# Wolverine World Wide, Inc. (WWW)

founded in 1883 on the belief in the possibility of opportunity, wolverine world wide, inc. (nyse: www) is one of the world’s leading marketers and licensors of branded casual, active lifestyle, work, outdoor sport, athletic, children's and uniform footwear and apparel. through a diverse portfolio of highly recognized brands, our products are designed to empower, engage and inspire our consumers every step of the way. the company’s portfolio includes merrell®, sperry®, hush puppies®, saucony®, wolverine®, keds®, stride rite®, chaco®, bates®, and hytest®. wolverine worldwide is also the global footwear licensee of the popular brands cat® and harley-davidson®. based in rockford, michigan, for more than 130 years, our products are carried by leading retailers in the u.s. and globally in approximately 170 countries and territories.

## 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 43.68% per annum.
- WWW price at the close of March 1, 2024 was $10.15 and was inside the primary price channel.
- The secondary trend is increasing.
- The growth rate of the secondary trend is 27.43% per annum.
- WWW price at the close of March 1, 2024 was higher than the top border of the secondary price channel by $0.02 (0.16%).
- 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:

Y_{i} = α + β × X_{i} + ε_{i}

Top border of price channel:

Exp(Y_{i}) = Exp(a + b × X_{i} + 2 × s)

Bottom border of price channel:

Exp(Y_{i}) = Exp(a + b × X_{i} – 2 × s)

where:

i - observation number

Y_{i} - natural logarithm of WWW price

X_{i} - time index, 1 day interval

σ - standard deviation of ε_{i}

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 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()

= $

#### February 21, 2024 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()

= $

#### Description

- The primary trend is decreasing.
- The decline rate of the primary trend is 43.68% per annum.
- WWW price at the close of March 1, 2024 was $10.15 and was inside the primary price channel.

### 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

=

#### August 14, 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()

= $

#### March 1, 2024 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()

= $

#### Description

- The secondary trend is increasing.
- The growth rate of the secondary trend is 27.43% per annum.
- WWW price at the close of March 1, 2024 was higher than the top border of the secondary price channel by $0.02 (0.16%).