-
1 Comment
P.A.M. Transportation Services, Inc is currently in a long term uptrend where the price is trading 1.1% above its 200 day moving average.
From a valuation standpoint, the stock is 98.5% cheaper than other stocks from the Industrials sector with a price to sales ratio of 0.8.
P.A.M. Transportation Services, Inc's total revenue rose by 15.6% to $143M since the same quarter in the previous year.
Its net income has increased by 202.3% to $14M since the same quarter in the previous year.
Finally, its free cash flow grew by 276.8% to $6M since the same quarter in the previous year.
Based on the above factors, P.A.M. Transportation Services, Inc gets an overall score of 5/5.
Sector | Industrials |
---|---|
Exchange | NASDAQ |
ISIN | US6931491061 |
Industry | Trucking |
CurrencyCode | USD |
Beta | 1.15 |
---|---|
Market Cap | 430M |
PE Ratio | None |
Target Price | 19 |
Dividend Yield | None |
P.A.M. Transportation Services, Inc., through its subsidiaries, operates as a truckload transportation and logistics company in the United States, Mexico, and Canada. The company operates a truckload dry van carrier that transports general commodities, such as automotive parts; expedited goods; consumer goods, such as general retail store merchandise; and manufactured goods, including heating and air conditioning units. The company also provides brokerage and logistics services. As of December 31, 2023, it operated a fleet of 2,200 trucks, which included 300 independent contractor trucks; and 8,567 trailers. The company was founded in 1980 and is headquartered in Tontitown, Arkansas.
Learn MoreHere's how to backtest a trading strategy or backtest a portfolio for PTSI 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