As of 06/14/2024
Indus: 38,589 57.94 0.1%
Trans: 14,807 161.34 1.1%
Utils: 917 0.41 0.0%
Nasdaq: 17,689 +21.32 +0.1%
S&P 500: 5,432 2.14 0.0%

YTD
+2.4%
6.9%
+4.0%
+17.8%
+13.9%

39,900 or 37,650 by 07/01/2024
15,800 or 14,300 by 07/01/2024
960 or 890 by 07/01/2024
17,800 or 16,750 by 07/01/2024
5,500 or 5,250 by 07/01/2024

As of 06/14/2024
Indus: 38,589 57.94 0.1%
Trans: 14,807 161.34 1.1%
Utils: 917 0.41 0.0%
Nasdaq: 17,689 +21.32 +0.1%
S&P 500: 5,432 2.14 0.0%

YTD
+2.4%
6.9%
+4.0%
+17.8%
+13.9%
 
39,900 or 37,650 by 07/01/2024
15,800 or 14,300 by 07/01/2024
960 or 890 by 07/01/2024
17,800 or 16,750 by 07/01/2024
5,500 or 5,250 by 07/01/2024
 
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: 2/8/2024.
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 10year 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.
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.
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.
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.
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).
Date  Close  Comments 
12/31/1936  179.90  Note: First trading day is 1/4/1937 
1/2/1947  176.39  
1/2/1957  496.03  
12/30/1966  785.69  First trading day is 1/3/1967 
12/31/1976  1004.65  First trading day is 1/3/1977 
1/2/1987  1,927.31  
1/2/1997  6,442.49  
12/29/2006  12,463.15  First trading day is 1/3/2007 
2,934.45  Average 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 shows what I have.
When no trading occurred on the target date (such as the holiday on January 1), 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.
Date  Close  Comments 
12/31/1936  179.90  Note: First trading day is 1/4/1937. Use prior close 
1/3/1947  176.76  
1/3/1957  499.20  
1/3/1967  786.41  
1/3/1977  999.75  
1/2/1987  1,927.31  1/3/87 is weekend. Prior close is this one 
1/3/1997  6,544.09  
1/3/2007  12,474.52  
1/3/2017  Not used: We're trying to predict it  
2,948.49  Average 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).
 Thomas Bulkowski
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