Pearson's R for ForksI was wondering if there was a way to calculate Pearson's R for pitchforks like it's done with a regression trend, and if so: is there a way to implemented it already on TV.
Pearsonscoefficient
Alteryx: Worthy Competitor in Analytic Process AutomationIntroduction
Alteryx "AYX" is another tech software stock that has been on fire after the initial market dip from Covid-19. The company’s primary focus is enterprise-grade Analytic Process Automation (APA) platform and accomplishes this feat extremely well. I have had the opportunity to try a demo when looking for data analysis software for my organization. The software is amazing! Forget Tableau, SAS, and the others - Alteryx's platform (after the initial installation) can process “dirty data" (no data preparation) and provide business insights from the automated analysis. There is going to be a lot less “Data Scientists” and even “BI Analysts” around.
The cost of the platform runs something around $70,000. Yet from a business standpoint, it is much cheaper than adding an extra data analyst IT personnel whose value was dubious at best. I can see why this company has taken flight. I would call it the Salesforce “CRM” (Salesforce is a partner as well as Microsoft) of Business Data Analysis.The company has few large or established competitors (excluding Tableau). I would caution that until they themselves have established more dominance in the field of APA – it would not be difficult for the likes of IBM (with SPSS) to jump into the ring. However, this would seem unlikely considering IBM's struggles as of late.
AYX Performance
Note: You will come to notice I only use a few technical indicators in my analyses. I would much rather focus on the quantitative aspects that are rooted in strong mathematical theory, than try to quantify human behavior. Additionally, I have been including the acid test – Quick Ratio – to ensure that companies have enough liquidity to make it through any additional “stay-at-home” orders (hopefully we will not have any more serious outbreaks).
Fundamentals
We see that AYX has a Quick Ratio of 4.1123 and a ratio of 1.00 is considered satisfactory to meet all debt obligations. QR = (CE + MS + AR)/CL, where; CE = Cash + Equivalents, MS = Marketable Securities, AR = Accounts Receivable, and CL = Current Liabilities. The information is given in simple terms in a company’s SEC Form Q-10 Filing or the 10-K for the fiscal year.
AYX has a Price-to-Earnings Ratio that is absolutely through the roof at 2418.9831. This may be a cause for concern. Alteryx IS a tech company during a tremendous growth spurt and recent earnings have been all over the map due to Covid-19. So, we will give it the benefit of the doubt.
Technicals:
I use the Exponential Moving Averages (EMAs) as they are better indicators of what is happening “today” as they are exponentially weighted to the most recent dates of the chosen time frame.
Exponential Moving Averages
Price(Close) = 148.71
EMA(10) = 147.89
EMA(20) = 129.99
EMA(50) = 121.24
These all look good and in the correct order. The price is greater than all three (3) EMAs and there is no “cross-unders” of the EMA averages.
EMA Trend Analysis = Bullish
Linear Regression and Reversion to the Mean
The calculated Pearson’s r coefficient is almost perfectly correlated at approximately 0.9736 (A Pearson’s r coefficient of 1.00 is a perfect correlation).
We can interpret this as a Bullish trend as 1) the slope of the line is upward and 2) the price is on the lower-end of the linear regression channel (and will move toward the mean or reversion to the mean).
Linear Regression Trend Analysis = Bullish
Thanks for your time and feel free to leave any comments below!
Position: 50 shares @ 118.26 Long
APPLE LONG: $151-$182 - CYCLICAL ANALYSIS & REGRESSION FORECASTAnalysing 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.
