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Range Low Price High Price Comment
30 days 6.04€ 6.04€ Monday, 7th Sep 2020 SILMA.HE stock ended at 6.04€. During the day the stock fluctuated 0% from a day low at 6.04€ to a day high of 6.04€.
90 days 6.04€ 6.04€
52 weeks 5.52€ 6.10€

Historical Silmäasema Oyj prices

Date Open High Low Close Volume
Nov 07, 2018 4.91€ 5.01€ 4.87€ 5.01€ 5 129
Nov 06, 2018 5.08€ 5.08€ 4.90€ 4.98€ 4 998
Nov 05, 2018 5.19€ 5.19€ 5.14€ 5.14€ 5 424
Nov 02, 2018 5.08€ 5.19€ 5.08€ 5.18€ 16 644
Nov 01, 2018 4.85€ 5.20€ 4.85€ 5.08€ 14 262
Oct 31, 2018 4.77€ 4.85€ 4.74€ 4.85€ 12 090
Oct 30, 2018 4.85€ 4.85€ 4.77€ 4.77€ 7 005
Oct 29, 2018 4.78€ 4.79€ 4.77€ 4.79€ 6 274
Oct 26, 2018 4.77€ 4.79€ 4.77€ 4.77€ 9 690
Oct 25, 2018 4.77€ 4.79€ 4.77€ 4.77€ 7 815
Oct 24, 2018 4.76€ 4.83€ 4.76€ 4.80€ 9 288
Oct 23, 2018 4.76€ 4.78€ 4.71€ 4.78€ 10 363
Oct 22, 2018 4.66€ 4.85€ 4.66€ 4.84€ 5 404
Oct 19, 2018 4.86€ 4.86€ 4.62€ 4.76€ 22 593
Oct 18, 2018 4.80€ 4.88€ 4.80€ 4.85€ 13 633
Oct 17, 2018 4.95€ 4.95€ 4.81€ 4.85€ 4 067
Oct 16, 2018 4.76€ 5.00€ 4.76€ 4.95€ 11 739
Oct 15, 2018 4.48€ 4.99€ 4.47€ 4.95€ 21 043
Oct 12, 2018 4.43€ 4.54€ 4.41€ 4.53€ 1 063 531
Oct 11, 2018 4.51€ 4.51€ 4.40€ 4.43€ 9 737
Oct 10, 2018 4.52€ 4.60€ 4.51€ 4.60€ 9 756
Oct 09, 2018 4.51€ 4.61€ 4.51€ 4.53€ 11 219
Oct 08, 2018 4.63€ 4.63€ 4.51€ 4.60€ 5 631
Oct 05, 2018 4.58€ 4.63€ 4.49€ 4.61€ 16 128
Oct 04, 2018 4.56€ 4.56€ 4.54€ 4.56€ 1 422

FAQ

What are historical stock prices?
Historical stock prices refer to a stock’s recorded prices at various past points. These prices include several key figures that help investors and analysts evaluate a stock’s performance over time:
Open: Open price for the trading day.
High: Highest price for the trading day.
Low: Lowest price for the trading day.
Close: Close price for the trading day.

Additionally, historical prices often include:
Volume is the number of shares traded during the day. It indicates how actively a stock was traded and can provide insights into market sentiment and liquidity.

How can I use SILMA.HE stock historical prices to predict future price movements?
Trend Analysis: Examine the SILMA.HE stock’s historical trends to identify patterns that might continue.
Moving Averages: Use moving averages to detect potential reversal points.
Momentum Indicators: Apply indicators like RSI or MACD to assess the momentum and strength of price movements.
Volume Analysis: Analyze trading volume alongside price changes to gauge trend strength.
Statistical Methods: Use statistical tools such as regression analysis to model and forecast future prices based on past data.
These techniques can provide insights but should be used with risk management practices to mitigate potential losses.

What impact do stock splits have on historical price data?
When a company performs a stock split, it adjusts the historical price data to reflect the new, lower trading price as if it had always been that way.

This ensures consistency for anyone analyzing the stock’s past prices. The adjustment helps prevent misleading signals on charts, such as false sell signals or bearish trends that aren’t there. For instance, in a 2-for-1 stock split, the price per share is cut in half, which would otherwise appear as a dramatic drop on the chart. If someone didn’t know about the split, they might wrongly think something negative happened to the company. Most technical indicators would also react to this apparent drop by signaling to sell.

A stock split, while making the shares seem more affordable and potentially more attractive to investors, doesn’t alter the company’s fundamental value.

Why do the SILMA.HE stock historical prices show a range for periods like 30 days, 90 days, and 52 weeks?
The range provides the lowest and highest prices at which the stock has traded during the specified period. This helps investors understand the stock’s volatility and price variability within that timeframe.

How can I use historical price volatility to assess risk?
High price volatility historically indicates higher risk and potentially higher returns. Investors can gauge the stock’s risk level by examining the range between high and low prices over various periods.
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