Stock Price Trends

Digital Turbine, Inc. (APPS)

digital turbine (nasdaq: apps) aims to deliver the right app to the right person at the right time, around the world. we provide products and solutions for mobile operators and oems, app advertisers, and mobile publishers, to make the most of the app ecosystem. our app marketing solutions enable brands and app developers to acquire users and promote their brands, while giving mobile content publishers an efficient means of monetization. while our mobile operator and oem products enable white-label billing and content marketplaces as well as monetization solutions through app recommendations. digital turbine has delivered more than 150 million app installs for hundreds of advertisers, and more than 31 million customers use our solutions each month across more than 20 global operators. digital turbine is headquartered in austin, texas with global offices in durham, berlin, san francisco, singapore, sydney and tel aviv. for additional information visit www.digitalturbine.com. follow us on

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 64.49% per annum.
  • APPS price at the close of March 1, 2024 was $3.21 and was inside the primary price channel.
  • The secondary trend is decreasing.
  • The decline rate of the secondary trend is 99.14% per annum.
  • APPS price at the close of March 1, 2024 was inside the secondary price channel.

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 APPS 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
=

February 4, 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()
= $

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 primary trend is decreasing.
  • The decline rate of the primary trend is 64.49% per annum.
  • APPS price at the close of March 1, 2024 was $3.21 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
=

December 26, 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 decreasing.
  • The decline rate of the secondary trend is 99.14% per annum.
  • APPS price at the close of March 1, 2024 was inside the secondary price channel.