# Mercury General Corporation (MCY)

when it comes to choosing an insurance company, we know consumers want the lowest price possible. but they also want the appropriate amount of coverage to keep their family safe. why should they have to sacrifice one over the other? with mercury, we believe in having the best of both worlds. low rates, excellent coverage and a local agent who’s there every step of the way. that’s what sets mercury apart from our competitors. and that’s what makes us the obvious choice when it comes to insurance. since we first opened our doors in 1962, we’ve provided comprehensive coverage options ranging from personal auto insurance to homeowners insurance to mechanical breakdown protection. dedicated managers and enthusiastic employees work hand-in-hand with our network of independent agents to make mercury one of the fastest-growing auto insurers in the nation and the leading insurer in california. the momentum is building. and the good news is we have no plans to stop.

## Stock Price Trends

Stock price trends estimated using linear regression.

## Paying users area

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Unhide data and trends.

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#### Key facts

• The primary trend is decreasing.
• The decline rate of the primary trend is 30.75% per annum.
• MCY price at the close of November 28, 2023 was \$36.70 and was higher than the top border of the primary price channel by \$6.14 (20.09%). This indicates a possible reversal in the primary trend direction.
• The secondary trend is decreasing.
• The decline rate of the secondary trend is 7.19% per annum.
• MCY price at the close of November 28, 2023 was higher than the top border of the secondary price channel by \$0.70 (1.95%). 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 MCY 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
=

Exp(4 × s) – 1
= Exp(4 × ) – 1
=

#### March 1, 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()
= \$

#### October 13, 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
=

Exp(4 × s) – 1
= Exp(4 × ) – 1
=

#### August 3, 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()
= \$