-
1 Comment
Kirkland's, Inc is currently in a long term downtrend where the price is trading 1.0% below its 200 day moving average.
From a valuation standpoint, the stock is 98.7% cheaper than other stocks from the Consumer Cyclical sector with a price to sales ratio of 0.8.
Kirkland's, Inc's total revenue sank by 6.9% to $195M since the same quarter in the previous year.
Its net income has increased by 520.7% to $21M since the same quarter in the previous year.
Finally, its free cash flow grew by 22.8% to $63M since the same quarter in the previous year.
Based on the above factors, Kirkland's, Inc gets an overall score of 3/5.
Sector | |
---|---|
Industry | |
Exchange | F |
CurrencyCode | EUR |
ISIN | None |
Market Cap | 221M |
---|---|
PE Ratio | 18.24 |
Target Price | 39.5 |
Dividend Yield | 0.0% |
Beta | 2.0 |
Kirkland's, Inc. operates as a specialty retailer of home décor in the United States. The company's stores provide various merchandise, including holiday décor, furniture, textiles, ornamental wall décor, decorative accessories, art, mirrors, fragrance and accessories, lamps, artificial floral products, housewares, outdoor living items, gifts, and frames. Its stores also offer an assortment of holiday merchandise in seasonal periods. The company operates its stores under the Kirkland's, Kirkland's Home, Kirkland's Home Outlet, Kirkland's Outlet, and The Kirkland Collection names. As of January 30, 2021, the company operated 373 stores in 35 states, as well as an e-commerce website, www.kirklands.com. Kirkland's, Inc. was founded in 1966 and is headquartered in Brentwood, Tennessee.
Learn MoreHere's how to backtest a trading strategy or backtest a portfolio for KI2.F 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