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Macbee Planet,Inc.(7095) Summary

7095
TSE Prime
Macbee Planet,Inc.
1,446
JPY
-20
(-1.36%)
Jan 29, 1:37 pm JST
9.44
USD
Jan 28, 11:37 pm EST
Result
PTS
outside of trading hours
1,446
Jan 29, 1:24 pm JST
Summary Chart Historical News Financial Result
PER
8.0
PBR
1.53
Yield
3.80%
Margin Trading Ratio
591.50
Stock Price
Jan 29, 2026
Opening Jan 29, 9:00 am
1,465 JPY 9.56 USD
Previous Close Jan 28
1,466 JPY 9.60 USD
High Jan 29, 9:00 am
1,470 JPY 9.60 USD
Low Jan 29, 10:10 am
1,430 JPY 9.33 USD
Volume
134,000
Trading Value
0.19B JPY 1.26M USD
VWAP
1443.68 JPY 9.43 USD
Minimum Trading Value
144,600 JPY 944 USD
Market Cap
0.02T JPY 0.14B USD
Number of Trades
517
Liquidity & Number of Trades
As of Jan 29, 2026
Liquidity
Slightly High
1-Year Average
664
1-Year High Jun 13, 2025
5,686
Margin Trading
Date Short Interest Long Margin Positions Ratio
Jan 23, 2026 0 1,003,500
Jan 16, 2026 0 991,500
Jan 9, 2026 0 1,006,100
Dec 26, 2025 0 995,100
Dec 19, 2025 0 1,079,000
Company Profile
Macbee Planet, Inc. specializes in marketing support with a focus on LTV (Lifetime Value) prediction. The company primarily operates on a performance-based fee model.
Sector
Services
Macbee Planet, Inc. engages in marketing support services, leveraging its strength in LTV (Lifetime Value) prediction. The company assists businesses aiming to enhance their sales promotion, customer acquisition, and brand awareness through internet-based strategies. By utilizing data analysis platforms and web hospitality tools, Macbee Planet optimizes cost-effectiveness for its clients. The company primarily offers new user acquisition support on a performance-based fee model, providing services such as centralized management of web advertisements, selection of appropriate ad placements, improvement of landing page traffic, strategy planning, and operational support. In addition to affiliate and listing advertisements, Macbee Planet also incorporates offline advertising. The company implements effective marketing strategies by employing proprietary AI technology for user behavior and LTV predictions.