Essays on Asset Management

A significant portion of household wealth is managed by financial professionals and investment companies. How these entities create value for their investors and whether their contributions are sufficient to justify the compensation are of great interest to both researchers and the general public. In this dissertation, we seek to further our understanding of the asset management industry. Chapter 1 explores the relationship between the fee of an investment product and how it is described by the money manager. To control for variations in dynamic strategies of differentfunds, we focus on Unit Investment Trust (UIT), a class of investment product with buy-and-hold portfolios and fixed life spans. In order to compare different portfolios, we adopt the method of collaborative filtering to transform the portfolio to a latent representation. A significant portion of dispersion in fees of UITs is correlated with how the investment objectives are stated, even after controlling for the variation in the portfolios. UITs whose investment statements include morepersuasive marketing languages and more discussions on stock selection procedures, diversification, and stock weightings charge higher sales fees. In Chapter 2, we consider a model of money management that reconciles the investment expertise of institutional investors and the lack of outperformance on their in-house managed capital.Due to the synergy between institutions' knowledge and external asset managers' skill, institutions never trade on their information directly. Instead, informed institutions delegate investment decisions to external asset managers while passing on their investment knowledge. Only uninformed institutions invest in-house due to the search cost of finding a skilled asset manager. We discuss how management fees and the size of the money management industry are related to the number of institutions with expertise and the value of institution expertise. Price is more informative when knowledge of institutions is more diffused.Chapter 3 is based on a joint paper with Bryan Routledge and Matthew Denes. We model the dynamic decisions of a Venture Capital (VC) fund during fundraising and capital deployment. VCs with better access and network receive greater deal flows, raise larger funds, and deploy capitalmore frequently. On the contrary, VCs with better screening abilities deploy capital at a slower rate due to the higher expected payoffs of future prospective projects. We estimate the model using VC deal data at each fund level. Funds differ mainly in access and are homogeneous in terms ofscreening ability. Differences in deal flows alone account for almost all the dispersion in fund values. By optimally matching estimated deal flow and screening, we can improve the average fund value by 2.5 percent. This corresponds to the unrealized gain from complementarity.