Stock Price Trends

Frontdoor, Inc. (FTDR)

frontdoor is a company that’s on a mission to make home ownership simple. we have the culture and drive of a billion-dollar startup, and are committed to transforming the home services industry. our company more than 50 years of unmatched expertise as the nation’s largest provider of home service plans. last year, we responded to more than four million service requests from customers who depend on us for financial protection and professional repairs when a covered item breaks down. at frontdoor, we wake up every day and obsess about how to take the hassle out of our customer’s lives. we’re inquisitive and innovative, never satisfied with the status quo. we solve problems and make lives better, and enjoy what we do. our leadership team has a dynamic mix of experience in disrupting industries, changing how consumers receive products and services, creating leading digital platforms, and ensuring a world-class customer experience. they don’t just talk about culture, they live it, and it's

Stock Price Trends

Stock price trends estimated using linear regression.

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Key facts

  • The primary trend is increasing.
  • The growth rate of the primary trend is 68.86% per annum.
  • FTDR price at the close of November 28, 2023 was $34.33 and was inside the primary price channel.
  • The secondary trend is decreasing.
  • The decline rate of the secondary trend is 7.56% per annum.
  • FTDR 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 FTDR 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
=

August 26, 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()
= $

September 12, 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 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()
= $

November 2, 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()
= $