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Range Low Price High Price Comment
30 days $5.52 $5.52 Friday, 27th May 2022 CMFN stock ended at $5.52. During the day the stock fluctuated 0% from a day low at $5.52 to a day high of $5.52.
90 days $5.52 $5.72
52 weeks $4.77 $6.58

Historical CM Finance Inc prices

Date Open High Low Close Volume
Jul 16, 2018 $8.85 $8.95 $8.80 $8.85 16 936
Jul 13, 2018 $8.90 $8.95 $8.80 $8.80 23 279
Jul 12, 2018 $8.90 $8.95 $8.80 $8.85 32 702
Jul 11, 2018 $8.95 $9.06 $8.80 $8.95 19 015
Jul 10, 2018 $9.00 $9.00 $8.86 $8.95 37 028
Jul 09, 2018 $8.95 $9.05 $8.95 $8.95 12 261
Jul 06, 2018 $8.95 $9.10 $8.95 $9.00 48 608
Jul 05, 2018 $9.45 $9.45 $9.10 $9.20 15 866
Jul 03, 2018 $9.00 $9.35 $8.90 $9.35 21 019
Jul 02, 2018 $8.90 $9.00 $8.90 $9.00 37 529
Jun 29, 2018 $8.95 $8.95 $8.85 $8.90 19 568
Jun 28, 2018 $8.95 $9.00 $8.79 $8.90 16 072
Jun 27, 2018 $8.85 $8.95 $8.66 $8.95 32 221
Jun 26, 2018 $8.90 $8.95 $8.56 $8.80 48 342
Jun 25, 2018 $9.00 $9.00 $8.80 $8.90 16 193
Jun 22, 2018 $8.95 $9.02 $8.85 $9.00 12 718
Jun 21, 2018 $8.92 $9.05 $8.90 $8.95 20 387
Jun 20, 2018 $9.00 $9.00 $8.65 $8.90 48 604
Jun 19, 2018 $9.05 $9.05 $8.85 $8.95 34 997
Jun 18, 2018 $9.20 $9.40 $8.85 $9.05 107 348
Jun 15, 2018 $9.15 $9.45 $9.15 $9.20 50 061
Jun 14, 2018 $9.70 $9.70 $8.20 $9.35 109 379
Jun 13, 2018 $9.85 $9.85 $9.70 $9.80 102 113
Jun 12, 2018 $9.90 $9.90 $9.60 $9.70 49 665
Jun 11, 2018 $9.55 $9.90 $9.55 $9.88 150 487

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 CMFN stock historical prices to predict future price movements?
Trend Analysis: Examine the CMFN 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 CMFN 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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