Currency Returns, Institutional Investor Flows, and Exchange Rate Fundamentals

We explore the interaction between exchange rates, institutional investor currency flows and exchange-rate fundamentals. We find that these flows are highly correlated with contemporaneous and lagged exchange rate changes, and that they carry information for future excess currency returns. This information, however, is not strongly linked to future fundamentals. Flows are important in understanding transitory elements of excess returns, which include short-run underreaction and long-run overreaction. However, flows have a zero or negative correlation with permanent components of excess returns. We find that measured fundamentals - not flows - seem important in understanding permanent elements of excess returns. We conclude that investor flows are important for understanding deviations of exchange rates from fundamentals, but not for understanding the long-run currency values.

Currency Returns, Institutional Investor Flows, and Exchange Rate Fundamentals

We explore the interaction between exchange rates, institutional investor currency flows and exchange-rate fundamentals. We find that these flows are highly correlated with contemporaneous and lagged exchange rate changes, and that they carry information for future excess currency returns. This information, however, is not strongly linked to future fundamentals. Flows are important in understanding transitory elements of excess returns, which include short-run underreaction and long-run overreaction. However, flows have a zero or negative correlation with permanent components of excess returns. We find that measured fundamentals - not flows - seem important in understanding permanent elements of excess returns. We conclude that investor flows are important for understanding deviations of exchange rates from fundamentals, but not for understanding the long-run currency values.

Caught On Tape: Predicting Institutional Ownership With Order Flow

Many questions about institutional trading behavior can only be answered if one can track institutional equity ownership continuously, yet institutional ownership data are only available on quarterly reporting dates. We infer institutional trading behavior from the “tape”, the Transactions and Quotes database of the New York Stock Exchange, by regressing quarterly changes in reported institutional ownership on quarterly buy and sell volume in different trade size categories. We find that institutions in aggregate demand liquidity, in that total buy (sell) volume predicts increasing (decreasing) institutional ownership. Institutions also tend to trade in large or very small sizes, in that buy (sell) volume at these sizes predicts increasing (decreasing) institutional ownership, while the pattern reverses at intermediate trade sizes that are favored by individuals. Our regression method predicts institutional ownership significantly better than the simple cutoff rules used in previous research.institutions, individuals, trading behavior, execution

Long-Run Discounting: Evidence from the UK Leasehold Valuation Tribunal

The United Kingdom's Leasehold Valuation Tribunal hears extension and enfranchisement cases between landlords (freeholders) and tenants (leaseholders). In these cases, the two parties argue about the terms of housing lease extensions of up to 90 years in length, and about enfranchisements to convert leasehold contracts of specific durations to perpetual ownership freeholds. The widely-followed decisions of the leasehold valuation tribunal provide a unique insight into household-level variation in expectations of long-run discount rates and cash-flows, and set bounds on the prices that market participants may be willing to pay for housing over long periods of time. We use the record of decisions since 1995 to extract information about long-run cash-flow and discount rate expectations in this unique setting, which requires no estimation, but has real stakes for the participants in these negotiations. We find evidence that the discount rate associated with these decisions causes values of properties discounted for long periods (above 90 years) to be close to zero

Caught on Tape: Predicting Institutional Ownership With Order Flow

Many questions about institutional trading can only be answered if one can track institutional equity ownership continuously. However, these data are only available on quarterly reporting dates. We infer institutional trading behavior from the "tape," the Transactions and Quotes database of the New York Stock Exchange, by regress- ing quarterly changes in reported institutional ownership on quarterly buy and sell volume in different trade size categories. Our regression method predicts institutional ownership signifcantly better than the simple cutoff rules used in previous research. We also find that total buy (sell) volume predicts increasing (decreasing) institutional ownership, consistent with institutions demanding liquidity in aggregate. Furthermore, institutions tend to trade in large or very small sizes: buy (sell) volume at these sizes predicts increasing (decreasing) institutional ownership, while the pattern reverses at intermediate trade sizes that appear favored by individuals. We then explore changes in institutional trading strategies. Institutions appear to prefer medium size trades on high volume days and large size trades on high volatility days.

Caught On Tape: Institutional Order Flow and Stock Returns

Many questions about institutional trading can only be answered if one can track high-frequency changes in institutional ownership. In the US, 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 both a naive approach and a sophisticated method that best matches quarterly 13-F data. Increases in our measures of institutional flows negatively predict returns, particularly when institutions are selling. We interpret this as evidence that 13-F institutions compensate more patient investors for the service of providing liquidity. We also find that both very large and very small trades signal institutional activity, while medium size trades signal activity by the rest of the market.

Does Hedging Affect Commodity Prices? The Role of Producer Default Risk

Do hedging and speculative activity in commodity futures affect spot prices? Yes, when commodity producers have hedging needs. We build a model in which producers are risk-averse to future cash flow variability and hedge using futures contracts. Increases in speculative demand for futures reduces the cost of hedging, allowing producers to hedge more and hold larger inventories. This pushes spot prices higher. Reductions in speculative demand for futures have the opposite effects. The data provide support for the hedging channel we identify - oil and gas producers - hedging demands (proxied by their default risk), forecast spot prices, futures prices and producers' inventories

The Information Content of International Portfolio Flows

We examine the forecasting power of international portfolio flows for local equity markets and attempt to attribute it to either better information about fundamentals on the part of international investors, or to price pressure. Price pressure is a potential explanation because flows have positive contemporaneous price impacts and are strongly positively autocorrelated. We find that cross-border flows forecast both individual country equity market prices and associated US closed-end country fund prices, even after controlling for closed-end fund purchases. Cross-border flows have no discernable impact on the difference, the closed-end fund discount. This fact is consistent with the information story, which says that cross-border inflows predict no change in the discount, but forecast positive changes in both net asset values and closed-end fund prices. This fact also contradicts the price pressure story, which predicts that cross-border inflows increase local country equity prices, thereby increasing the closed-end fund discount. We also use our approach to test for the presence of trend following in cross-border flows based on relative, as well as absolute returns. Like other studies, we find evidence of trend following based on absolute returns. Interestingly, however, we find also that flows are trend reversing based on relative returns. Flows therefore seem to be stabilizing with respect to notions of relative, but not absolute, value.

Caught On Tape: Institutional Order Flow and Stock Returns

Many questions about institutional trading can only be answered if one can track high-frequency changes in institutional ownership. In the US, 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 both a naive approach and a sophisticated method that best matches quarterly 13-F data. Increases in our measures of institu- tional flows negatively predict returns, particularly when institutions are selling. We interpret this as evidence that 13-F institutions compensate more patient investors for the service of providing liquidity. We also find that both very large and very small trades signal institutional activity, while medium size trades signal activity by the rest of the market.

Caught on Tape: Institutional Trading, Stock Returns, and Earnings Announcements

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 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.Economic