|
Ronnie Sadka, Boston College
Liquidity Risk and the Cross-Section of Hedge-Fund Returns
Sadka demonstrates that liquidity risk, as measured by the covariation of fund returns with unexpected changes in aggregate liquidity, is an important determinant in the cross-section of hedge-fund returns. Using the aggregate liquidity risk factor in Sadka (2006), this paper shows that funds that significantly load on liquidity risk subsequently outperform low-loading funds by about 8 percent annually over the period 1994-2007. This outperformance is independent of the illiquidity of a fund as measured by lockup and redemption notice periods. These findings are also robust to risk controls, portfolio rebalancing frequency, and potential return smoothing. The results highlight the importance of understanding systematic liquidity variations in the evaluation of hedge-fund performance.
Shane Corwin, University of Notre Dame, Paul Schultz, University of Notre Dame
A Simple Way to Estimate Bid-Ask Spreads from Daily High and Low Prices
Corwin and Schultz develop a new way to estimate bid-ask spreads from daily high and low prices. Daily high (low) prices are almost always buy (sell) orders. Hence, the ratio of high-to-low prices for a day reflects both the stocks variance and its bid-ask spread. When high-low price ratios are estimated over two days, the variance is twice as large but the bid-ask spread component is unchanged. This allows the authors to estimate bid-ask spreads by comparing high-low price ratios over one-day and two-day intervals. They compare the high-low estimator to alternative spread estimators and to spreads estimated from intraday TAQ data. They find that the estimator is accurate and easy to use, providing a useful measure of transaction costs in a wide variety of applications.
Alain Chaboud, Federal Reserve Board,
Benjamin Chiquoine, The Investment Fund for Foundations,
Erik Hjalmarsson, Federal Reserve Board,
and Clara Vega, Federal Reserve Board
Rise of the Machines: Algorithmic Trading in the Foreign Exchange Market
Chaboud, Chiquoine, Hjalmarsson, and Vega study the impact that algorithmic trading -- computers directly interfacing with trading platforms -- has had on price discovery and volatility in the foreign exchange market. They use high frequency data representing a majority of global inter-dealer trading in three major currency pairs from 2006 to 2007. Their dataset contains precise observations of the size and the direction of the computer-generated and human- generated trades each minute. As such, it allows them to analyze the possible links between algorithmic trading and market volatility and liquidity, to identify whose trades with a more permanent impact on prices, and to study how correlated algorithmic trades are. Their study provides several important insights. First, they observe that algorithmic trades tend to be correlated, suggesting that the algorithmic strategies used in the market are not as diverse as those used by non-algorithmic traders. Second, they find no evident causal relationship between algorithmic trading and increased exchange rate volatility. If anything, the presence of more algorithmic trading is associated with lower volatility. Third, they show that even though some algorithmic traders appear to restrict their activity in the minute following a macroeconomic data release, algorithmic traders increase their provision of liquidity relatively more than non-algorithmic traders over the their following the release. Fourth, they find that non-algorithmic order flow accounts for most of the (long-run) variance in exchange rate returns, that is non-algorithmic traders are better informed than algorithmic traders. Fifth, they find evidence that supports the literature that proposes to depart from the prevalent assumption that liquidity providers in limit order books are passive.
|
Lawrence Harris, University of Southern California,
Ethan Namvar, UC, Irvine, and Blake Phillips, University of Alberta
Price Inflation and Wealth Transfer during the 2008 SEC Short-Sale Ban
Using a factor-analytic model that extracts common valuation information from the prices of stocks that were not banned, Harris and his co-authors estimate that the ban on short-selling financial stocks imposed by the SEC in September 2008 led to substantial price inflation in the banned stocks. The inflation reversed somewhat following the ban, but the data are too noisy to conclusively link the reversal to the ban. Other factors such as the pending TARP legislation may also have affected prices, though these results suggest that it was not a significant factor. If prices were inflated, buyers paid more than they otherwise would have paid for the banned stocks during the period of the ban. The researchers provide an estimate of $4.9 billion for the resulting transfer from buyers to sellers. Such transfers should interest policymakers concerned about maintaining fair markets.
Zhi Da, University of Notre Dame,
Joseph Engelberg, University of North Carolina, and
Pengjie Gao, University of Notre Dame
In Search of Attention
Turnover, extreme returns, news, and advertising expense are indirect proxies of investor attention. Da, Engelberg, and Gao propose instead a direct measure of investor demand for attention or active attention using aggregate search frequency in Google (SVI). In a sample of Russell 3000 stocks from 2004 to 2008, they find SVI to be correlated with but different from existing proxies of investor attention. In addition, SVI captures investor attention on a timelier basis. SVI allows them to shed new light on how retail investor attention affects the returns to IPO stocks and price momentum strategies. Using retail order execution in SEC Rule 11Ac1-5 reports, they establish a strong and direct link between SVI changes and trading by less sophisticated individual investors. Increased retail attention as measured by SVI during the IPO contributes to the large first-day return and long-run underperformance of IPO stocks. They also document stronger price momentum among stocks with higher level of SVI, consistent with the explanation of momentum proposed by Daniel, Hirsheifer and Subrahmanyam (1998).
Stewart Mayhew, Securities and Exchange Commission,
Timothy McCormick, Securities and Exchange Commission,
and Chester Spatt, Carnegie Mellon University and NBER
The Information Content of Market-on-Close Imbalances, the Specialist and NYSE Equity Prices
Mayhew and his co-authors examine the relationship between announcements of a) market-on-close orders, b) price dynamics, and c) specialist trading on the New York Stock Exchange. They find that the closing-order imbalance affects prices even prior to the announcement of the imbalance. This is consistent with investors expressing their demands through both market-on-close orders and direct purchasesso that some of the price impact could occur before the specialists announcement of the imbalance. However, to a degree the specialist himself is trading ahead of the announcement, which also helps explain why price movement occurs before dissemination of the imbalances. Consequently, the remaining traders (other than the specialist) are actually trading against the direction of their closing imbalances and the specialist is not helping to smooth investor demands. Because of the structure of the mechanism and the volatility of prices near the close (which is especially elevated at the deadline for market on close orders at 3:40 p.m.), this is an interesting context for studying how information gets reflected in price.
|