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PKSHA Technology Inc.(3993) Summary

3993
TSE Prime
PKSHA Technology Inc.
3,295
JPY
-20
(-0.60%)
Apr 30, 9:37 am JST
20.57
USD
Apr 29, 8:37 pm EDT
Result
PTS
outside of trading hours
3,301.5
Apr 30, 9:34 am JST
Summary Chart Historical News Financial Result
PER
35.8
PBR
2.89
Yield
ー%
Margin Trading Ratio
4.37
Stock Price
Apr 30, 2026
Opening Apr 30, 9:00 am
3,275 JPY 20.44 USD
Previous Close Apr 28
3,315 JPY 20.82 USD
High Apr 30, 9:03 am
3,320 JPY 20.73 USD
Low Apr 30, 9:00 am
3,275 JPY 20.44 USD
Volume
41,200
Trading Value
0.14B JPY 0.85M USD
VWAP
3295.07 JPY 20.57 USD
Minimum Trading Value
329,500 JPY 2,057 USD
Market Cap
0.11T JPY 0.66B USD
Number of Trades
116
Liquidity & Number of Trades
As of Apr 30, 2026
Liquidity
High
1-Year Average
1,219
1-Year High Aug 15, 2025
4,063
Margin Trading
Date Short Interest Long Margin Positions Ratio
Apr 24, 2026 376,100 1,632,300 4.34
Apr 17, 2026 340,800 1,649,400 4.84
Apr 10, 2026 349,300 1,723,400 4.93
Apr 3, 2026 373,300 1,740,700 4.66
Mar 27, 2026 381,000 1,762,400 4.63
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.