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Advisor select

Bob Meeder is President & CEO of Meeder Investment Management. He joined the firm in 1983 and has served as President since 1995. Bob oversees the formulation and integrati +

Meeder Quantex Fund
FLCGX (Ret Class)
QNTAX (Adv Class)
QNTIX (Inst Class)
Fund Family
Meeder Funds
Fund Advisor
Meeder Asset Management, Inc.

6125 Memorial Drive
Dublin, OH 43017

T: 866-633-3371

Out of Favor and Poised to Recover
Meeder Quantex Fund
Aug 29, 2017

Q: What is the history of the fund?

The Meeder Quantex Fund was launched in March 1985 by Meeder Asset Management. It is an open-end equity mutual fund that employs a quantitative strategy to invest in value stocks within the market capitalization range of the Russell Midcap Index. 

The fund currently has assets of about $93 million under management.

Q: What is your investment process?

When we developed our quantitative investment strategy, our goal was to create a tool that would remove the emotion and qualitative considerations from the stock selection process. We wanted to be able to create a portfolio that was predictable in performance with results similar to the mid-cap and small-cap indices.

When we developed our quantitative investment strategy, our goal was to create a tool that would remove the emotion and qualitative considerations from the stock selection process.

After much research, we came across a study that analyzed the performance of mid-cap stocks based solely on the market-cap range. So, we constructed a model that identifies the optimal stocks in the market-cap range. We then back-tested the model to determine whether monthly, quarterly, semi-annually, or annually updates would produce optimal results

Using our model, we run an annual analysis at the beginning of each year to identify optimal stocks to be included. For the stocks that fall within our market-cap range, we filter and rank the stocks by quality and beta on a weekly basis. If a stock falls in the bottom 5% of the list in range by quality, we ignore it. Stocks having betas in the top 10% of the range are also ignored. The remaining stocks, which number from 100 to 130 names, are included in our portfolio and are equally-weighted.

So, our stock selection process is 100% quantitative with no qualitative overlay. We don’t have portfolio managers poring over financials and looking at management teams and business strategies; and we do not use the traditional fundamental bottom-up approach. 

Instead, our rules-based quantitative approach selects a portfolio of undervalued stocks, and tries to avoid overvalued stocks.

Q: Would you describe your model?

The function of the model is to identify value stocks. Each year, we identify about 20 to 30 new names that have underperformed during the previous year. They are names which are out of favor in the market and we call them “fallen angels.” 

For example, of the 31 new names that came into the portfolio in 2009, they were on average down 66% in 2008. In 2011, which was another tough year, the new names we added in 2012 had been down 35% on average.

To summarize, we have a model that looks for companies that are fallen angels or out of favor based on certain price characteristics and that gives us a list of those names and then the model goes through it and finds the right market cap range in those stocks and then we apply other filters and it becomes part of our portfolio.

Q: How do you know if the model has worked?

It is important to stress that there are periods when the fund can get out of sync and the fallen angels added a little too early. The 10-year performance is important because it demonstrates that the process has worked over extended periods of time. 

There are times when this fund is a little early and underperforms. The best example was 1999 when this portfolio had zero exposure to technology. The portfolio struggled due to the fact that during the late 1990’s the only thing that was moving was technology. Nonetheless, we held onto our beliefs and the model’s discipline. When the market turned in 2000, this portfolio significantly outperformed for the following three years because it was underweight technology. 

In 2015, we started adding energy a little early. About six-to-nine months later, the energy markets bottomed out, struggled a little bit, and then turned up. By early 2016, the fund was off to the races again.

Currently, the fund is buying out of favor retail names. While that has hurt the fund a little bit this year, the model has identified those stocks as being cheap on a relative basis.

Q: What market-cap range do you focus on?

The market-cap range varies every year, but for 2017 it was $1 billion – $9 billion. Typically, it usually ranges from less than a billion to $5 billion. The range is designed to fall into the mid- and small-cap space, but majority of the names are going into the mid-cap range with a few at the upper end of the small-cap range. 

Q: How does your model detect out-of-favor stocks?

It’s not just the percentage decline that identifies a fallen angel. Within the market cap range, a stock might have dropped 2% or 50%, or might even have gone up but that would have gone up less than others and still in our market cap range, and still would qualify as a fallen angel.

  • Inception: March 20, 1985
  • AUM: $93 million

We do however separate stocks that fall to the extremes. The bottom 5% of the stocks when scored on quality or the highest 10% scored on beta within the market-cap range are those that we avoid.

Q: What determines your sector allocation within the market-cap range?

We don’t have any pre-defined sector limits. In the late 1990’s we had zero names in technology sector. Instead, we are slicing and dicing our cap range of the S&P 500 and the S&P 400 indices. 

The model focuses only on the stocks that fall within that range.

Q: Have you ever modified your model?

Actually, we did tweak the model once three years ago when we incorporated the quality and beta analysis. That is the only modification we have made since it was originally constructed in 1989. 

We are always looking for ways to improve our stock selection process, but unless our research gives us confidence that a modification will enhance the results, we will stick with our current model.

Q: Why is the fund rebalanced annually?

When we originally built the model, our testing showed that that an annual rebalance was superior to semi-annual, quarterly, or monthly updates. Perhaps by letting the winners run a full year gave the performance a little boost. 

Also, annual rebalancing has an additional advantage of being tax efficient in terms of capital gains and income distributions. 

Q: Do you consider revenue or earnings growth in you stock selection? 

We do not look at either of these metrics. We are strictly focused on stocks of good companies that are currently out of favor and have the potential for rebounding. Our valuation does not explicitly look at growth expectations, but rather assesses whether or not a stock has become undervalued and is poised to recover. 

Q: What is your benchmark index?

Well, it is somewhere between the Small- and Mid-Cap Indexes. An appropriate index to benchmark the fund’s performance would be an equal blend between the S&P Mid-Cap 400 Index and the Russell 2000 Index.

Q: What is your portfolio construction process?

At the beginning of each year, we reconfigure the portfolio so that every name starts with an equally-weighted position. Typically, the maximum for any name is 1% of the total portfolio. And on average, we are diversified by having 100 to 130 different securities.

Every week we run the quality and beta filters. If a stock falls into one of the filters, it is eliminated from the portfolio.

We also maintain an allocation in S&P Mid-Cap 400 futures and use that allocation to balance our cash flow needs. For stocks that have appreciated outside our market-cap range we sell and reinvest in other stocks within the portfolio.  

Q: How do you define and manage risk?

By relying on our model and using the beta filter, we avoid investing in stocks that have high betas or excess volatility and trade out of the names. Our other quality model determines if the companies that are sufficiently sound enough to weather a storm. 

We prefer companies that have historically developed a level of quality but have fallen on hard times.