-
1 Comment
Plains GP Holdings, L.P is currently in a long term uptrend where the price is trading 21.3% above its 200 day moving average.
From a valuation standpoint, the stock is 98.8% cheaper than other stocks from the Energy sector with a price to sales ratio of 0.1.
Plains GP Holdings, L.P's total revenue sank by 34.9% to $6B since the same quarter in the previous year.
Its net income has dropped by 141.7% to $-20M since the same quarter in the previous year.
Finally, its free cash flow fell by 73.1% to $125M since the same quarter in the previous year.
Based on the above factors, Plains GP Holdings, L.P gets an overall score of 2/5.
Exchange | NASDAQ |
---|---|
CurrencyCode | USD |
Sector | Energy |
Industry | Oil & Gas Midstream |
ISIN | US72651A1088 |
Beta | 1.09 |
---|---|
Target Price | 21.7857 |
Dividend Yield | 7.8% |
Market Cap | 5B |
PE Ratio | 37.29 |
Plains GP Holdings, L.P., through its subsidiary, Plains All American Pipeline, L.P., owns and operates midstream infrastructure systems in the United States and Canada. The company operates in two segments, Crude Oil and Natural Gas Liquids (NGLs). It engages in the gathering and transporting crude oil using pipelines, trucks, and barges or railcars, as well as providing terminalling, storage, and other related services. The company is also involved in the natural gas processing and NGL fractionation, storage, transportation, and terminalling activities. PAA GP Holdings LLC operates as a general partner of the company. Plains GP Holdings, L.P. was incorporated in 2013 and is headquartered in Houston, Texas.
Learn MoreHere's how to backtest a trading strategy or backtest a portfolio for PAGP 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