Taming the volatile OTC
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October 15, 1999
When you find a new use for an old tool, it fits our definition of a good deal. And that’s exactly what we have found: another way aggressive investors can use our U.S. market timing systems to seek higher returns with only moderate risk.

In a nutshell, we have done a study applying our four U.S. equity timing systems to the Nasdaq Composite Index, made up of more than 5,000 U.S. and international stocks that aren’t traded on a stock exchange. Without timing, the Nasdaq Composite underperformed the S&P 500 Index while subjecting investors to much deeper losses along the way. But with timing, the Nasdaq Composite, which we will refer to as the OTC, handily beat the S&P 500 Index. With leverage and timing, the OTC did much better. And it did even better with the addition of a short-side component – the use of a mutual fund that attempts to make money during market declines.

When it was timed with our systems, the OTC not only produced superior returns. In every case, it produced lower interim losses than the comparable Standard & Poor's 500 Index strategy with the same timing systems. We don’t know if that will continue in the future, of course. The OTC is more volatile than the S&P 500 Index. That could mean much greater losses for OTC investors in any sudden market drop – and not all such declines can be avoided by any timing system.

You’ll find the details of our study in Table 1. Here are a couple of things you should know to understand the numbers. First, the worst-period results are based on calendar quarters and years, not the rolling months we have used in other studies. This is because our best historical data on the OTC market is based on calendar quarters.

Timing the OTC market
Our four U.S. market timing systems weren’t specifically designed for the over-the-counter market. But based on a hypothetical study back to 1972, they would have substantially increased the return of this profitable arena.
  S&P 500 Index OTC w/o timing OTC 1 to 0 OTC 2 to 0 OTC 2 to -1
1972 19.0 17.2 15.7 26.4 26.4
1973 (14.8) (31.1) (0.6) (7.9) 17.5
1974 (26.5) (35.1) 2.2 (4.8) 17.4
1975 37.3 29.7 30.2 52.5 45.0
1976 23.7 26.1 18.3 30.5 23.2
1977 (7.4) 7.3 5.7 6.0 6.0
1978 6.5 12.4 17.5 28.1 33.0
1979 18.6 28.1 26.2 41.3 40.8
1980 32.6 33.9 37.9 58.6 55.4
1981 (4.9) (3.2) 9.0 2.2 4.1
1982 21.6 18.7 36.6 59.9 63.5
1983 22.5 19.9 30.8 51.5 58.9
1984 6.2 (11.2) 5.0 0.0 12.6
1985 31.8 31.3 29.6 54.3 54.3
1986 18.7 7.4 15.6 23.2 23.2
1987 5.2 (5.3) 16.2 25.6 54.6
1988 16.5 15.4 13.3 18.1 18.1
1989 31.7 19.2 17.3 25.6 25.6
1990 (3.1) (17.8) 6.3 4.5 20.9
1991 30.5 56.9 54.5 98.2 98.2
1992 7.6 15.5 13.9 20.0 20.0
1993 10.1 14.7 8.2 14.7 14.7
1994 1.3 (3.1) 1.9 (0.1) 0.7
1995 37.6 39.9 19.4 34.2 30.9
1996 23.0 22.7 9.4 13.0 4.4
1997 33.4 21.6 18.5 29.8 21.7
1998 28.6 39.7 27.7 49.1 52.2
1st half 1999 12.4 22.5 1.8 0.3 (8.2)
           
Compound return 14.0 12.2 17.1 25.3 28.5
Standard deviation 18.0 24.9 16.0 33.2 33.8
Worst quarter (25.0) (26.7) (6.9) (13.4) (16.9)
Worst four quarters (32.6) (39.6) (2.0) (11.2) (8.0)
Worst 12 quarters (25.2) (47.6) 22.9 16.0 29.3
Worst 20 quarters (1.1) (26.6) 66.6 76.8 96.5
           
$10,000 grew to $370,805 $ 235,664 $ 768,908 $4,957,032 $ 9,836,362
$1,000/year grew to $448,876 $ 372,741 $ 663,895 $3,614,698 $ 5,781,115
Second, the timing systems we used in this hypothetical study were not designed specifically for the over-the-counter market. They are designed for the broad U.S. stock market, including both listed and unlisted securities. We believe this adds credibility to the results by reducing any effect of optimization. And it suggests that at least in theory, better performance should be achievable by using systems specifically designed to work with the OTC market.

These results convince us that aggressive investors may benefit from including timed OTC funds in their portfolios. The key word here is "timed." Without timing, over this period the OTC looks like a comparative laggard, with lower return than the S&P 500 Index (only 12.2 percent compared with 14 percent) and a much higher standard deviation.

But the OTC’s additional volatility (which is what standard deviation measures) makes it a better candidate for timing, because there’s more opportunity to buy lower and sell higher. As you will see toward the bottom of the third column, timing boosted the compound return of the OTC significantly (by 41 percent) while it reduced the standard deviation by 36 percent. Timing also made huge reductions in the losses experienced in the worst quarter and worst four quarters.

You will find more spectacular returns in the table’s fourth column, showing the hypothetical results of using timing and leverage to try to double the performance of the OTC. This is somewhat similar to a timed investment in the ProFunds UltraOTC Fund, which attempts to double the performance, in either direction, of the Nasdaq 100 Index.

Here’s where our data has a disconnect. The Nasdaq 100 consists of only the largest 100 Nasdaq stocks, and isn’t nearly as broad as the Nasdaq Composite Index. The enhanced index funds that invest in the OTC market are based on the Nasdaq 100, not the Nasdaq Composite. Yet the historical data is on the Composite, not the 100.

Even though the 100 index contains fewer than 2 percent of the stocks of the Composite, the Composite is market-value weighted, meaning each company’s security affects the index in proportion to its market value. This means, in turn, that the 100 largest stocks will account for a very significant part of the movement of the Composite index. This leads us to believe that over time, the performance of these two Nasdaq indexes will be somewhat similar.

To test that, we looked at a decade of data for the Fidelity OTC Fund, which is not an index fund, but seems loosely modeled after the Nasdaq 100 Index. This fund’s performance tracked the Nasdaq Composite reasonably well. In the five years ending August 31, 1999, Fidelity OTC achieved 88 percent of the performance of the Composite index; for the 10-year period ended August 31, 1999, the fund achieved 94 percent of the Composite.

The far-right column shows the higher performance – and higher volatility – that could have come from taking short-side positions when all four of our timing systems were on a sell signal. Investors might achieve similar results by investing half their money in the ProFunds’ UltraShort OTC Fund, which attempts to double the inverse of the performance of the Nasdaq 100 Index.

This strategy is not for timid investors, and it’s not suitable for the majority of anybody’s portfolio. However, we believe our timing systems can be successfully used with OTC index funds to give investors a shot at returns of 15 to 25 percent at a reasonable level of risk.

If you are currently using our systems with enhanced index funds such as Rydex Nova or ProFunds UltraBull, you might want to consider investing some of that money in funds like Rydex OTC and Arktos and the ProFunds UltraOTC and UltraShort OTC. We think this is a viable way for aggressive investors to harness the volatility – and profitability – of the OTC market.
 
 
 

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Important notes

This document refers to hypothetical performance data. We have done our best to present this information fairly, but you should be aware that hypothetical performance is still potentially misleading.

Hypothetical data does not represent actual performance and should not be interpreted as an indication of actual performance. This data is based on transactions that were not made. Instead, the trades were simulated, based on knowledge that was available only after the fact and thus with the benefit of hindsight.

There is no indication that these hypothetical results would have been achieved even if our company had been in business and the appropriate investment vehicles had been available at the time.

Any strategy used in backtesting can be changed at any time, with the benefit of hindsight, in order to show better results. In fact, strategies can be continually backtested and modified until the desired results are achieved. Hypothetical performance also does not include the effect, if any, that trades might have on market prices if those trades were actually executed. And backtesting necessarily excludes any possible changes to an advisor’s decision-making that might occur as a result of the real-world effects of trades.