The Journal of Finance

The Secondary Market for Hedge Funds and the Closed Hedge Fund Premium

Published: 03/27/2012   |   DOI: 10.1111/j.1540-6261.2012.01723.x

TARUN RAMADORAI

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.

Currency Returns, Intrinsic Value, and Institutional‐Investor Flows

Published: 05/03/2005   |   DOI: 10.1111/j.1540-6261.2005.00769.x

KENNETH A. FROOT, TARUN RAMADORAI

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.

On the High‐Frequency Dynamics of Hedge Fund Risk Exposures

Published: 11/26/2012   |   DOI: 10.1111/jofi.12008

ANDREW J. PATTON, TARUN RAMADORAI

We propose a new method to model hedge fund risk exposures using relatively high‐frequency conditioning variables. In a large sample of funds, we find substantial evidence that hedge fund risk exposures vary across and within months, and that capturing within‐month variation is more important for hedge funds than for mutual funds. We consider different within‐month functional forms, and uncover patterns such as day‐of‐the‐month variation in risk exposures. We also find that changes in portfolio allocations, rather than in the risk exposures of the underlying assets, are the main drivers of hedge funds' risk exposure variation.

Asset Fire Sales and Purchases and the International Transmission of Funding Shocks

Published: 11/19/2012   |   DOI: 10.1111/j.1540-6261.2012.01780.x

CHOTIBHAK JOTIKASTHIRA, CHRISTIAN LUNDBLAD, TARUN RAMADORAI

We identify a new channel for the transmission of shocks across international markets. Investor flows to funds domiciled in developed markets force significant changes in these funds' emerging market portfolio allocations. These forced trades or “fire sales” affect emerging market equity prices, correlations, and betas, and are related to but distinct from effects arising purely from fund holdings or from overlapping ownership of emerging markets in fund portfolios. A simple model and calibration exercise highlight the importance to these findings of “push” effects from funds' domicile countries and “co‐ownership spillover” between markets with overlapping fund ownership.

Change You Can Believe In? Hedge Fund Data Revisions

Published: 01/27/2015   |   DOI: 10.1111/jofi.12240

ANDREW J. PATTON, TARUN RAMADORAI, MICHAEL STREATFIELD

We analyze the reliability of voluntary disclosures of financial information, focusing on widely‐employed publicly‐available hedge fund databases. Tracking changes to statements of historical performance recorded between 2007 and 2011, we find that historical returns are routinely revised. These revisions are not merely random or corrections of earlier mistakes; they are partly forecastable by fund characteristics. Funds that revise their performance histories significantly and predictably underperform those that have never revised, suggesting that unreliable disclosures constitute a valuable source of information for investors. These results speak to current debates about mandatory disclosures by financial institutions to market regulators.

Change You Can Believe In? Hedge Fund Data Revisions: Erratum

Published: 07/23/2015   |   DOI: 10.1111/jofi.12306

ANDREW J. PATTON, TARUN RAMADORAI, MICHAEL STREATFIELD

Hedge Funds: Performance, Risk, and Capital Formation

Published: 07/19/2008   |   DOI: 10.1111/j.1540-6261.2008.01374.x

WILLIAM FUNG, DAVID A. HSIEH, NARAYAN Y. NAIK, TARUN RAMADORAI

We use a comprehensive data set of funds‐of‐funds to investigate performance, risk, and capital formation in the hedge fund industry from 1995 to 2004. While the average fund‐of‐funds delivers alpha only in the period between October 1998 and March 2000, a subset of funds‐of‐funds consistently delivers alpha. The alpha‐producing funds are not as likely to liquidate as those that do not deliver alpha, and experience far greater and steadier capital inflows than their less fortunate counterparts. These capital inflows attenuate the ability of the alpha producers to continue to deliver alpha in the future.

Predictably Unequal? The Effects of Machine Learning on Credit Markets

Published: 10/28/2021   |   DOI: 10.1111/jofi.13090

ANDREAS FUSTER, PAUL GOLDSMITH‐PINKHAM, TARUN RAMADORAI, ANSGAR WALTHER

Innovations in statistical technology in functions including credit‐screening have raised concerns about distributional impacts across categories such as race. Theoretically, distributional effects of better statistical technology can come from greater flexibility to uncover structural relationships or from triangulation of otherwise excluded characteristics. Using data on U.S. mortgages, we predict default using traditional and machine learning models. We find that Black and Hispanic borrowers are disproportionately less likely to gain from the introduction of machine learning. In a simple equilibrium credit market model, machine learning increases disparity in rates between and within groups, with these changes attributable primarily to greater flexibility.

Who Owns What? A Factor Model for Direct Stockholding

Published: 03/07/2023   |   DOI: 10.1111/jofi.13220

VIMAL BALASUBRAMANIAM, JOHN Y. CAMPBELL, TARUN RAMADORAI, BENJAMIN RANISH

We build a cross‐sectional factor model for investors' direct stockholdings and estimate it using data from almost 10 million retail accounts in the Indian stock market. Our model identifies strong investor clienteles for stock characteristics, most notably firm age and share price, and for particular clusters of stock characteristics. These clienteles are intuitively associated with investor attributes such as account age, size, and diversification. Coheld stocks tend to have higher return covariance, inconsistent with simple models of diversification but suggestive that clientele demands influence stock returns.