APPLE LONG: $151-$182 - CYCLICAL ANALYSIS & REGRESSION FORECAST
Analysing Apple's (AAPL) historical cyclical price movements and using the +/- 2SD of the linear regression to forecast a naive regression price for the next extension phase.
* Extension leg Regression Forecast*
1. For leg A (Extension Leg 1) we use a start point of $12.5 or $33 (phase doesnt have a clear start), or we could assume a mean value of (12.5+33)/2= $22.75.
- Leg A is then, $12.5, $23.5 or $33 divided by $100, which means Leg A is a price increase of = 700%, 310% or 200%
2. For Leg C (Extension Leg 2) the price increased from $55 to $134.5 which is a 145% increase.
3. For Foretasted Leg E (Extension Leg 3), we start at $89 and we derive the price "%" increase by:
- Using the regression of the price increase % from Leg A to Leg C e.g. 145%/700%= 21%; 145/310 = 46%; 145/200= 73%, so this means for each of the calculations we can then assume each is the regression growth differential from Leg C's 145% increase to foretasted Leg E's "%" increase
4. e.g. Foretasted Leg E/ Extension Leg 3:
21% of 145% = 31% increase; $89 * 31% = $117
or 46% of 145% = 67% increase ; $89* 67% * $89= $151
or 73% of 145% = 106% increase; $89 * 106% = $182
- Thus Apples Leg E/ Extension Leg 3's Naive Regression Forecast = between $151 and $182
* As shown on graph.
Furthermore, another interesting statistical measure for apples 10year/ 120 Month +/-2SD channel was that the Pearsons R was 0.95. This means that the linear correlation between Apples Price over the measured time period was 95%. 95% of all values observed lie averagely on its linear regression line (middle line of the Stan Dev channel) - en.wikipedia.org/wiki/Pearson_product-moment_correlation_coefficient
- Having such a high Peasons R means the regression line holds true for 95% of past data and therefore is MAY also include 95% of future data thus extrapolating the linear line (or using basic regressions as i have done) is of some statistical significance.
A Pearsons R coefficient of 0.3 means there is little positive correlation between Price and Time, thus extrapolating prices through time using basic regressions/ forecasts is much less statistically prudent, since only 30% of past data correlated about the linear regression line.
* Extension leg Regression Forecast*
1. For leg A (Extension Leg 1) we use a start point of $12.5 or $33 (phase doesnt have a clear start), or we could assume a mean value of (12.5+33)/2= $22.75.
- Leg A is then, $12.5, $23.5 or $33 divided by $100, which means Leg A is a price increase of = 700%, 310% or 200%
2. For Leg C (Extension Leg 2) the price increased from $55 to $134.5 which is a 145% increase.
3. For Foretasted Leg E (Extension Leg 3), we start at $89 and we derive the price "%" increase by:
- Using the regression of the price increase % from Leg A to Leg C e.g. 145%/700%= 21%; 145/310 = 46%; 145/200= 73%, so this means for each of the calculations we can then assume each is the regression growth differential from Leg C's 145% increase to foretasted Leg E's "%" increase
4. e.g. Foretasted Leg E/ Extension Leg 3:
21% of 145% = 31% increase; $89 * 31% = $117
or 46% of 145% = 67% increase ; $89* 67% * $89= $151
or 73% of 145% = 106% increase; $89 * 106% = $182
- Thus Apples Leg E/ Extension Leg 3's Naive Regression Forecast = between $151 and $182
* As shown on graph.
Furthermore, another interesting statistical measure for apples 10year/ 120 Month +/-2SD channel was that the Pearsons R was 0.95. This means that the linear correlation between Apples Price over the measured time period was 95%. 95% of all values observed lie averagely on its linear regression line (middle line of the Stan Dev channel) - en.wikipedia.org/wiki/Pearson_product-moment_correlation_coefficient
- Having such a high Peasons R means the regression line holds true for 95% of past data and therefore is MAY also include 95% of future data thus extrapolating the linear line (or using basic regressions as i have done) is of some statistical significance.
A Pearsons R coefficient of 0.3 means there is little positive correlation between Price and Time, thus extrapolating prices through time using basic regressions/ forecasts is much less statistically prudent, since only 30% of past data correlated about the linear regression line.
منشورات ذات صلة
إخلاء المسؤولية
لا يُقصد بالمعلومات والمنشورات أن تكون، أو تشكل، أي نصيحة مالية أو استثمارية أو تجارية أو أنواع أخرى من النصائح أو التوصيات المقدمة أو المعتمدة من TradingView. اقرأ المزيد في شروط الاستخدام.