ANSYS, Inc. (ANSS)
founded in 1970, ansys employs more than 2,900 professionals, and many of them are engineers expert in fields such as finite element analysis, computational fluid dynamics, electronics and electromagnetics, and design optimization. our staff includes more master’s and ph.d.-level engineers than any other simulation provider. ansys is passionate about pushing the limits of world-class technology, all so our customers can turn their design concepts into successful, innovative products. the company has been recognized as one of the world's most innovative and fastest -growing companies by prestigious organizations including businessweek and fortune magazines. over the years, our steady growth and financial strength reflect our commitment to innovation and r&d. we reinvest 15 percent of our revenues each year into research to continually refine our software. we are listed on the nasdaq stock exchange.
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 15.01% per annum.
- ANSS price at the close of November 28, 2023 was $294.62 and was higher than the top border of the primary price channel by $8.61 (3.01%). This indicates a possible reversal in the primary trend direction.
- The secondary trend is increasing.
- The growth rate of the secondary trend is 27.21% per annum.
- ANSS price at the close of November 28, 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 ANSS 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
=
July 30, 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()
= $
February 22, 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
=
May 6, 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()
= $
October 17, 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()
= $