ABSTRACT TWO ESSAYS ON MANAGERIAL BEHAVIORS IN THE MUTUAL FUND INDUSTRY LENG LING ESSAY 1: DOES MUTUAL FUND WINDOW-DRESSING PROMOTE FUND FLOWS? I investigate the effectiveness of window-dressing as a potential strategy to be used by mutual fund managers to promote fund flows. Using a rank gap measure as a proxy for the likelihood that window-dressing has occurred, I find that fund investors as whole punish those managers who are suspected to have engaged in window-dressing. That is, I find a negative relation between the window-dressing measure and net fund flows in subsequent quarters after controlling for fund performance, size, expense ratio, and other pertinent characteristics. I also find that window-dressing leads to higher trading activities and lower fund performance. ESSAY 2: A LIFE CYCLE ANALYSIS OF PERFORMANCE AND GROWTH IN U.S. MUTUAL FUNDS I propose a five-stage growth model to describe the life cycle evolution of mutual funds and show that mutual funds exhibit distinctive performance, size, expense ratios, asset turnover, and other pertinent characteristics through stages of incubation, high-growth, low-growth, maturity, and decline. I also investigate the viability of managerial strategies to affect a fund’s life cycle evolution and find that changing a declining fund’s investment objective is effective in rejuvenating asset growth and thus repositioning the fund to younger life cycle stages. However, the strategy of adding portfolio managers appears to have no such rejuvenation effect.

Life insurance policies are not equally profitable is sense of expected value. In practice, profitability is an output of complex cash flow models, which need utilizing special systems and the run time of such calculation can be significant if number of policies is high. Therefore we consider variables, which change most frequently, stimulate the profitability model with several values of these variables and then we search for a regression model to explain the changes. We apply Gamma regression on the data. But what if there exist some policies which are negative? Then we determine these policies with logistic regression applied on data censored to the binary form. Loss of these policies is modelled using symmetrical Gamma model. These three models, when considered together, can be viewed as a single model, which is a generalization of the well known zero inflated count model. The most interesting part of inference in such model is diagnostics. We show that the basic types of residuals - Pearson, deviance and quantile - can be defined. We also build an ordinary linear model and we compare utility of these two approaches. While building models, we meet various statistical issues like dimension reduction of yield curve or dispersion proportional to sum insured. 1