Omnicom Group Inc. (OMC)
omnicom group is the leading global marketing communications company providing the best talent, creativity and digital innovation in the world of integrated marketing services for more than 200 brands - including some of the world’s most iconic, innovative and successful. about: omnicom group (nyse: omc) is a strategic holding company that has the industry’s most diverse portfolio of more than1,000 companies serving over 5,000 clients in more than 100 countries. omnicom and its agencies have a corporate culture distinguished by a long-standing belief in the power of learning and training. the company offers the industry’s richest opportunities for individual growth, with offerings that include omnicom university, catalyst at ddb, bbdo university, tiger academy at tbwa, ketchum university, omd foundations, and more - all contributing to enhancing employees’ professional breadth and our success in retaining top talent. year after year, omnicom brands win the lion’s share of industry awar
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 25.46% per annum.
- OMC price at the close of December 8, 2023 was $81.94 and was lower than the bottom border of the primary price channel by $18.97 (18.80%). This indicates a possible reversal in the primary trend direction.
- The secondary trend is decreasing.
- The decline rate of the secondary trend is 5.68% per annum.
- OMC price at the close of December 8, 2023 was higher than the top border of the secondary price channel by $5.34 (6.97%). This indicates a possible reversal in the secondary trend direction.
- 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 OMC 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
=
March 9, 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()
= $
May 9, 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()
= $
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
=
February 5, 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 21, 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()
= $