[Using a new technique, and weekly data for 25 countries from 1994 to 1998, we analyze the relationship between institutional cross-border portfolio flows, and domestic and foreign equity returns. In emerging markets, institutional flows forecast statistically indistinguishable movements in country closed-end fund NAV returns and price returns. In contrast, closed-end fund flows forecast price returns, but not NAV returns. Furthermore, institutional flows display trend-following (trend-reversing) behavior in response to symmetric (asymmetric) movements in NAV and price returns. The results suggest that institutional cross-border flows are linked to fundamentals, while closed-end fund flows are a source of price pressure in the short run.]

To identify capacity constraints in hedge funds and simultaneously gauge how well-informed hedge fund investors are, we need measures of investor demand that do not affect deployed hedge fund assets. Using new data on investor interest from a secondary market for hedge funds, this paper verifies the existence of capacity constraints in hedge funds. There is more mixed evidence on the information available to hedge fund investors. Buy and sell indications arrive following fund outperformance. While buy indications have little incremental power to predict hedge fund performance over and above well-known forecasting variables, sell indications do somewhat better.

Rational theories of the closed‐end fund premium puzzle highlight fund share and asset illiquidity, managerial ability, and fees as important determinants of the premium. Several of these attributes are difficult to measure for mutual funds, and easier to measure for hedge funds. This paper employs new data from a secondary market for hedge funds, discovers a closed‐hedge fund premium that is highly correlated with the closed‐end mutual fund premium, and shows that the closed‐hedge fund premium is well explained by variables suggested by rational theories. Sentiment‐based explanations do not find support in the data.

Using a comprehensive dataset of privacy policies, firm characteristics, consumer tracking, and cybersecurity incidents, we document several stylized facts about the heterogeneity of firms’ data extraction practices and the influence of privacy regulations. Rather than adopting standardized boilerplate privacy policies, we find substantial within-industry differences correlated with firms’ technical sophistication; firms engaging in data extraction have lengthier policies, seeking to hedge legal risks. Firms with intermediate technical sophistication appear to follow a “collect and share” model, collecting large amounts of consumer data and sharing it with third parties for processing, thus creating cybersecurity risks. Conversely, high sophistication firms appear to implement a “receive and process” model, consistent with a two-tier data market in which data flow from intermediate to high sophistication firms.

Using a new technique, and weekly data for 25 countries from 1994 to 1998, we analyze the relationship between institutional cross-border portfolio flows, and domestic and foreign equity returns. In emerging markets, institutional flows forecast statistically indistinguishable movements in country closed-end fund NAV returns and price returns. In contrast, closed-end fund flows forecast price returns, but not NAV returns. Furthermore, institutional flows display trend-following (trend-reversing) behavior in response to symmetric (asymmetric) movements in NAV and price returns. The results suggest that institutional cross-border flows are linked to fundamentals, while closed-end fund flows are a source of price pressure in the short run.

We study a unique field experiment in India in which 1.5 million stock investors face lotteries for the random allocation of shares. We find that the winners of these randomly assigned initial public offering (IPO) lottery shares are significantly more likely to hold them than lottery losers 1, 6, and even 24 months after the random allocation. This finding strongly evokes laboratory findings of an “endowment effect” for risky gambles, and persists in samples of highly active investors, suggesting along with additional evidence that this behaviour is not driven by inertia alone. The effect decreases as experience in the IPO market increases, but remains even for very experienced investors. Leading theories of the endowment effect based on reference-dependent preferences are unable to fully explain these and other findings in the data.

We study a natural experiment in which 1.5 million investors participate in allocation lotteries for Indian IPO stocks. Investors who win the lottery and obtain IPO stocks that rise in value increase portfolio trading volume in non-IPO stocks relative to lottery losers; the effects are negative for lottery winners obtaining IPO stocks that fall in value. A model in which agents learn from random experience about their ability to operate in the market environment best explains the results. Investors who have received multiple past IPO allocations show smaller responses, suggesting that learning/selection moderates these responses to noise shocks.

We decompose currency returns into (permanent) intrinsic‐value shocks and (transitory) expected‐return shocks. We explore interactions between these shocks, currency returns, and institutional‐investor currency flows. Intrinsic‐value shocks are: dwarfed by expected‐return shocks (yet currency returns overreact to them); unrelated to flows (although expected‐return shocks correlate with flows); and related positively to forecasted cumulated‐interest differentials. These results suggest flows are related to short‐term currency returns, while fundamentals better explain long‐term returns and values. They also rationalize the long‐observed poor performance of exchange‐rate models: by ignoring the distinction between permanent and transitory exchange‐rate changes, prior tests obscure the connection between currencies and fundamentals.

Many questions about institutional trading can only be answered if one tracks high-frequency changes in institutional ownership. In the United States, however, institutions are only required to report their ownership quarterly in 13-F filings. We infer daily institutional trading behavior from the “tape”, the Transactions and Quotes database of the New York Stock Exchange, using a sophisticated method that best predicts quarterly 13-F data from trades of different sizes. We find that daily institutional trades are highly persistent and respond positively to recent daily returns but negatively to longer-term past daily returns. Institutional trades, particularly sells, appear to generate short-term losses—possibly reflecting institutional demand for liquidity—but longer-term profits. One source of these profits is that institutions anticipate both earnings surprises and post-earnings announcement drift. These results are different from those obtained using a standard size cutoff rule for institutional trades.

Identifying the effects of “flights to safety” on asset prices using pure time-series methods is difficult because crises are infrequent. We develop a new cross-sectional identification approach, motivated by the insight that investors may differ in their “preferred habitats” within a broad asset class. We apply the method to the question of whether foreign capital is responsible for residential real estate price movements in global cities such as London and New York, especially during crises. Using large data sets of housing transactions, we find that foreign risk strongly affects London house prices. The effects are long-lasting, and are associated with both safe-haven effects and immigration.