Stock Price Trends

Waste Management, Inc. (WM)

waste management is north america's leading provider of integrated environmental solutions. we partner with our customers and communities to manage and reduce waste from collection to disposal while recovering valuable resources and creating clean, renewable energy. we are on a quest for environmental performance, a mission to maximize resource value, while minimizing – and even eliminating – environmental impact so that both our economy and our environment can thrive. waste management tailors its services to meet the needs of each customer group and to ensure consistent, superior service at the local level. headquartered in houston, the company serves nearly 20 million municipal, commercial, industrial, and residential customers through a network of 367 collection operations, 355 transfer stations, 273 active landfill disposal sites, 16 waste-to-energy plants, 104 recycling plants, and 111 beneficial-use landfill gas projects.

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 increasing.
  • The growth rate of the primary trend is 2.44% per annum.
  • WM price at the close of November 28, 2023 was $169.68 and was inside the primary price channel.
  • The secondary trend is increasing.
  • The growth rate of the secondary trend is 130.12% per annum.
  • WM price at the close of November 28, 2023 was lower than the bottom border of the secondary price channel by $0.18 (0.10%). This indicates a possible reversal in the secondary trend direction.

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 WM 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, 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 27, 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 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()
= $