LGMS Berhad (0249) Fair Value & Analysis
Technology · MY · Market cap 219M MYR
Fair value as of: Jun 24, 2026
Analysis
LGMS Berhad (0249) currently trades at 0.4500 MYR, while our model-based Fair Value estimate is 0.4500 MYR — implying the stock looks roughly 0.0% undervalued today. We read business quality at 95/100 (high quality), in the Technology sector. Bull case: trading below our estimate, it may offer upside if the fundamentals hold. Bear case: a low price can be a value trap when quality is weak or the data is thin (evidence: high) — always confirm before acting.
About the company
LGMS Berhad provides cybersecurity services in Malaysia and internationally. It operates through Cyber Risk Prevention, Cyber Risk Management and Compliance, and Cyber Threat and Incident Response segments. The company offers penetration testing services, including vulnerability assessment and penetration testing, web application and services penetration testing, mobile application penetration testing, source code review, wireless network penetration testing, CREST penetration testing, red team engagement, intelligence LED penetration testing, social engineering with physical and logical attack, cyber drill stimulation, and IoT device and SST security assessments. It also provides forensics services, such as digital forensics and computer crime investigations; cyber security incident response; and compromise assessment. In addition, the company offers compliance services, such as PCI DSS consulting; PCI DSS official ASV scanning; ISO 27001:2022 consultancy; cyber security governance…
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Frequently asked questions
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How we calculate Fair Value
Each company is valued through a stack of independent intrinsic-value models (DCF variants, residual-income, multiples and more), blended into one family-balanced consensus and weighted by how much trustworthy data backs it. A separate quality layer scores the fundamentals. Every input is real reported data — nothing guessed.
Educational research only · not financial advice · no buy/sell recommendation. Model-based estimates are not certainties; their reliability depends on data quality and assumptions.