Discussions are often held about the
use of using rolling period analysis vs. single end point
analysis for fund and manager evaluation. This short paper
focuses on some of the pros and cons of using rolling period
analysis.
Over the past several decades, many
approaches have been used to understand investor's risk and
return expectations. Currently two distinctly
different approaches are in use. With the first method,
investors answer a series of questions, some of which may
not be investment related, to determine the level of risk
they are comfortable with, then similar recommendations are
made to all investors in that risk-level group. This
article discusses a second method focused exclusively on
investment issues relevant to high-net-worth and small
institutional investors.
At its core, K4 Manager
Selection is a decision tool that dynamically constructs a
multi-factor model and ranks alternative investment
solutions based on the characteristics of these alternatives
and the relative importance of each characteristic as
defined by the model. Multi-factor models are already
widely used for analysis of investments, but K4
Manager Selection takes a unique approach to the
construction of these models and the application of the
resulting model to the decision process.
This paper explores a comparison of
commonly-used filtering and screening decision processes, less
frequently used weighted factor models and an alternative approach
using the relative importance of preferences. While the
criteria for screening, weighted factor model and preference-based
decision making may be based on the same information, the results
may vary significantly. This paper explains the structure and
the pros and the cons of each selection process. In addition,
the manner in which the selection processes may power an Investment
Policy Statement and performance reporting is presented
This Due to the shortcomings of screening and
filtering methods for manager and fund evaluation, many advisors and
analysts have been forced to build tools to effectively
differentiate the importance of their investment criteria and the
results of the funds or managers. In rare cases, these types
of tools are being built into analytical software to make the task
of building these models easier. Much more frequently, the advisor
is faced with the time-consuming task of building and maintaining
such tools and avoiding the pitfalls inherent in applying a
multi-factor approach. The very fact that efforts to build
these models are occurring validates the shortcomings of existing
analysis tools.
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