Stock Price Trends

Northern Trust Corporation (NTRS)

northern trust corporation (nasdaq: ntrs) is a leading provider of wealth management, asset servicing, asset management and banking to corporations, institutions, affluent families and individuals. founded in chicago in 1889, northern trust has offices in the united states in 19 states and washington, d.c., and 20 international locations in canada, europe, the middle east and the asia-pacific region. as of september 30, 2015, northern trust corporation had: $120 billion in banking assets $6.0 trillion in assets under custody $887 billion in assets under management for over 125 years, northern trust has earned distinction as an industry leader for exceptional service, financial expertise, integrity and innovation. we seek qualified people for a wide range of challenging positions and who are interested in building a career with a global growth company, excel in a team oriented environment, yet respect others and have strong values and principles. the northern trust company is

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 25.30% per annum.
  • NTRS price at the close of November 28, 2023 was $76.70 and was inside the primary price channel.
  • The secondary trend is increasing.
  • The growth rate of the secondary trend is 705.93% per annum.
  • NTRS 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 NTRS 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
=

October 8, 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 16, 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
=

October 20, 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()
= $