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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 , the Dow was below Today it's over 24, Leaving off the 24, 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 which I discard since it's not a full year.
Williams appears to use data going back to 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 That may not sound like much but it's huge when you are dealing with numbers that range so widely. In some cases, I removed the bear market influence on the predictions.
By that, I mean if I was looking for 's prediction, I'd skip any data from 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:. 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 to bear market, I'll adjust the values to compensate for the missing years and I remove the entire , , and 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. 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, when later predictions are all in the 24, range. 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. 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. Because the weekly scale uses Friday's close and the monthly scale uses the last trading day of the month.
Suppose we want to make a prediction for The data I have goes back to late , but we start with , the first full year of data ending with a 7. The table on the right shows what I have. The average of those numbers is 2, That's far away from the current Dow's close of 24, 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 is 19, If you multiply each predicted price for the remainder of the year by 6.
So the first plotted point would be 2, 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. Notice that the market remains weak until late February or early March when it bottoms.
After that, the Dow makes a nice bullish run. If the prediction is correct, then you should start to bottom fish begin buying in late February. This chart shows weakness in followed by nice upward run to early A little turbulence sets in for about a year until mid Then it's up, up, and away until notice a weak year happens a decade after Year - Name Date Price Basic Amount. Year - Increased Price Basic Amount.
Month M01 - M12 Quality of the statistics Quality Declaration pdf pdf pdf pdf pdf. CPI, Historical numbers, —. CPI, annual changes Inflation Rate. Inflation rate according to CPI. Inflation in Sweden — Price level in Sweden — CPI at constant taxes, monthly changes.
CPI at constant taxes, annual changes. Inflation rate according to HICP. CPIF at constant taxes, annual changes. CPIF at constant taxes, monthly changes. Net Price Index, annual changes, percent.
Increased price basic amount. Month M01 - M Consumer Price Index exclusive energy with fixed interest rate. Month M02 - M Month M12 - M Increased Price Basic Amount. Month M05 - M Consumer price index for December Consumer price index for November Consumer price index for October Consumer price index for September Consumer price index for August