Do Short-Sellers Profit from Mutual Funds? Evidence from Daily Trades

Salman Arif

Indiana University – Kelley School of Business – Department of Accounting

Azi Ben-Rephael

Indiana University – Kelley School of Business – Department of Finance

Charles M.C. Lee

Stanford University – Graduate School of Business

Rock Center for Corporate Governance at Stanford University Working Paper No. 195


Using high resolution data, we show that short-sellers (SSs) systematically profit from mutual fund (MF) flows. At the daily level, SSs trade strongly in the opposite direction to MFs. This negative relation is associated with the expected component of MF flows (based on prior days’ trading), as well as the unexpected component (based on same-day flows). The ability of SS trades to predict stock returns is up to 3 times greater when MF flows are in the opposite direction. The resulting wealth transfer from MFs to SSs is most pronounced for high-MF-held, low-liquidity firms, and is much larger during periods of high retail sentiment.

Do Short-Sellers Profit from Mutual Funds? – Introduction

Traditional rational expectation models with costly information feature agents who expend resources to become informed. These informed agents earn a return on their information acquisition efforts by trading against the uninformed, and as they do so, the information they possess is incorporated into prices.1 Although such models offer broad insights into the informational role of prices, they are of less help in understanding the nature of the information possessed by the informed. A particular limitation is that informed traders are assumed to be identical – that is, they possess the same information and face the same cost constraints.

In real-world markets, price discovery is a much more complex process. A more realistic characterization of the world of professional investment management, for example, is one in which multiple groups of “heterogeneously informed” traders facing different cost constraints are seeking to earn a competitive return on their particular parcels of knowledge.2 In such markets, efforts to understand price discovery calls for insights into the roles played by different groups, and the motives that each has for trading. A challenge to empiricists is to identify settings in which the actions of distinct investor groups can be identified and studied in isolation.

In this study, we use trade data at the daily level to examine the interaction between two important, yet largely orthogonal, investor groups – mutual funds (MFs) and short-sellers (SSs). Both groups are prominent in the U.S. equity market. MFs are professionally managed investment vehicles that charge an active management fee and cater primarily to a retail clientele. With close to $6 trillion in assets under management as of year-end 2012, they constitute a major source of active trading in the market.3 Short-sellers are another important group of active investors, which prior studies consistently associate with superior information processing capabilities.

Although MF managers tend to be regarded as sophisticated investors, they are also subject to a variety of regulatory and agency-related constraints that may impede fund performance. Prior evidence suggests that at least some subset of MF managers possess persistent stockselection skill, although as a group MFs seem to curiously underperform.5 Indeed, a number of recent studies (summarized in Section 2) show that MF trading can lead to predictable return patterns at the individual stock level. However, prior studies that examine institutional investor behavior typically rely on quarterly holdings culled from 13-F filings, which offer limited insights on daily trading activities (e.g., Kacperczyk, Sialm, and Zheng (2008) and Puckett and Yan (2011)). In addition, because these filings only reflect the long-side of investors’ positions, no distinction can be made between the actions of MFs and SSs.

Using highly granular data, we provide the first close-up evidence on how these two groups of investors interact with each other at the daily level, and the implications of their interaction for future stock returns.



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