Stock Price Trends

Travel + Leisure Co. (TNL)

travel + leisure co. is the world’s leading membership and leisure travel company, with nearly 20 travel brands across its resort, travel club, and lifestyle portfolio. the company provides outstanding vacation experiences and travel inspiration to millions of owners, members, and subscribers every year through its products and services: wyndham destinations, the largest vacation ownership company with more than 245 vacation club resort locations across the globe; panorama, the world’s foremost membership travel business that includes the largest vacation exchange company, and subscription travel brands; and travel + leisure group, featuring top travel content and travel services including the brand’s eponymous travel club. at travel + leisure co., our global team of associates brings hospitality to millions each year, turning vacation inspiration into exceptional travel experiences. we put the world on vacation. learn more at travelandleisureco.com.

Stock Price Trends

Stock price trends estimated using linear regression.

Paying users area

The data is hidden behind and trends are not shown in the charts.
Unhide data and trends.

Get full access to the entire website.

This is a one-time payment. There is no automatic renewal.

Key facts

  • The primary trend is decreasing.
  • The decline rate of the primary trend is 20.08% per annum.
  • TNL price at the close of November 28, 2023 was $36.93 and was inside the primary price channel.
  • The secondary trend is decreasing.
  • The decline rate of the secondary trend is 2.86% per annum.
  • TNL price at the close of November 28, 2023 was inside the secondary price channel.

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 TNL 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
=

February 23, 2021 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 28, 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
=

September 16, 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()
= $

November 28, 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()
= $