In this paper the relation between aggregate mutual fund flows and stock market returns is analysed with respect to three issues. First, we study the relation between fund flows and long-term realized returns (past, current and future). Second, we find out that fund flows are not driven by fundamentally expected returns. Mutual fund investors appear to have naive expectations, as it seems that they just extrapolate past price trends into the future. This leads to a substantial performance loss of more than one percentage point per year. Third, the firstly presented results of the German fund market resemble those of the US market. Differences between the two fund markets do not seem to influence investor behaviour.

Using survey data we analyze institutional investors' expectations about the future performance of fund managers and the impact of those expectations on asset allocation decisions. We find that institutional investors allocate funds mainly on the basis of fund managers' past performance and of investment consultants' recommendations, but not because they extrapolate their expectations from these. This suggests that institutional investors base their investment decisions on the most defensible variables at their disposal, and supports the existence of agency considerations in their decision making.

This paper investigates the implications of investor learning for the sensitivity of mutual fund flows to past performance. We illustrate theoretically that when some sophisticated investors learn from past fund performance to form their posterior expectations of managerial ability, the flow-performance sensitivity should be weaker for funds with more volatile past performance and longer track records. Moreover, the dampening effects of performance volatility and fund age on the flow-performance sensitivity should be stronger for funds attracting more sophisticated investors. We provide supporting evidence for this investor learning hypothesis using mutual fund flows and compare the relative level of sophistication among investors in load versus no-load funds, institutional versus retails funds, and star versus non-star funds.

Peter Lückoff investigates why fund flows and manager changes act as equilibrium mechanisms and drive the performance of both previously outperforming and previously underperforming funds back to average levels.

How to prevent runs on open-end mutual funds? In recent years, markets have observed an innovation that changed the way open-end funds are priced. Alternative pricing rules (known as swing pricing) adjust funds’ net asset values to pass on funds’ trading costs to transacting shareholders. Using unique data on investor transactions in U.K. corporate bond funds, we show that swing pricing eliminates the first-mover advantage arising from the traditional pricing rule and significantly reduces redemptions during stress periods. The positive impact of alternative pricing rules on fund flows reverses in calm periods when costs associated with higher tracking error dominate the pricing effect.

This book addresses the importance of diversification for reducing volatility of investment portfolios. It shows how to improve investment efficiency, and explains how international diversification reduces overall risk while enhancing performance. This book is a crucial tool for any investor looking to improve the profit gain from their investment.

This thesis contributes to existing literature by analysing the role of performance streaks in the US mutual fund industry. Existing research suggests that performance streaks, i.e. multiple consecutive months of positive or negative performance, are an important determinant of mutual fund flows. My dataset comprises monthly returns and net-flows from US equity mutual funds from 1996 through 2015. My first analysis shows that streaks are not an indication of performance persistence and should not be used in investment decisions. Next, I develop two forecasting models using streaks based on several different performance metrics, such as excess returns and CAPM-alphas. The first one is a probit model that forecasts future investor sentiment, measured by the sign of future net-flows. This model is very robust to different time period specifications. The second one is a multiple linear regression model that forecasts actual future net- flows. The performance of this model strongly depends on the time period specified, as it performs poorly following the financial crisis. In both models the best-performing specification uses streaks based on CAPM-alphas. However, a Shapley decomposition reveals that streaks are, despite being statistically significant, the least-important predictors of future net-flows. Instead, lagged net-flows are the most-important determinants of future net-flows. The results of this thesis suggest that active streaks tip the scales when investors decide between two or more funds with a comparable track record. Hence, the results presented are ambiguous regarding investor rationality.