Investors routinely pour cash into recently launched funds. Is that a mistake?
By Lee Davidson, Madison Sargis, and Tim Strauts
The following article was part of a larger research study on the subject. To get a copy of the full white paper, email the authors at email@example.com.
It is easy to dismiss new funds as an immature, unproven, and unimportant segment of the asset-management industry. But the truth of the matter is that the asset-management industry is dominated by new funds. Globally, we find that new funds account for the preponderance of new asset flows. In 2015, global fund flows reached approximately $516.4 billion across the three asset classes in our study—equity, fixed income, and allocation. Of the total, new funds with less than 12 months of track record accounted for $379 billion, or 73% of all flows. Even in years when the industry experiences net outflows, new funds continue to garner assets. In 2014, new funds grabbed $316 billion in net inflows compared with negative $526 billion in net outflows for funds with greater than 12 months of track record.
We hope, therefore, that it does not come as a surprise why the patterns of flows into new funds should be of interest to us as researchers. It is simply too big and too important to ignore. Furthermore, we are particularly concerned about investor behavior when investing in these unproven products. Do we find that investors prefer what’s good for them?
In this paper, we explore the relationship between observed investor preferences for and eventual investor outcomes in newly launched funds. To conduct this exploration, we built two models that unpack the historical correlations between both forward risk-adjusted returns and forward cumulative fund flows with observed fund characteristics, economic regimes, and category environs. In isolation, each model offers us a multivariate view of the factors that drive future flows and risk-adjusted returns for newly launched funds. By comparing the outputs from the two models, we can also identify potential conflicts of interest for asset managers. A variable that has tended to result in higher flows but lower returns, for example, socially responsible investing, presents a difficult choice for asset managers over whose interests to prioritize.
This tension between what investors have preferred and the outcomes investors are seeking also highlights poor choices on the part of the investing populace. We find that investors have been inconsistent with how they allocate their assets among newly launched funds. For example, we find that investors prefer cheaper funds that, not surprisingly, tend to result in better outcomes. But we also find that investors prefer funds that invest in more-popular, familiar stocks that have done well recently when the opposite choice would have resulted in better risk-adjusted returns.
Newly launched funds are of particular interest to us as they often lack information that we know investors use to make decisions. From prior work, we have observed that investors exhibit strong preferences for funds with strong performance track records. While this is perhaps not surprising, it does raise the question of what information investors use to make an investment decision when a performance track record is unavailable. Despite the lack of information on newly launched funds, we have already presented evidence that new funds can garner immense assets almost immediately.
Given the lack of relevant data early on in the life cycle of a fund, variable selection and variable definition were critical choices in our study. Variables included in the model had to be both widely available in our sample and hypothesized to be relevant drivers. We included 23 variables in the fixed-income and allocation models. In the equity model, we were able to expand this number to 39 because of the quality of Morningstar’s equity data and portfolio holdings database. Many variables included in the model are to be expected: fees, firm-level characteristics, index fund, socially responsible, and portfolio disclosure. However, some variables are less common. Category-level data points such as market concentration and firm market share by category were constructed specifically to examine the role of category structure on newly launched fund outcomes. Furthermore, we also expanded the use of our ownership data by building data points to examine whether a fund manager behaves in a similar fashion to other types of managers. An example of this is the Manager CFA Ownership data point, which captures the extent to which the manager of a newly launched fund is buying stocks in a manner similar to the typical manager who is a CFA charterholder. In general, we attempted to capture various perspectives of a new fund by focusing on data categories such as fund structure, manager demographics, firm reputation, competition within category, portfolio disclosure, portfolio style, and economic regimes.