Using a large hand-collected data set from 2001 to 2006, we find that activist hedge funds in the United States propose strategic, operational, and financial remedies and attain success or partial success in two-thirds of the cases. Hedge funds seldom seek control and in most cases are nonconfrontational. The abnormal return around the announcement of activism is approximately 7%, with no reversal during the subse-quent year. Target firms experience increases in payout, operating performance, and higher CEO turnover after activism. Our analysis provides important new evidence on the mechanisms and effects of informed shareholder monitoring. ALTHOUGH HEDGE FUND ACTIVISM IS WIDELY discussed and fundamentally important, it remains poorly understood. Much of the commentary on hedge fund activism is based on supposition or anecdotal evidence. Critics and regulators question whether hedge fund activism benefits shareholders, while numerous commen-tators claim that hedge fund activists destroy value by distracting managers from long-term projects. However, there is a dearth of large-sample evidence about hedge fund activism, and existing samples are plagued by various biases. ∗We thank the Acting Editor who handled our submission. Brav is with Duke University, Jiang

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We study the economic sources of stock–bond return comovements and their time variation using a dynamic factor model. We identify the economic factors employing a semistruc-tural regime-switching model for state variables such as interest rates, inflation, the output gap, and cash flow growth. We also view risk aversion, uncertainty about inflation and output, and liquidity proxies as additional potential factors. We find that macroeconomic fundamentals contribute little to explaining stock and bond return correlations but that other factors, especially liquidity proxies, play a more important role. The macro factors are still important in fitting bond return volatility, whereas the “variance premium ” is criti-cal in explaining stock return volatility. However, the factor model primarily fails in fitting covariances. (JEL G11, G12, G14, E43, E44) Stock and bond returns in the United States display an average correlation of about 19 % during the post-1968 period. Shiller and Beltratti (1992) un-derestimate the empirical correlation using a present value with constant dis-count rates, whereas Bekaert, Engstrom, and Grenadier (2005) overestimate it in a consumption-based asset pricing model with stochastic risk aversion. Yet,

Abstract This paper explores the role of liquidity risk in the pricing of corporate bonds. We show that liquidity risk is a priced factor for the expected returns on corporate bonds. The exposures of corporate bond returns to fluctuations in treasury bond liquidity and equity market liquidity help to explain the credit spread puzzle. In terms of expected returns, the total estimated liquidity risk premium is around 0.45% for US long-maturity investment grade bonds. For speculative grade bonds, which have higher exposures to the liquidity factors, the liquidity risk premium is around 1%. We find very similar evidence for the liquidity risk exposure of corporate bonds using a sample of European corporate bond prices. * Liquidity Risk Premia in Corporate Bond Markets Abstract This paper explores the role of liquidity risk in the pricing of corporate bonds. We show that liquidity risk is a priced factor for the expected returns on corporate bonds. The exposures of corporate bond returns to fluctuations in treasury bond liquidity and equity market liquidity help to explain the credit spread puzzle. In terms of expected returns, the total estimated liquidity risk premium is around 0.45% for US long-maturity investment grade bonds. For speculative grade bonds, which have higher exposures to the liquidity factors, the liquidity risk premium is around 1%. We find very similar evidence for the liquidity risk exposure of corporate bonds using a sample of European corporate bond prices.

We propose a novel approach to optimizing portfolios with large numbers of assets. We model directly the portfolio weight in each asset as a function of the asset’s characteristics. The coefficients of this function are found by optimizing the investor’s average utility of the portfolio’s return over the sample period. Our approach is computationally simple, easily modified and extended, produces sensible portfolio weights, and offers robust performance in and out of sample. In contrast, the traditional approach of first modeling the joint distribution of returns and then solving for the corresponding optimal portfolio weights is not only difficult to implement for a large number of assets but also yields notoriously noisy and unstable results. Our approach also provides a new test of the portfolio choice implications of equilibrium asset pricing models. We present an empirical implementation for the universe of all stocks in the CRSP-Compustat dataset, exploiting the size, value, and momentum anomalies.

We aim to tackle the longstanding debate on whether stock liquidity enhances or impedes firm innovation. This topic is of interest because innovation is crucial for firm- and national-level competitiveness and stock liquidity can be altered by finan-cial market regulations. Using a difference-in-differences approach that relies on the exogenous variation in liquidity generated by regulatory changes, we find that an increase in liquidity causes a reduction in future innovation. We identify two possi-ble mechanisms through which liquidity impedes innovation: increased exposure to hostile takeovers and higher presence of institutional investors who do not actively gather information or monitor. INNOVATION PRODUCTIVITY is of interest to a large number of stakeholders includ-ing firmmanagers, employees, investors, and regulators. As Porter (1992, p. 65) states, “To compete effectively in international markets, a nation’s businesses must continuously innovate and upgrade their competitive advantages. Inno-vation and upgrading come from sustained investment in physical as well as in-

This paper proposes and tests a flow-based explanation for three important empirical findings on return predictability – the persistence of mutual fund performance, the “smart money ” effect, and stock price momentum. Since mutual fund managers generally scale up or down their existing positions in response to investment flows, and since the portfolios of funds receiving capital generally differ from the portfolios of funds losing capital, investment flows can cause significant demand shocks in individual stocks. Meanwhile, since fund flows are predictable from past fund performance and past fund flows, flow-induced price pressure is also predictable. This paper further shows that such flow-based return predictability can fully account for mutual fund performance persistence and the “smart money ” effect, and can partially explain stock price momentum. These findings have implications both for asset pricing theories and mutual fund performance evaluation. I am indebted to my advisors Nick Barberis and Will Goetzmann, and to Lauren Cohen, Andrew Metrick, Antti Petajisto, and Paul Tetlock for encouragement and valuable discussions. I also thank Zhiwu Chen, Judy

This paper studies the “confidential holdings ” of institutional investors, especially hedge funds, where the quarter-end equity holdings are disclosed with a significant delay through amendments to the Form 13F. Our evidence supports hiding private information as the dominant motive for hedge funds to seek confidentiality. Hedge funds managing large risky portfolios with less conventional investment strategies seek confidentiality more frequently. Stocks included in the confidential holdings of hedge funds are disproportionately associated with information-sensitive events such as mergers and acquisitions, and share characteristics indicating greater information asymmetry. Moreover, confidential holdings of hedge funds exhibit superior performance up to the typical confidential period of twelve months, suggesting valuable private information. Overall, our study presents new evidence on the performance of hedge funds, provides reference on the potential limitations of the standard 13F holdings databases which usually exclude the confidential holdings, and contributes to the policy debate regarding ownership

Abstract The U.S. financial services industry grew from 4.9% of GDP in 1980 to 7.9% of GDP in 2007. A sizeable portion of the growth can be explained by rising asset management fees, which in turn were driven by increases in the valuation of tradable assets, particularly equity. Another important factor was growth in fees associated with an expansion in household credit, particularly fees associated with residential mortgages. This expansion was itself fueled by the development of non-bank credit intermediation (or "shadow banking"). We offer a preliminary assessment of whether the growth of active asset management, household credit, and shadow banking -the main areas of growth in the financial sector -has been socially beneficial. * We thank Toomas Laarits for excellent research assistance. We are grateful to

Microstructure noise in security prices biases the results of empirical asset pricing specifications, particularly when security-level explanatory variables are cross-sectionally correlated with the amount of noise. We focus on tests of whether measures of illiquidity, which are likely to be correlated with the noise, are priced in the cross-section of stock returns, and document a significant upward bias in estimated return premia for an array of illiquidity measures in CRSP monthly return data. The upward bias is larger when illiquid securities are included in the sample, but persists even for NYSE/AMEX stocks after decimalization. We introduce a methodological correction to eliminate the biases that simply involves WLS rather than OLS estimation, and find evidence of smaller, but still significant,