry momentum and is not easily attributable to risk-based explanations. It is more pronounced for focal firms that: (a) have a more intense and specific technology focus, (b) receive lower investor attention, and (c) are more difficult to arbitrage. Our results are broadly consistent with sluggish price adjustment to more nuanced technological news.

Economic theory suggests that production complementarity is an important driver of sectoral co-movements and business cycle fluctuations. We operationalize this concept using a measure of production complementarity proximity (COMPL) between any two companies. We show firms from different industries but are closely aligned in COMPL exhibit strong co-movement in their operating, investing, and financing activities, as well as quarterly earnings revisions and monthly returns. We further document a lead-lag effect in their returns, such that a long-short strategy based on recent COMPL peer returns yields a monthly 6-factor alpha of 122 basis points. This inter-industry momentum spillover effect is not explained by other network-based mechanisms, such as shared analyst coverage. We conclude information transmission takes place along complementarity networks, but stock prices do not update instantaneously.

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.

We re-examine the puzzling pattern of lead-lag returns among economically-linked firms. Our results show that investors consistently underreact to information from lead firms that arrives continuously, while information with the same cumulative returns arriving in discrete amounts is quickly absorbed into price. This finding holds across many different types of economic linkages, including shared-analyst-coverage. We conclude that the ǣfrog in the panǥ (FIP) momentum effect is pervasive in co-momentum settings, suggesting that information discreteness (ID) serves as a cognitive trigger that reduces investor inattention and improves inter-firm news transmission.