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Invesco KBW Property & Casualty Ins ETF Stock Price Chart

  • Based on the share price being above its 5, 20 & 50 day exponential moving averages, the current trend is considered strongly bullish and KBWP is experiencing slight buying pressure.

Invesco KBW Property & Casualty Ins ETF Price Chart Indicators

Moving Averages Level Buy or Sell
8-day SMA: 114.8 Buy
20-day SMA: 114.4 Buy
50-day SMA: 110.1 Buy
200-day SMA: 102.99 Buy
8-day EMA: 115.1 Buy
20-day EMA: 113.86 Buy
50-day EMA: 110.89 Buy
200-day EMA: 103.18 Buy

Invesco KBW Property & Casualty Ins ETF Technical Analysis Indicators

Chart Indicators Level Buy or Sell
MACD (12, 26): 1.57 Buy
Relative Strength Index (14 RSI): 62.67 Buy
Chaikin Money Flow: 6241 -
Bollinger Bands Level Buy or Sell
Bollinger Bands (25): (111.63 - 115.57) Buy
Bollinger Bands (100): (102.91 - 111.39) Buy

Invesco KBW Property & Casualty Ins ETF 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 Invesco KBW Property & Casualty Ins ETF Stock

Is Invesco KBW Property & Casualty Ins ETF Stock a Buy?

KBWP Technical Analysis vs Fundamental Analysis

Buy
60
Invesco KBW Property & Casualty Ins ETF (KBWP) is a Buy

Is Invesco KBW Property & Casualty Ins ETF a Buy or a Sell?

Invesco KBW Property & Casualty Ins ETF Stock Info

Market Cap:
0
Price in USD:
115.49
Share Volume:
17.89K

Invesco KBW Property & Casualty Ins ETF 52-Week Range

52-Week High:
116.95
52-Week Low:
82.64
Buy
60
Invesco KBW Property & Casualty Ins ETF (KBWP) is a Buy

Invesco KBW Property & Casualty Ins ETF Share Price Forecast

Is Invesco KBW Property & Casualty Ins ETF Stock a Buy?

Technical Analysis of Invesco KBW Property & Casualty Ins ETF

Should I short Invesco KBW Property & Casualty Ins ETF stock?

* Invesco KBW Property & Casualty Ins ETF stock forecasts short-term for next days and weeks may differ from long term prediction for next month and year based on timeline differences.