-
1 Comment
GMB KOREA Corp is currently in a long term downtrend where the price is trading 9.0% below its 200 day moving average.
From a valuation standpoint, the stock is 79.6% cheaper than other stocks from the Consumer Cyclical sector with a price to sales ratio of 0.3.
GMB KOREA Corp's total revenue sank by 1.3% to $131B since the same quarter in the previous year.
Its net income has dropped by 4.2% to $2B since the same quarter in the previous year.
Finally, its free cash flow fell by 147.5% to $-6B since the same quarter in the previous year.
Based on the above factors, GMB KOREA Corp gets an overall score of 1/5.
Industry | Auto Parts |
---|---|
Sector | Consumer Cyclical |
Exchange | KO |
CurrencyCode | KRW |
ISIN | KR7013870001 |
PE Ratio | None |
---|---|
Market Cap | 77B |
Target Price | None |
Beta | 0.92 |
Dividend Yield | 6.3% |
GMB Korea Corp. manufactures and sells automotive parts in South Korea and internationally. The company offers engine parts, including water pumps, viscous fan clutches, tensioner bearing assemblies, idler bearing assemblies, sockets, and oil pumps; and auto transmission parts, such as spool-valves, manual control shafts, hydraulic pressure pistons, pinion shafts/differential pins, and retainer-U/d brakes. It also provides chassis parts comprising universal joints, tripod joints, steering joints, and cages; bearing parts consisting of ball, water pump, and clutch release bearings; and green energy parts, including electric water pumps, integrates thermal management modules, and valve pump modules. The company was founded in 1979 and is headquartered in Changwon-si, South Korea.
Learn MoreHere's how to backtest a trading strategy or backtest a portfolio for 013870.KO using our backtest tool. PyInvesting provides the backtesting software for you to backtest your investment strategy. Our backtest software is written using Python code and allows you to backtest stock, backtest etf, backtest options, backtest crypto and backtest forex online. Our backtesting Python framework is highly robust and gives you a realistic simulation of how your strategy would have performed in the past using backtest data.
© PyInvesting 2025