We estimate continuous-time event-history models of the acquisition of conglomerate vs. non-conglomerate and predatory vs. friendly acquisitions among the 1962 Fortune 500 between January, 1963, and December, 1968. Our analysis of predatory acquisitions reveals that there were strong disciplinary motivations for these acquisitions in the 1960s. Q ratios were, by a large margin, the most important determinant of predatory acquisition likelihood. Surprisingly, however, corporate boards appear to have provided little alternative to predatory acquisition as a monitoring mechanism during this period. Friendly acquisitions, on the other hand, were concentrated among firms with low price-earnings ratios and high return on equity, suggestive of the earnings manipulation story often associated with conglomerate acquisitions. Our analysis of conglomerate acquisitions reveals that there were strong disciplinary motivations for conglomerate acquisitions during this period. Conglomerate targets had low Q ratios and were as likely as non-conglomerate targets to be acquired in a predatory fashion. We find no evidence that conglomerate acquisitions were motivated by a desire to improve earnings-per-share numbers, as some have maintained. In addition, regardless of type or tenor, we find managerial ownership, firm size, and industrial organization motivations for acquisition are consistently important determinants of acquisition likelihood.

From January 1996 through June 2003, the average daily abnormal return to independent research firm buy recommendations exceeds that of investment bank buy recommendations by 3.1 basis points (almost 8 percentage points annualized). Investment bank buy recommendation underperformance is more pronounced following the NASDAQ market peak (March 10, 2000) and strikingly so for buy recommendations on firms that recently conducted equity offerings. In contrast, investment bank hold and sell recommendations outperform those of independent research firms by 1.8 basis points daily (412 percentage points annualized). These results suggest reluctance by investment banks to downgrade stocks whose prospects dimmed during the bear market of the early 2000s, as claimed in the SEC's Global Research Analyst Settlement.

When assessing a fund manager's skill, sophisticated investors will consider all factors (priced and unpriced) that explain cross-sectional variation in fund performance. We investigate which factors investors attend to by analyzing mutual fund flows as a function of recent returns decomposed into alpha and factor-related returns. Surprisingly, investors attend most to market risk (beta) when evaluating funds and treat returns attributable to size, value, momentum, and industry factors as alpha. Using proxies for investor sophistication (wealth, distribution channels, and periods of high investor sentiment), we find that more sophisticated investors use more sophisticated benchmarks when evaluating fund performance.

We study the trading of individual investors using transaction data and identifying buyer- or seller-initiated trades. We document four results: (1) Small trade order imbalance correlates well with order imbalance based on trades from retail brokers. (2) Individual investors herd. (3) When measured annually, small trade order imbalance forecasts future returns; stocks heavily bought underperform stocks heavily sold by 4.4 percentage points the following year. (4) Over a weekly horizon, small trade order imbalance reliably predicts returns, but in the opposite direction; stocks heavily bought one week earn strong returns the subsequent week, while stocks heavily sold earn poor returns.

Rational models claim “trading to learn” explains widespread excessive speculative trading and challenge behavioral explanations of excessive trading. We argue rational learning models do not explain speculative trading by studying day traders in Taiwan. Consistent with previous studies of learning, unprofitable day traders are more likely than profitable traders to quit. Consistent with models of overconfidence and biased learning (but not with rational learning), the aggregate performance of day traders is negative; 74% of day trading volume is generated by traders with a history of losses; and 97% of day traders are likely to lose money in future day trading. Received: March 4, 2019; Editorial decision: May 16, 2019 by Editor: Jeffrey Pontiff.

This paper analyzes the distribution of stock ratings at investment banks and brokerage firms and examines whether these distributions can predict the profitability of analysts’ recommendations. We document that the percentage of buys decreased steadily starting in mid-2000, likely due, at least partly, to the implementation of NASD Rule 2711, requiring the public dissemination of ratings distributions. Additionally, we find that a broker's ratings distribution can predict recommendation profitability. Upgrades to buy (downgrades to hold or sell) issued by brokers with the smallest percentage of buy recommendations significantly outperformed (underperformed) those of brokers with the greatest percentage of buys.

U.S. stocks have been shown to earn higher returns during earnings announcement months than during non-announcement months. We document that this earnings announcement premium exists across the globe. Moreover, it is not isolated to a few countries. Of the 20 countries with enough data to conduct a within-country analysis, nine exhibit a significantly positive premium. A cross-country analysis finds that the premium is strongest in countries with the greatest increase in idiosyncratic volatility around the time of their firms' earnings announcements, suggesting that uncertainty over the earnings information to be disclosed is a primary driver of the global announcement premium.