-
1 Comment
Sterling Bancorp, Inc. (Southfield, MI) is currently in a long term downtrend where the price is trading 5.3% below its 200 day moving average.
From a valuation standpoint, the stock is 71.3% cheaper than other stocks from the Financial Services sector with a price to sales ratio of 2.6.
Sterling Bancorp, Inc. (Southfield, MI)'s total revenue rose by 107.2% to $67M since the same quarter in the previous year.
Its net income has increased by 15.0% to $-12M since the same quarter in the previous year.
Finally, its free cash flow grew by 99.5% to $-13K since the same quarter in the previous year.
Based on the above factors, Sterling Bancorp, Inc. (Southfield, MI) gets an overall score of 4/5.
CurrencyCode | USD |
---|---|
ISIN | US85917W1027 |
Industry | Banks - Regional |
Sector | Financial Services |
Exchange | NASDAQ |
Beta | 0.54 |
---|---|
PE Ratio | 121.0 |
Target Price | 7 |
Dividend Yield | 0.0% |
Market Cap | 253M |
Sterling Bancorp, Inc. (Southfield, MI) operates as the unitary thrift holding company for Sterling Bank and Trust, F.S.B. that provides community banking services to individuals and businesses. The company offers checking, savings, money market, term certificate, and individual retirement accounts, as well as certificates of deposit. It also provides residential mortgage, commercial and industrial, commercial real estate, construction, and consumer loans, as well as commercial lines of credit and private banking services. The company was founded in 1984 and is headquartered in Southfield, Michigan.
Learn MoreHere's how to backtest a trading strategy or backtest a portfolio for SBT 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