Klein Decisions’ K⁴ Fund Selection offers you the opportunity to build your own unique scoring models for mutual funds. You can choose from almost two hundred different factors and three different weighting schemes. No matter what you’re seeking, K⁴ has the flexibility to enable you to build the appropriate model. But with this flexibility comes a lot of choices. That’s usually a good thing, but it can be daunting, especially when you’re confronted with two hundred choices at a time. Some users have been overwhelmed by the sheer number of available factors, but there is a simple resolution. By dividing the factors into five specific groups, you can create better models and obtain better results.

The first step is realizing that the goal is to create a good factor model, not simply to adapt a screening process already in use. For example, if you are coming from a screening background, you may be tempted use the maximum – fifteen factors – in an effort to convert your screens into a factor model. Generally, that’s not a good approach because screens often use the same factor over several different time periods (e.g., 3-, 5-, and 10-year return). While these may be helpful in narrowing a screen’s universe, they can be problematic in a factor model. With so many factors, the lowest weighted will be virtually insignificant while others (e.g., 3-, 5-, and 10-year return) will be highly correlated. As a result, while you think you’re using a lot of factors, you’re really only using a few that carry all the weight. That is probably not what you really intended to do.

Instead, the best factor models typically only use a handful of factors. (See Simple Models Are Often The Best). But with so many to choose from, how do you decide which ones make up that handful?

Start by realizing that almost all important factors fall into one of five groups:

• Return
  Return factors include various time periods and also the various measures of return such as return NAV; Return +/- Peers or the Benchmark Index; Best Year; and Up Market Ratio.
• Risk
  Risk measures include Standard Deviation, Beta, Worst Year, and Down Market Ratio.
• Value Added
  Alpha, Information Ratio, Sharpe Ratio, and Morningstar Rating are examples of value added factors.
• Consistency
  Batting Average and number of positive months are some of the measures of consistency.
• Portfolio Fundamentals
  Fundamental factors such as the P/E or earnings growth rate can help you drill down to find funds in specific corners of broader categories such as “growth at a reasonable price” rather than simply “growth.”

Effective models can be created by selecting one or two factors from these major groups. Many models draw factors only from the first four, Return, Risk, Value Added, and Consistency, skipping Portfolio Fundamentals. If you follow this approach, you’ll be using 4-10 factors, a far cry from the K⁴ allowable maximum of fifteen.

With this reduced number of factors, you can be assured that each will play an important role in the model. There is also less likelihood that they’ll be highly correlated. In essence, you’ll have a better model with fewer factors. Dividing the factors into five specific groups and selecting one or two factors from each group can help you make more consistent and logical choices. Not only that, it will force you to think about your ultimate goal for each, helping you to better understand your own model – yet another benefit of the K⁴ process.

Product Highlight - K⁴ Fund Selection

Scoring Beats Screening for Mutual Fund Evaluation and Monitoring

For many investment professionals, mutual fund analysis, monitoring, and selection are an integral part of the services they provide. Like all investment decisions, this requires them to consider a number of different and often conflicting attributes. Long-term performance needs to be considered along with expense ratio and risk. Accurately measuring their relative importance and resolving the conflicts between them is a complex and difficult task. In addition, many are measured in different units making aggregation of the results impossible without a sophisticated scoring system. All of this can really only be accomplished with a clearly defined process that is quick, efficient, and objective. That is why these investment professionals need K⁴ Fund Selection.

K⁴ Fund Selection is an internet-based tool used to rank, evaluate and monitor mutual funds and ETFs. Unlike the pass/fail search and single point analysis methodologies used in other tools, K⁴ Fund Selection doesn’t simply eliminate funds based on equally-weighted stand-alone screens. Instead, it gives the user the ability to rigorously, objectively and easily create weighted factor models that score, rank and monitor funds on a number of attributes simultaneously. K⁴ Fund Selection yields a ranked list of all funds based on all their attributes and the importance the user places on each.

This ranking process not only resolves the major problems of pass/fail hurdles, but also eliminates the need to sort on individual attributes. With K⁴ Fund Selection’s comprehensive reporting and exporting capabilities, the user can quickly access a wealth of data for documentation, client presentations, and further analysis. When it’s time to re-evaluate or monitor funds, the entire process can be repeated with just a few clicks of the mouse, saving valuable time -- something most investment professionals can really use.

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