# Genpact Limited (G)

genpact (nyse: g) stands for “generating business impact.” we architect the lean digitalsm enterprise, our unique approach that reimagines our clients’ middle and back offices to generate growth, cost efficiency, and business agility for our clients. genpact’s hundreds of long-term clients include more than one-fourth of the fortune global 500. we have grown to over 70,000 people in 25 countries, with key management and a corporate office in new york city. we believe we are able to generate impact quickly and power intelligent operationssm for our clients because of our business domain expertise and experience running complex operations and making technology-enabled transformation sustainable. behind our passion for technology, process, and operational excellence is the heritage of a former general electric division that has served ge businesses since 1998. genpact’s annual revenues as of december 31, 2014 were $2.28 billion. genpact is an employer of choice and we are committed to

## 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 22.52% per annum.
- G price at the close of February 16, 2024 was $35.91 and was inside the primary price channel.
- The secondary trend is increasing.
- The growth rate of the secondary trend is 27.50% per annum.
- G price at the close of February 16, 2024 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:

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 G 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

=

#### July 27, 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()

= $

#### February 16, 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 22.52% per annum.
- G price at the close of February 16, 2024 was $35.91 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

=

#### October 24, 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()

= $

#### February 16, 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 27.50% per annum.
- G price at the close of February 16, 2024 was inside the secondary price channel.