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Thomas Bulkowski’s successful investment activities allowed him to retire at age 36. He is an internationally known author and trader with 30+ years of stock market experience and widely regarded as a leading expert on chart patterns. He may be reached at

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Bulkowski's Forecast

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Market
Industrials (^DJI):
Transports (^DJT):
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Nasdaq (^IXIC):
S&P500 (^GSPC):
As of 06/22/2018
24,581 119.19 0.5%
10,773 -53.64 -0.5%
697 5.98 0.9%
7,693 -20.13 -0.3%
2,755 5.12 0.2%
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-0.6%
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-3.7%
11.4%
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Tom's Targets    Overview: 06/14/2018
25,750 or 24,500 by 07/01/2018
11,350 or 10,600 by 07/01/2018
695 or 645 by 07/01/2018
8,000 or 7,500 by 07/01/2018
2,850 or 2,700 by 07/01/2018

Written by and copyright © 2005-2018 by Thomas N. Bulkowski. All rights reserved. Disclaimer: You alone are responsible for your investment decisions. See Privacy/Disclaimer for more information. Some pattern names are the registered trademarks of their respective owners.

This article looks at the historical performance of the Dow Jones industrials over the last 10 years according to forecasted behavior and how the Dow is expected to move in the coming decade.

Most recent update: 12/12/2017.

 

Forecast: Background and Methodology

The December 2011 issue of Active Trader magazine had an article titled, "Looking ahead to 2012" by Larry Williams. In it, he describes a method to forecast the future by using historical price behavior. In an earlier article, "Cycles and seasonals: A 2011 roadmap, January 2011," he provides a similar review.

Both articles are based on the work of Edgar Lawrence Smith in the 1930s. Smith said that the stock market followed a 10-year cycle. Each year tended to repeat the behavior of the year a decade earlier. In other words, if you averaged all years ending in 1 (2001, 1991, 1981 and so on), that would give you a forecast for 2011. For 2012, you'd make a similar average, only use 2002, 1992, 1982, and so on.

That's what I did.

Forecast: Warning

While the approach sounds easy, it does have some problems as you move from the monthly to weekly to daily scales. Monthly is easy. You take the closing price at the end of each month for all years ending in the target year (meaning 2001, 1991, 1981 and so on for years ending in 1) to get the forecast. Events like 9/11 where the markets were closed from 9/11/2001 to 9/16/2011 didn't matter much. I used the close closest to the end of the month. However, on the weekly and daily scales, those become a problem.

The daily scale is worse. January 3 might be a Tuesday in one year but it's a weekend in another. The average of those will be a mess because you could be leaving off big numbers. For example, back in 1928, the Dow was below 300. Today it's over 24,000. Leaving off the 24,000 reading will make the average of what remains much lower. Thus, the line tends to jump all over the place.

In that situation, I just used the prior day's close and copied that into the gap and averaged the values as normal. That smoothed out the curve.

Let me also say that my data only goes back to October 1928 (which I discard since it's not a full year). Williams appears to use data going back to 1900 and perhaps earlier. The peaks and valleys you will see on my charts may be earlier or later than his. He may show an up trend and my charts do not. It's because of the missing data. For example, my charts use 8 samples and his use 10. That may not sound like much but it's huge when you are dealing with numbers that range so widely.

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Forecast: Adjustment Factor, Aligning Scales

In some cases, I removed the 2007-2009 bear market influence on the predictions. By that, I mean if I was looking for 2018's prediction, I'd skip any data from 2008 (which was in the middle of the bear market). That means the chart will show a gap (big rise or drop) because we've lopped off a sample. In that situation, I compute an adjustment factor which plots the new year starting at the same value.

I also do this for some of the charts shown below. I want to begin the prediction and the actual price series (if there is one) using the same value at the start of the year. That way, I can tell what the predicted ending year's value will be, as well as the yearly high and low values. The adjustment factor is this:

Adjustment Factor = (Prior Close)/(Predicted Close).

Each new prediction is multiplied by the adjustment factor once it's found. By that, I mean I only find one adjustment factor, the first one of the first year charted or if I remove the 2007 to 2009 bear market, I'll adjust the values to compensate for the missing years (and I remove the entire 2007, 2008, and 2009 years, not just the bear market from the prediction).

Simply put, this adjustment factor allows me to begin plotting the actual prices and the predicted values at or near the same value as the actual price (or as the last predicted close).

Let me also say that I often don't use the first value in the prediction when computing the adjustment. Why? Because January 2 is sometimes the first trading day of the year. Sometimes it's a weekend. When you average the numbers together, the first few predictions might be, say, 20,000 when later predictions are all in the 24,000 range. Thus, I wait for the day-to-day prediction to vary less than 5% before using the predicted value in the formula. Often, on the daily scale, that means waiting one or two additional days. And that's why some of the charts don't show the predicted line starting exactly at the closing price of the first day's actual price value.

Once I have the adjustment factor for the daily scale, I also use the same adjustment factor for the weekly and monthly scales. That way, all of the charts begin at the same value. And that makes the predicted yearly high and low closer to that shown on the daily scale.

All of these adjustments means the predictions of my charts versus someone else's will likely be different. But if you follow these rules, using the same data as I am, you'll get the same result.

Forecast: Monthly High Doesn't Match Daily High

One other thing. If you compare the highest (or lowest) predicted closing price on the daily scale, it probably won't match the predicted close of the weekly or monthly scales. Why not? Because the weekly scale uses Friday's close and the monthly scale uses the last trading day of the month. Those can be far from the highest/lowest close found on the daily scale.

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Forecast: Examples

DateCloseComments
12/31/1936179.90Note: First trading day is 1/4/1937
1/2/1947176.39
1/2/1957496.03
12/30/1966785.69First trading day is 1/3/1967
12/31/19761004.65First trading day is 1/3/1977
1/2/19871,927.31
1/2/19976,442.49
12/29/200612,463.15First trading day is 1/3/2007
 2,934.45Average of above numbers

Let's discuss a few examples for the Dow Industrials.

Suppose we want to make a prediction for 2017. We start with 1/1/2017 but that's a holiday. So we skip that day and start with 1/2/2017.

The data I have goes back to late 1928, but we start with 1937, the first full year of data ending with a 7. The table on the right shows what I have.

When no trading occurred on the target date (1/2/year), then I used the prior close.

The average of those numbers is 2,934.45. That's far away from the current Dow's close of 24,386.

Let's compute the next day's prediction: 1/3/2017. I show the data in the table.

DateCloseComments
12/31/1936179.90Note: First trading day is 1/4/1937. Use prior close
1/3/1947176.76
1/3/1957499.20
1/3/1967786.41
1/3/1977999.75
1/2/19871,927.311/3/87 is weekend. Prior close is this one
1/3/19976,544.09
1/3/200712,474.52
1/3/2017 Not used: We're trying to predict it
 2,948.49Average of above numbers

The average is 2,948.49

Because the two predictions are close, within 5% of each other, I use the first predicted close in the calculation for the adjustment factor. The adjustment factor allows us to plot the actual price and predicted price using the same scale. The first value of the two will be the same.

The closing price of the Dow industrials on the first trading day of 2017 is 19,881.76, so the adjustment factor would be AF = 19881.76/2934.45 or 6.77529.

If you multiply each predicted price for the remainder of the year by 6.77529, you'll get an adjusted price normalized to the current price. So the first plotted point would be 2,934.45 (the prediction for 1/2/2017) times 6.77529 or 19,881.76, which matches the first closing price of the Dow.

The next day's predicted close would be 6.77529 x 2,948.49 or 19976.87.

You'd continue this method for the remainder of the year and any succeeding year. Note that if you're plotting multiple years, you only calculate the adjustment factor for the first day of the first year. Then multiply that value by each predicted close for all remaining data (years).

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2017 Forecast

Picture of the Dow industrials forecast on the daily scale.

Pictured above is the forecast for the Dow in 2017, using the daily scale. The vertical magenta lines show important turns.

The Dow starts the year at its low and climbs from there until mid February. Then price eases lower in a choppy trend until bottoming in April.

Following that, we get a nice climb during the summer, to late July. The Dow makes a dramatic move lower into August and then recovers to peak in October, reaching the high for the year.

Something happens in October which sends the Dow plunging into November. The index recovers a portion of the decline by year end.

2017-2027 Monthly Forecast

Picture of Dow industrials forecast on the monthly scale.

This is the Dow forecast made on 1/1/2017 using the monthly scale and it covers a decade of price movement. The vertical magenta lines show important turns.

Price is fine during most of 2017 until the Dow peaks in October. Then the big decline starts in what looks to be a bear market lasting to 2019. Then we get a nice run up which continues until at least 2027.

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2016 Historical Forecast

Picture of the Dow industrials forecast on the daily scale.

This is the forecast for the Dow in 2016 (red), using the daily scale, accompanied by the actual Dow price action (black). Notice how close the predicted value was to the actual. Wow.

2015 Historical Forecast

Picture of the Dow industrials forecast on the daily scale.

This is the Dow industrials on the daily scale in 2015 (black) and the predicted path (red) of the Dow.

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2014 Historical Forecast

Picture of the Dow industrials forecast on the daily scale.

Pictured above is the forecast for the Dow in 2014, daily scale.

2013 Historical Forecast

Picture of the Dow industrials forecast on the daily scale.

This is the Dow industrials on the daily scale in 2013 and how it was predicted to do. Both the prediction and the Dow start the year from the same value.

The method predicted that the year would be a good one for the Dow. It was.

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2012 Historical Forecast

Picture of the Dow industrials forecast on the daily scale.

This is the Dow industrials on the daily scale in 2012

In May, the prediction headed lower, but the Dow turned up in June.

2011 Historical Forecast

Picture of the Dow industrials forecast on the daily scale.

This is the Dow industrials on the daily scale in 2011.

The prediction did quite well in 2011. It may have peaked or hit a valley a few weeks out of synch, but it did predict a bumpy year.

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2010 Historical Forecast

Picture of the Dow industrials forecast on the daily scale.

This is the Dow industrials on the daily scale in 2010.

The prediction was off this year. The drop predicted to start in September and bottom in October never appeared.

2009 Historical Forecast

Picture of the Dow industrials forecast on the daily scale.

This is the Dow industrials on the daily scale in 2009.

Although the prediction ended the year very close to the actual, it missed the large decline in March.

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2008 Historical Forecast

Picture of the Dow industrials forecast on the daily scale.

This is the Dow industrials on the daily scale in 2008.

This was another year that the index and the prediction missed each other. The Dow dropped even as the prediction said it would rise. If you invert the prediction, it would have been closer to reality.

2002-2011 Historical Forecast

Picture of the Dow industrials forecast on the monthly scale.

I show a picture of the Dow industrials from January 2002 to November 2011 on the monthly scale as candlesticks. Below that is the forecast using the method described earlier in this article.

At A, the index turns higher but the forecast had already shown the index moving up.

At B, the index hit a relative peak and so did the forecast.

At C, both began the move higher into the peak at D.

At D, the index started the bear market run. The forecast showed the index peaking a few months earlier. That's ok, since it gives a good warning to begin liquidating.

The move from D to E shows the index plummeting but the forecast has it trending marginally higher.

Finally, the move from E to F shows the index recovering but the forecast is flat.

In other words, the forecast works some times and doesn't some times. My view is it's better than nothing. Clearly, in a few thousand years, when sufficient samples are available, it may be worth trusting. But until then, keep in mind that it could be inaccurate.

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2012-2015 Weekly Forecast

Picture of Dow industrials forecast on the weekly scale.

This is the Dow on the weekly scale, showing as much as I can fit on the chart.

It shows 2012 with a low in October and a nice climb from there, going into 2015. Clearly 2012 is not going to be a pleasant year but 2013 is going to be a buy-and-holder's dream!

At least for a year... 2014 shows price going nowhere except down slightly until near year end with a resumption of the move up starting in October and continuing to the end of 2015.

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2012-2021 Monthly Forecast

Picture of Dow industrials forecast on the monthly scale.

This is the Dow forecast made on 11/27/2011 using the monthly scale and it covers a decade of price movement.

We see the same bottom in 2012 before a rise up to 2017. In the summer of 2017, the Dow peaks and begins what looks to be a bear market going into 2019. Then we get a bounce up which lasts until mid 2021.

When you think of the current economic picture and world events, this forecast is plausible. 2012 is going to be a rocky year just as this year has been.

After that, the world's economies should get in gear and the stock market should reflect that by posting higher highs.

If this forecast is correct, buy and hold should consider taking profits in early 2012 and then buy back in October with the potential to hold on to 2017.

-- Thomas Bulkowski

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See Also

  • Best buy days. Which day of the week is the best one to buy or sell?
  • Best buy months. Can buying at the end of the worst performing month and selling at the best performing be profitable?
  • Holidays. Does the market rise or fall before and after holidays? Answer: Fall.
  • Seasonality. What are the best months to buy and sell stocks?
  • 1-2-3 trend change. How do you know when the price trend is changing?

Written by and copyright © 2005-2018 by Thomas N. Bulkowski. All rights reserved. Disclaimer: You alone are responsible for your investment decisions. See Privacy/Disclaimer for more information. Some pattern names are the registered trademarks of their respective owners. A closed mouth gathers no foot.