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Using detailed data about company visits by Chinese mutual funds, we provide direct evidence of mutual fund information acquisition activities and the consequent informational advantages mutual funds establish in local firms. Mutual funds are more likely to visit local and nearby firms, though ease of travel between fund and firm locations alleviates geographic distance constraints. Company visits by mutual funds are related to both fund trading activities and fund trading performance. The relation between company visits and fund trading performance is particularly strong in newly initiated stock positions. Consistent with this result, we find evidence of intensive information acquisition by mutual funds in stocks not in their current portfolios. Our results suggest that mutual fund local preference in portfolio choice is at least partially driven by geographic constraints of information acquisition and the ensuing information asymmetry.

We develop and test a hypothesis that hedge-fund managers who respond to and capitalize on the changing political environment around Presidential elections are better skilled. We find that hedge funds, on average, trade in anticipation of the presidential election outcome, and adjust the political sensitivity of their portfolio accordingly. Managers who adjust their portfolio political sensitivity most successfully generate significantly higher alpha than those that are least successful in their adjustments. The significant superior performance by these funds persists for about a year. We also find that these funds are more likely to survive over the following two years. Our evidence suggests that hedge funds’ anticipation and response to Presidential elections indicate managerial skill and can successfully predict future fund performance.

We apply an ex ante measure of divergence of investor opinions – industry-level dispersion in analyst earnings forecast – to test the Miller (1977) prediction about IPO overvaluation. Our measure of divergence of opinion is the dispersion in analyst long-term earnings growth forecasts divided by the absolute value of the mean long-term growth forecast averaged at the industry level in the month prior to the IPO. We focus on the effect of divergence of opinion on long-run IPO performance, and test two hypotheses. First, greater divergence of opinion should predict lower long-run performance. Second, since the Miller effect depends on the demand of the offer relative to the supply, we further predict that the effect of belief dispersion should be stronger for offers with high investor demand, as measured by issue overallotment at the time of the offer. Using a sample of about 7,000 IPOs from 1982 to 2005, we find that IPOs in industries with high divergence of opinion have lower long-run returns in the following 3-5 years than IPOs in industries with low divergence of opinion. The effect of belief dispersion on long-run performance primarily exists among IPOs with high demand, as measured by the overallotment at the time of the offer, and this effect remains strong when returns are value-weighted. These findings support Miller’s hypothesis that in markets with restricted short-selling, valuations reflect the most optimistic investor’s appraisal in the short-run, and the initial overvaluation is corrected in the long-run. There is evidence that IPOs with high divergence of opinion also have lower first-day returns, but this association is weakened by the amount of overallotment, suggesting that the correction of initial overvaluation starts shortly after the offer, but the initial correction is dampened by investor demand at the time of the offer.

We find that, due to arbitrage around index changes, investors in S&P 500-linked funds lose between 0.03% and 0.12% annually, while investors in Russell 2000-linked funds lose between 1.30% and 1.84%. In dollar terms, the losses range from $3.75 billion to $6 billion a year for the two indexes together. These losses are an unexpected consequence of index fund investors evaluating index fund managers based on tracking error in an effort to control agency costs. Minimization of tracking error coupled with the predictability and/or pre-announcement of index changes creates the opportunity for a wealth transfer from index fund investors to arbitrageurs, particularly for Russell 2000-linked funds where the index changes are predictable. We propose solutions aimed at resolving the problem that can be implemented by indexing companies, index fund managers, or fund investors.

We present evidence on the December effect. When investors do not sell winner stocks in December but postpone their sale to January so that capital gains will not be realized in the current fiscal year, the "winners" appreciate in December. The December effect is relatively easy to arbitrage. We also present evidence regarding the persistence of the January effect and note that the January effect continues because it is difficult to exploit profitably.

The multitude of explanations for the January effect leaves the reader confused about its primary cause(s): is it tax-loss selling, window-dressing, information, bid-ask bounce, or a combination of these causes? The confusion arises, in part, because evidence has been presented in support of a particular hypothesis though the same evidence may be consistent with more than one hypothesis. Furthermore, prior work has not adequately controlled for the bid-ask bounce. In this paper, we try to disentangle different explanations of the January effect and identify its primary cause. We find that tax-related selling is the most important cause, overshadowing other possible explanations.

Several studies have found that stock price changes resulting from firms added to the S&P 500 index can be best exp lained by a downward sloping demand curve. In this paper, we study price effects around both additions and deletions and find that the price effect of index changes is consistent with Merton's (1987) investor-awareness and market segmentation hypothesis. We find that the reduction in shadow cost of incomplete diversification that follows additions is correlated with abnormal returns accruing to the added stocks. We also find that the asymmetric price effects of additions and deletions that have not been explained by empirical studies thus far are consistent with market segmentation.

In this paper, we argue that short sellers affect prices in a significant and systematic manner. In particular, we contend that speculative short sales contribute to the weekend effect: the inability to trade over the weekend is likely to cause these short sellers to close many of their speculative positions on Fridays and reestablish new short positions on Mondays causing stock prices to rise on Fridays and fall on Mondays. We find evidence in support of this hypothesis: the weekend effect is significantly larger for high short-interest stocks than for low short-interest stocks. Further, we find that the likely substitution of speculative short sales by put options results in the weekend effect to diminish for stocks with actively traded options, but to continue for other stocks. An analysis of several special types of stocks, viz. IPOs, zero short-interest stocks, and highly volatile stocks, reveals support for the hypothesis.

The January anomaly has attracted much academic interest and has been explained in different ways. However, the multitude of explanations has created confusion about the validity and relative importance of those explanations. In some cases, the hypotheses are examined individually though the evidence may be consistent with more than one hypothesis. Furthermore, prior work has not adequately controlled for the bid-ask bounce. Therefore, the results leave the reader somewhat confused regarding the January effect: is it caused by tax-loss selling, window-dressing, information, bid-ask bounce, or a combination of these causes? In this paper, we try to disentangle different explanations of the January effect and identify its primary cause. We find that past losers are more likely to be sold in December than in January to realize the tax advantage of capital losses. Past winners are more likely to be sold in January than in December to postpone payment of taxes. The selling is accompanied by changes in volume around turn of the year consistent with the tax-related selling hypotheses. The results are not materially affected when we use the midpoint of quotes instead of actual prices: the bid-ask bounce accounts for about 20-25% of the observed returns. To verify the window-dressing hypothesis, we examine stock returns around June-July, the period of semi-annual reporting by institutional managers that is not contaminated by tax-related trading. We do not find an economically meaningful difference between the 5-day return at the end of June and the 5-day return at the beginning of July, which is not consistent with window dressing. If the January effect occurs due to release of new information in January that affects the information-poor firms more than the information-rich firms then the returns in January should be related to availability of information (for example, with the number of analysts as a proxy). We do not find a correlation consistent with the information hypothesis. There is no information-related effect in June-July. The evidence here supports the tax-related selling hypotheses as the drivers of January effect.