Asset bundling and information acquisition of investors with different expertise

Abstract

This paper investigates how a profit-maximizing asset originator can coordinate the information acquisition of investors with different expertise by means of asset bundling. Bundling is beneficial to the originator when it discourages investors from analyzing idiosyncratic risks and focuses their attention on aggregate risks. But it is optimal to sell aggregate risks separately in order to exploit investors' heterogeneous expertise in learning about them and thus lower the risk premium. This analysis rationalizes the common securitization practice of bundling loans by asset class, which is at odds with existing theories based on diversification. The analysis also offers an alternative perspective on conglomerate formation (a form of asset bundling), and its relation to empirical evidence in that context is discussed.

Introduction

Securitization plays an important role in the U.S. economy. As of April 2011, outstanding securitized assets totaled $11 trillion, which was substantially more than the amount of all outstanding marketable U.S. Treasury securities (Gorton and Metrick, 2013). One salient feature of securitization is that the creation of asset-backed securities (ABS) always involves pooling loans of the same asset class; i.e., a pool consists exclusively of mortgages, auto receivables or credit card receivables. Different asset classes are not mixed, even if the originator in fact instigates loans of many different asset classes. Existing theories based on diversification² do not square well with this feature, as one would expect the benefit of diversification to be greater when different asset classes are mixed.

In this paper I demonstrate that this feature is no longer a puzzle if we recognize the important role played by the heterogeneous expertise of investors in acquiring different information about asset payoffs, which existing theories of securitization abstract from. Pooling all loans of the same asset class prohibits buyers from cherry-picking individual loans, and thus prevents them from using their expertise to exploit other buyers regarding the risks peculiar to the loans picked. This encourages all buyers to acquire information only about risks common to all the loans being sold. Since they face less uncertainty after learning about these risks, buyers demand a lower risk premium from the originator. Different asset classes are sold separately. This enables mortgage specialists to freely trade mortgages and to profit from mortgage-specific information and thus induces them to specialize in acquiring information in their area of expertise. The cost advantages in information acquisition of different buyers are thus better utilized and result in a lower total risk premium required, benefiting the originator.³

This paper develops a model that formalizes this explanation and further studies a broader theoretical issue: How can a self-interested asset originator coordinate the information acquisition of investors that have different areas of expertise? Because potential investors in any financial asset inherently have different learning expertise, this seems to be a fundamental question in understanding the workings of the financial market, in addition to rationalizing the puzzle as an application, but it has received little attention in the literature to date. As a first step, this paper focuses on asset bundling, a technique commonly used by asset originators. The application of asset bundling in financial market practice is not limited to securitization. Indeed, a conglomerate can also be viewed as a bundle of its several lines of business, in the sense that its stakeholders cannot selectively invest in and receive cash flows from any particular business that it operates. Thus, the model developed can also be used to study conglomerate formation.

My model features two key ingredients: the interaction of heterogeneous investors and their endogenous learning behavior. Asset payoffs are determined by different risks; e.g., sector-specific shocks, region-specific shocks, asset-specific shocks. There is one asset originator and a continuum of investors with different learning expertise. Each risk-averse investor allocates his limited attention to learning about these risks before trading the assets. How he does that is endogenously shaped by the bundling choice of the asset originator and by his interaction with other investors. The asset originator, who wants to maximize the revenue of the sale, bundles his original assets to channel the allocation of investors' learning capacity in the way that minimizes the total risk premium.

Three key theoretical channels novel in the literature are highlighted in the model, leading to the upside and downside of asset bundling.

The upside of asset bundling is driven by a discipline channel: asset bundling restricts speculation on risks that are supposedly diversified away, and gives investors less incentive to acquire information about them. As such, the originator successfully persuades investors to learn only about risks that cannot be reduced by diversification. Since investors have better knowledge of such risks after studying them, they demand a lower risk premium in equilibrium, benefiting the originator.

The downside of asset bundling is driven by two different economic forces. First, asset bundling mechanically restricts the asset span available to investors, thus preventing them from holding their respective favorite portfolios. Hence in equilibrium, they demand lower prices to compensate. This is a trade-restriction channel. Second, asset bundling induces each investor to specialize less in acquiring information about the risk that he has expertise in. Because the expertise of investors is less utilized, there are more risks priced in equilibrium. This is a specialization-destruction channel.

These theoretical channels work not only in the context of securitization, but also in the context of conglomerate formation. By relabeling the asset originator as an entrepreneur who owns several lines of business and decides how to set the firm boundaries, my model can also be viewed as one of conglomerate formation. It offers a new investor-side (instead of firm-side) perspective of conglomerate formation that can generate both a diversification premium (by the discipline channel) and a discount (by the trade-restriction channel and the specialization-destruction channel), and yields empirical predictions consistent with existing evidence in the literature. As such, my model also builds a conceptual connection between securitization and conglomerate formation, two seemingly remote contexts that are both important in their own right.

My model follows Van Nieuwerburgh and Veldkamp, 2009, Van Nieuwerburgh and Veldkamp, 2010, which study the endogenous information acquisition of investors with heterogeneous expertise, and uses their modeling approach. My work differs from theirs, as my focus is on the implications of asset design and asset pricing rather than on the portfolio choices of individual investors.

There are a few papers that also study the endogenous information acquisition of investors. Peng and Xiong (2006) show how the limited attention of a representative investor leads to categorical learning and return comovement. In a multiple asset, noisy rational expectations model with rational inattentive investors, Mondria (2010) shows how investors' attention allocation generates asset price comovement. For technical simplification, these papers do not incorporate the interaction of heterogeneous investors. Subrahmanyam (1991) demonstrates how markets of baskets of securities reduce adverse selection cost. Recently, Goldstein and Yang (2015) identify strategic complementarities in the trading and information acquisition of investors informed about different components of the same asset. These two papers endow traders with exogenous information in their baseline models, and traders are ex ante identical in the extensions with endogenous information acquisition.

My work is also related to the literature on security design. In addition to rationalizing the feature of bundling loans by asset classes of securitization, my model complements this literature in two aspects. First, it studies the interaction of heterogeneous security buyers, which existing security design models (e.g. Demarzo and Duffie, 1999, Demarzo, 2005) typically abstract from. Second, existing security-design models (e.g. Townsend, 1979, Dang et al., 2013) usually focus on the extensive margin of information acquisition; i.e., how to reduce the costly information acquisition of security buyers. My model focuses instead on the intensive margin: given the resources available to security buyers for information acquisition, how can the seller induce buyers to use those resources in his preferred way? A more detailed discussion on the relation of my work to this literature is given in Section 5.2.

My work is also related to the literature on financial innovation (e.g. Marin and Rahi, 2000, Duffie and Rahi, 1995). I obtain a similar result that more complete, but less than perfectly complete financial markets may not be Pareto optimal, as shown in Section 5.4. In this literature, each investor's private knowledge (i.e., knowledge NOT obtained from prices) of assets being traded is typically exogenous. My model complements their work by exploring how asset design can endogenously affect each investor's incentive to acquire private knowledge of asset fundamentals.

Lastly, my work complements the literature on corporate diversification by offering an alternative perspective on conglomerate formation. A detailed discussion can be found in Section 6.

The rest of this paper is organized as follows. Section 2 introduces the setup of the baseline model. Section 3 illustrates the discipline channel by studying a polar case in which only one risk is non-diversifiable. Section 4 illustrates the trade-restriction channel and the specialization-destruction channel by studying another polar case in which all sources of risks are non-diversifiable and play a symmetric role. Section 5 discusses the general case and several issues of the baseline model, and introduces a generalization of the baseline model that establishes the optimality of categorization strategy. Section 6 discusses the application of the model in the context of corporate diversification and relevant empirical evidence in the existing literature. Section 7 concludes.