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PKSHA Technology Inc.(3993) Summary

3993
TSE Prime
PKSHA Technology Inc.
3,885
JPY
-25
(-0.64%)
Jan 7, 3:30 pm JST
24.62
USD
Jan 7, 1:30 am EST
Result
PTS
outside of trading hours
Summary Chart Historical News Financial Result
PER
46.3
PBR
3.74
Yield
ー%
Margin Trading Ratio
4.37
Stock Price
Jan 7, 2025
Opening Jan 7, 9:00 am
3,900 JPY 24.72 USD
Previous Close Jan 6
3,910 JPY 24.79 USD
High Jan 7, 1:04 pm
3,920 JPY 24.78 USD
Low Jan 7, 9:27 am
3,830 JPY 24.22 USD
Volume
442,200
Trading Value
1.72B JPY 0.01B USD
VWAP
3882.14 JPY 24.61 USD
Minimum Trading Value
388,500 JPY 2,462 USD
Market Cap
0.12T JPY 0.79B USD
Number of Trades
1,145
Liquidity & Number of Trades
As of Jan 7, 2025
Liquidity
High
1-Year Average
1,511
1-Year High Nov 15, 2024
5,790
Margin Trading
Date Short Interest Long Margin Positions Ratio
Dec 27, 2024 405,900 1,789,000 4.41
Dec 20, 2024 426,300 1,793,300 4.21
Dec 13, 2024 499,100 1,806,700 3.62
Dec 6, 2024 540,000 1,731,600 3.21
Nov 29, 2024 562,700 1,654,600 2.94
Company Profile
PKSHA Technology Inc. is an AI venture company focusing on deep learning technology development, including image recognition and automated dialogue. The company collaborates with Toyota.
Sector
Information & Communication
PKSHA Technology Inc. develops algorithm modules centered on machine learning, natural language processing, and deep learning as an AI venture. Utilizing these technologies, the company supports client businesses in improving operational efficiency and enhancing added value. In its AI Research & Solution business, PKSHA Technology conducts comprehensive services from joint research and development to solution provision with partner companies. The AI SaaS business offers products such as automated response engines, FAQ systems, and RPA software, enabling automation and efficiency in corporate customer touchpoints and internal operations. Through partnerships with industry-leading companies, PKSHA Technology develops high-quality software. The company achieves high retention rates and profitability through accuracy improvements based on data feedback and a monthly subscription model.