Columbia Seligman Semiconductor Etf Market Value
| SEMI Etf | USD 24.51 0.29 1.17% |
| Symbol | Columbia |
The market value of Columbia Seligman is measured differently than its book value, which is the value of Columbia that is recorded on the company's balance sheet. Investors also form their own opinion of Columbia Seligman's value that differs from its market value or its book value, called intrinsic value, which is Columbia Seligman's true underlying value. Investors use various methods to calculate intrinsic value and buy a stock when its market value falls below its intrinsic value. Because Columbia Seligman's market value can be influenced by many factors that don't directly affect Columbia Seligman's underlying business (such as a pandemic or basic market pessimism), market value can vary widely from intrinsic value.
Please note, there is a significant difference between Columbia Seligman's value and its price as these two are different measures arrived at by different means. Investors typically determine if Columbia Seligman is a good investment by looking at such factors as earnings, sales, fundamental and technical indicators, competition as well as analyst projections. However, Columbia Seligman's price is the amount at which it trades on the open market and represents the number that a seller and buyer find agreeable to each party.
Columbia Seligman 'What if' Analysis
In the world of financial modeling, what-if analysis is part of sensitivity analysis performed to test how changes in assumptions impact individual outputs in a model. When applied to Columbia Seligman's etf what-if analysis refers to the analyzing how the change in your past investing horizon will affect the profitability against the current market value of Columbia Seligman.
| 08/30/2024 |
| 11/28/2024 |
If you would invest 0.00 in Columbia Seligman on August 30, 2024 and sell it all today you would earn a total of 0.00 from holding Columbia Seligman Semiconductor or generate 0.0% return on investment in Columbia Seligman over 90 days. Columbia Seligman is related to or competes with Taitron Components, and Amtech Systems. Under normal market conditions, the fund invests at least 80 percent of its net assets in securities of semiconductor, s... More
Columbia Seligman Upside/Downside Indicators
Understanding different market momentum indicators often help investors to time their next move. Potential upside and downside technical ratios enable traders to measure Columbia Seligman's etf current market value against overall market sentiment and can be a good tool during both bulling and bearish trends. Here we outline some of the essential indicators to assess Columbia Seligman Semiconductor upside and downside potential and time the market with a certain degree of confidence.
| Information Ratio | (0.09) | |||
| Maximum Drawdown | 8.94 | |||
| Value At Risk | (3.76) | |||
| Potential Upside | 2.93 |
Columbia Seligman Market Risk Indicators
Today, many novice investors tend to focus exclusively on investment returns with little concern for Columbia Seligman's investment risk. Other traders do consider volatility but use just one or two very conventional indicators such as Columbia Seligman's standard deviation. In reality, there are many statistical measures that can use Columbia Seligman historical prices to predict the future Columbia Seligman's volatility.| Risk Adjusted Performance | (0.02) | |||
| Jensen Alpha | (0.22) | |||
| Total Risk Alpha | (0.37) | |||
| Treynor Ratio | (0.05) |
Columbia Seligman Backtested Returns
Columbia Seligman secures Sharpe Ratio (or Efficiency) of -0.0306, which signifies that the etf had a -0.0306% return per unit of risk over the last 3 months. Columbia Seligman Semiconductor exposes twenty-three different technical indicators, which can help you to evaluate volatility embedded in its price movement. Please confirm Columbia Seligman's Risk Adjusted Performance of (0.02), standard deviation of 1.97, and Mean Deviation of 1.45 to double-check the risk estimate we provide. The etf shows a Beta (market volatility) of 1.34, which signifies a somewhat significant risk relative to the market. As the market goes up, the company is expected to outperform it. However, if the market returns are negative, Columbia Seligman will likely underperform.
Auto-correlation | -0.37 |
Poor reverse predictability
Columbia Seligman Semiconductor has poor reverse predictability. Overlapping area represents the amount of predictability between Columbia Seligman time series from 30th of August 2024 to 14th of October 2024 and 14th of October 2024 to 28th of November 2024. The more autocorrelation exist between current time interval and its lagged values, the more accurately you can make projection about the future pattern of Columbia Seligman price movement. The serial correlation of -0.37 indicates that just about 37.0% of current Columbia Seligman price fluctuation can be explain by its past prices.
| Correlation Coefficient | -0.37 | |
| Spearman Rank Test | -0.54 | |
| Residual Average | 0.0 | |
| Price Variance | 0.46 |
Columbia Seligman lagged returns against current returns
Autocorrelation, which is Columbia Seligman etf's lagged correlation, explains the relationship between observations of its time series of returns over different periods of time. The observations are said to be independent if autocorrelation is zero. Autocorrelation is calculated as a function of mean and variance and can have practical application in predicting Columbia Seligman's etf expected returns. We can calculate the autocorrelation of Columbia Seligman returns to help us make a trade decision. For example, suppose you find that Columbia Seligman has exhibited high autocorrelation historically, and you observe that the etf is moving up for the past few days. In that case, you can expect the price movement to match the lagging time series.
Current and Lagged Values |
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Columbia Seligman regressed lagged prices vs. current prices
Serial correlation can be approximated by using the Durbin-Watson (DW) test. The correlation can be either positive or negative. If Columbia Seligman etf is displaying a positive serial correlation, investors will expect a positive pattern to continue. However, if Columbia Seligman etf is observed to have a negative serial correlation, investors will generally project negative sentiment on having a locked-in long position in Columbia Seligman etf over time.
Current vs Lagged Prices |
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Columbia Seligman Lagged Returns
When evaluating Columbia Seligman's market value, investors can use the concept of autocorrelation to see how much of an impact past prices of Columbia Seligman etf have on its future price. Columbia Seligman autocorrelation represents the degree of similarity between a given time horizon and a lagged version of the same horizon over the previous time interval. In other words, Columbia Seligman autocorrelation shows the relationship between Columbia Seligman etf current value and its past values and can show if there is a momentum factor associated with investing in Columbia Seligman Semiconductor.
Regressed Prices |
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Currently Active Assets on Macroaxis
When determining whether Columbia Seligman offers a strong return on investment in its stock, a comprehensive analysis is essential. The process typically begins with a thorough review of Columbia Seligman's financial statements, including income statements, balance sheets, and cash flow statements, to assess its financial health. Key financial ratios are used to gauge profitability, efficiency, and growth potential of Columbia Seligman Semiconductor Etf. Outlined below are crucial reports that will aid in making a well-informed decision on Columbia Seligman Semiconductor Etf:Check out Columbia Seligman Correlation, Columbia Seligman Volatility and Columbia Seligman Alpha and Beta module to complement your research on Columbia Seligman. You can also try the Risk-Return Analysis module to view associations between returns expected from investment and the risk you assume.
Columbia Seligman technical etf analysis exercises models and trading practices based on price and volume transformations, such as the moving averages, relative strength index, regressions, price and return correlations, business cycles, etf market cycles, or different charting patterns.