Autodesk, Inc. (ADSK)
for more than 30 years, autodesk software has helped designers, engineers, visual artists, students and makers imagine, design and create a better world. advances in accessible 3d design and fabrication technology are disrupting the design, engineering and entertainment professions as we know them. as a result, autodesk is expanding beyond our design roots and applying our industry insights to usher in a new era of making things. over 100 million people use autodesk software like autocad, revit, maya, 3ds max, fusion 360, sketchbook, and more to unlock their creativity and solve important design, business and environmental challenges. our software runs on both personal computers and mobile devices and taps the infinite computing power of the cloud to help teams around the world collaborate, design, simulate and fabricate their ideas in 3d. if you’ve ever driven a high-performance car, admired a towering skyscraper, used a smartphone, or watched a great film, you’ve experienced what a
Stock Price Trends
Stock price trends estimated using linear regression.
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Key facts
- The primary trend is decreasing.
- The decline rate of the primary trend is 18.92% per annum.
- ADSK price at the close of December 8, 2023 was $224.01 and was higher than the top border of the primary price channel by $17.48 (8.46%). This indicates a possible reversal in the primary trend direction.
- The secondary trend is increasing.
- The growth rate of the secondary trend is 3.95% per annum.
- ADSK price at the close of December 8, 2023 was inside the secondary price channel.
- The direction of the secondary trend is opposite to the direction of the primary trend. This indicates a possible reversal in the direction of the primary trend.
Linear Regression Model
Model equation:
Yi = α + β × Xi + εi
Top border of price channel:
Exp(Yi) = Exp(a + b × Xi + 2 × s)
Bottom border of price channel:
Exp(Yi) = Exp(a + b × Xi – 2 × s)
where:
i - observation number
Yi - natural logarithm of ADSK price
Xi - time index, 1 day interval
σ - standard deviation of εi
a - estimator of α
b - estimator of β
s - estimator of σ
Exp() - calculates the exponent of e
Primary Trend
Start date:
End date:
a =
b =
s =
Annual growth rate:
Exp(365 × b) – 1
= Exp(365 × ) – 1
=
Price channel spread:
Exp(4 × s) – 1
= Exp(4 × ) – 1
=
November 25, 2020 calculations
Top border of price channel:
Exp(Y)
= Exp(a + b × X + 2 × s)
= Exp(a + b × + 2 × s)
= Exp( + × + 2 × )
= Exp()
= $
Bottom border of price channel:
Exp(Y)
= Exp(a + b × X – 2 × s)
= Exp(a + b × – 2 × s)
= Exp( + × – 2 × )
= Exp()
= $
July 10, 2023 calculations
Top border of price channel:
Exp(Y)
= Exp(a + b × X + 2 × s)
= Exp(a + b × + 2 × s)
= Exp( + × + 2 × )
= Exp()
= $
Bottom border of price channel:
Exp(Y)
= Exp(a + b × X – 2 × s)
= Exp(a + b × – 2 × s)
= Exp( + × – 2 × )
= Exp()
= $
Secondary Trend
Start date:
End date:
a =
b =
s =
Annual growth rate:
Exp(365 × b) – 1
= Exp(365 × ) – 1
=
Price channel spread:
Exp(4 × s) – 1
= Exp(4 × ) – 1
=
March 3, 2022 calculations
Top border of price channel:
Exp(Y)
= Exp(a + b × X + 2 × s)
= Exp(a + b × + 2 × s)
= Exp( + × + 2 × )
= Exp()
= $
Bottom border of price channel:
Exp(Y)
= Exp(a + b × X – 2 × s)
= Exp(a + b × – 2 × s)
= Exp( + × – 2 × )
= Exp()
= $
December 8, 2023 calculations
Top border of price channel:
Exp(Y)
= Exp(a + b × X + 2 × s)
= Exp(a + b × + 2 × s)
= Exp( + × + 2 × )
= Exp()
= $
Bottom border of price channel:
Exp(Y)
= Exp(a + b × X – 2 × s)
= Exp(a + b × – 2 × s)
= Exp( + × – 2 × )
= Exp()
= $