$10.200
-0.089 (-0.87%)Al cierre
- High
- 10.385
- Open
- 10.260
- VWAP
- 10.23
- Vol
- 198.06K
- Mkt Cap
- —
- Low
- 10.097
- Amount
- 2.03M
- EV/EBITDA, TTM
- -0.09
Neostellar Capital Corp, formerly SuRo Capital Corp, is an investment company focused on private, venture-backed business. It provides public investors access to private companies. The Company seeks to primarily invest in private companies, giving public-market investors a way to evaluate exposure to a portfolio of venture capital (VC) backed companies through a publicly traded stock. The Company is externally managed by Neostellar Advisors LLC.
AI analysis of Neostellar Capital Corp (NSLR)
buyNeostellar Capital Corp (NSLR) is a good buy right now due to its recent insider purchase of 50,000 shares at $10.22, indicating strong confidence from management. The stock has shown a recent 5-day change of +1.90%, suggesting positive momentum. However, the main risk is its negative earnings per share (EPS) of -0.90 reported in the last earnings call, which reflects ongoing challenges.
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NSLR FAQ — answered by Alphio AI
Neostellar Capital Corp, formerly SuRo Capital Corp, is an investment company focused on private, venture-backed business. It provides public investors access to private companies. The Company seeks to primarily invest in private companies, giving public-market investors a way to evaluate exposure to a portfolio of venture capital (VC) backed companies through a publicly traded stock. The Company is externally managed by Neostellar Advisors LLC. It operates in the Financials sector.
Neostellar Capital Corp (NSLR) is a good buy right now due to its recent insider purchase of 50,000 shares at $10.22, indicating strong confidence from management. The stock has shown a recent 5-day change of +1.90%, suggesting positive momentum. However, the main risk is its negative earnings per share (EPS) of -0.90 reported in the last earnings call, which reflects ongoing challenges.
Esta página es solo para investigación y no constituye asesoramiento de inversión. Los modelos pueden equivocarse. El rendimiento pasado no garantiza resultados futuros.