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SPDR S&P Emerging Markets ex-China ETF Stock Price Chart

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

SPDR S&P Emerging Markets ex-China ETF Price Chart Indicators

Moving Averages Level Buy or Sell
8-day SMA: 24.59 Sell
20-day SMA: 24.79 Sell
50-day SMA: 25.02 Sell
200-day SMA: 0 Buy
8-day EMA: 24.51 Sell
20-day EMA: 24.74 Sell
50-day EMA: 25 Sell
200-day EMA: 4.7 Buy

SPDR S&P Emerging Markets ex-China ETF Technical Analysis Indicators

Chart Indicators Level Buy or Sell
MACD (12, 26): -0.18 Sell
Relative Strength Index (14 RSI): 34.03 Sell
Chaikin Money Flow: 0 -
Bollinger Bands Level Buy or Sell
Bollinger Bands (25): (24.5 - 25.12) Sell
Bollinger Bands (100): ( - ) Buy

SPDR S&P Emerging Markets ex-China 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 SPDR S&P Emerging Markets ex-China ETF Stock

Is SPDR S&P Emerging Markets ex-China ETF Stock a Buy?

XCNY Technical Analysis vs Fundamental Analysis

Sell
32
SPDR S&P Emerging Markets ex-China ETF (XCNY) is a Sell

Is SPDR S&P Emerging Markets ex-China ETF a Buy or a Sell?

SPDR S&P Emerging Markets ex-China ETF Stock Info

Market Cap:
0
Price in USD:
24.16
Share Volume:
100

SPDR S&P Emerging Markets ex-China ETF 52-Week Range

52-Week High:
26.54
52-Week Low:
24.21
Sell
32
SPDR S&P Emerging Markets ex-China ETF (XCNY) is a Sell

SPDR S&P Emerging Markets ex-China ETF Share Price Forecast

Is SPDR S&P Emerging Markets ex-China ETF Stock a Buy?

Technical Analysis of SPDR S&P Emerging Markets ex-China ETF

Should I short SPDR S&P Emerging Markets ex-China ETF stock?

* SPDR S&P Emerging Markets ex-China 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.