Tube Investments of India Limited (TIINDIA) Fair Value & Analysis
Industrials · IN · Market cap ₹606B
Analysis
Tube Investments of India Limited (TIINDIA) currently trades at ₹3,258, while our model-based Fair Value estimate is ₹559.32 — implying the stock looks roughly 82.8% overvalued today. We read business quality at 82/100 (high quality), in the Industrials sector. Bear case: priced above our estimate, the market already discounts strong expectations. Bull case: above-average quality can justify a premium — the entry price still matters most (evidence: medium).
About the company
Tube Investments of India Limited, together with its subsidiaries, engages in the manufacture and sale of precision engineered and metal formed products to automotive, railway, construction, agriculture, etc. in India. It operates through eight segments: Engineering, Metal Formed Products, Mobility, Gear and Gear Products, Power Systems, Industrial Systems, Medical, and Semiconductors. The Engineering segment offers cold rolled steel strips, precision steel tubes, cold drawn welded tubes, electric resistance welded tubes, and tubular components. The Metal Formed Products segments provides automotive chains, fine blanked and stamped products, roll-formed car doorframes, and cold rolled formed sections. The Mobility segment offers standard bicycles; special bicycles, including alloy bikes; specialty performance bikes; fitness equipment; and electric bicycles, as well as cycling accessories and spare parts. The Gear and Gear Products segment provides gears, gear boxes, gear motors, and…
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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.