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Service Properties Trust Stock Price Chart

  • The current trend is relatively stagnant and SVC is experiencing slight selling pressure.

Service Properties Trust Price Chart Indicators

Moving Averages Level Buy or Sell
8-day SMA: 4.55 Buy
20-day SMA: 4.58 Buy
50-day SMA: 4.99 Sell
200-day SMA: 6.29 Sell
8-day EMA: 4.58 Buy
20-day EMA: 4.62 Buy
50-day EMA: 4.86 Sell
200-day EMA: 5.97 Sell

Service Properties Trust Technical Analysis Indicators

Chart Indicators Level Buy or Sell
MACD (12, 26): -0.08 Sell
Relative Strength Index (14 RSI): 48.98 Sell
Chaikin Money Flow: -335117 -
Bollinger Bands Level Buy or Sell
Bollinger Bands (25): (4.45 - 4.67) Buy
Bollinger Bands (100): (4.66 - 5.74) Sell

Service Properties Trust Technical Analysis

Technical Analysis: Buy or Sell?
8-day SMA:
20-day SMA:
50-day SMA:
200-day SMA:
8-day EMA:
20-day EMA:
50-day EMA:
200-day EMA:
MACD (12, 26):
Relative Strength Index (14 RSI):
Bollinger Bands (25):
Bollinger Bands (100):

Technical Analysis for Service Properties Trust Stock

Is Service Properties Trust Stock a Buy?

SVC Technical Analysis vs Fundamental Analysis

Buy
52
Service Properties Trust (SVC) is a Buy

Is Service Properties Trust a Buy or a Sell?

Service Properties Trust Stock Info

Market Cap:
771.39M
Price in USD:
4.65
Share Volume:
822.56K

Service Properties Trust 52-Week Range

52-Week High:
8.72
52-Week Low:
4.26
Buy
52
Service Properties Trust (SVC) is a Buy

Service Properties Trust Share Price Forecast

Is Service Properties Trust Stock a Buy?

Fundamental Analysis of Service Properties Trust

Is Service Properties Trust a good investment?

  • Analysts estimate an earnings decrease this quarter of $0.00 per share, a decrease next quarter of $0.00 per share, a decrease this year of $0.17 per share, and a decrease next year of $0.00 per share.

Technical Analysis of Service Properties Trust

Should I short Service Properties Trust stock?

* Service Properties Trust stock forecasts short-term for next days and weeks may differ from long term prediction for next month and year based on timeline differences.