The 60% rule

As an investor it is quite important to realize how reliable our methods or indicators are. If we don’t know the limitations of our tools, then how are we going to use them well? Some readers may be surprised to know that I have never more than 60% confidence in any of my methods, cycles, indicators or forecasts. I expect them to be wrong at least 40% of the time. And I never have more than 60% confidence in methods, indicators or analysis I find elsewhere on the internet or in books, no matter how compelling the evidence that is being presented. That’s what I call the 60% rule. More on that and on its implications for trading further down in this post.

Let’s start with our customary look at the S&P 500 index (click for larger image):

S&P500

Last week’s drop saw the market test important support near 1750 before rebounding. We have another week of lunar red period to go, so it’s quite possible that the lows get tested again this week. But several of my indicators now show a nice bottom. The MoM indicator has actually dropped to its lowest levels since May 2012.
So, I think the recent lows will hold and then we will probably see the market climb back until March or April. Sentiment has become especially negative for emerging markets, but my weekly key reversal system is starting to give long term buy signals for markets like Indonesia and Vietnam. Of course, we cannot be more than 60% confident in any of the above.

Why this 60% rule? Well, if we are lucky enough to find some edge in the market it will always be a small edge. There are no big edges to be found in liquid markets. If we find something that works 60% of the time we can do very well already. But our game plan will need to take into account that we will be wrong 40% of the time. Peter Lynch, the famous investor, formulated it like this:

“In this business, if you’re good, you’re right 6 times out of 10. You’re never going to be right 9 times out of 10.”

The advantage of knowing that you will be right only 6 times out of 10 is that you start with realistic expectations and will not suffer from overconfidence. It also comes easier to cut losses short when you know that you will be wrong 4 times out of 10.

So, I always aim for 60% accuracy with my methods and in my forecasting. And that’s hard enough to do. For example, we expect markets to be stronger in lunar green periods than in lunar red periods. How reliable is it? Comparing the green periods to the red periods that come immediately before and after, is a simple way to remove effects from longer term trend and offers a fair comparison. Since 2009, when we started this blog, the green periods have outperformed the red periods that come before and after it 57.5% of the time. So, quite close to 60%.

Good luck,
Danny

 

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By Dan

Stock trader since 1986. Method based on proprietary indicators, seasonal patterns and moon cycles.

9 comments

  1. Hi ptolemy
    Have you ever heard of the Off Board Edge when trading futures?
    Ruth Miller wrote a book on the OBE..and showed an 85% win rate.
    kind regards
    bob

  2. To Bob Collett,

    Here are real performance stats from a futures account of mine:

    Trades 883
    Longs Won 167 of 205 (81.46%)
    Shorts Won 462 of 678 (68.14%)

    But lack of risk management was the issue:

    Average Win 271.70 pips
    Average Loss -703.84 pips

    That account lost 75%. Just shows winning percentages mean zilch.

    1. You are right that accuracy rate and % winners are two different things. Winning percentage can easily be cranked up by taking small profits while letting losses run. Even a dumb system will average 66% winners if you aim for 1% gains with a 2% stop-loss. If you take 1% profits with a 4% stop-loss, then winning % will go up to 80% and you would only break even on your trading.
      So, winnign % not a direct measure for accuracy rate. To measure accuracy rate we have to compare similar periods or same % for profit loss targets. When the “baseline expectation” is set to 50%, then we can measure our trading accuracy most easily.

      Danny

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