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Danakali Stock Predictions

Danakali Stock Forecast

  • Is Danakali Stock Undervalued?
    The current Danakali [SBMSF] share price is $0.16. The Score for SBMSF is 37, which is 26% below its historic median score of 50, and infers higher risk than normal.
  • SBMSF is currently trading in the 30-40% percentile range relative to its historical Stock Score levels.

Will Danakali Stock Go Up Next Year?

Data Unavailable

Danakali Stock Rating

Data Unavailable

Danakali Stock Price History

Is SBMSF stock going to rise?

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

Stock Information

Stock Info

Exchange:
OTCM
Country:
Australia
Industry:
Chemicals
Sector:
Materials
Type:
stock
Website:
danakali.com

52-Week Data

52-Week High:
0.35
52-Week Low:
0.04

Prediction Charts

Market Cap:
58.01M
Price in USD:
0.16
Volume:
0
Beta:
1.34

Technical Analysis

SMA50:
0.17
SMA100:
0.18
SMA200:
0.18
52-Wk Change:
-13.46%

Stock Predictions

  • Is Danakali stock public?
    Yes, Danakali is a publicly traded company.
  • What is the Danakali stock quote today?
    The Danakali stock price is 0.16 USD today.
  • How to buy Danakali stock online?
    You can buy Danakali shares by opening an account at a top tier brokerage firm, such as TD Ameritrade or tastyworks.

14-Day Historical Data

Date Opening Closing Minimum Maximum
Nov-7 0.16 0.16 0.16 0.16
Nov-8 0.15 0.16 0.16 0.15
Nov-11 0.16 0.16 0.17 0.16
Nov-12 0.16 0.16 0.16 0.16
Nov-13 0.16 0.17 0.17 0.16
Nov-14 0.17 0.15 0.17 0.15
Nov-15 0.17 0.17 0.17 0.17
Nov-18 0.17 0.17 0.17 0.10
Nov-19 0.14 0.17 0.17 0.14
Nov-20 0.17 0.17 0.17 0.17
Nov-21 0.17 0.17 0.17 0.17
Nov-22 0.16 0.16 0.16 0.16
Nov-25 0.16 0.16 0.16 0.16
Nov-26 0.16 0.16 0.16 0.16

Danakali Earnings

Data Unavailable

Danakali Forecast Revenue Growth

Data Unavailable

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