Columbia Research Enhanced Etf Market Value
| REVS Etf | USD 26.99 0.07 0.26% |
| Symbol | Columbia |
The market value of Columbia Research 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 Research's value that differs from its market value or its book value, called intrinsic value, which is Columbia Research'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 Research's market value can be influenced by many factors that don't directly affect Columbia Research'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 Research's value and its price as these two are different measures arrived at by different means. Investors typically determine if Columbia Research is a good investment by looking at such factors as earnings, sales, fundamental and technical indicators, competition as well as analyst projections. However, Columbia Research'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 Research '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 Research'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 Research.
| 11/06/2023 |
| 11/30/2024 |
If you would invest 0.00 in Columbia Research on November 6, 2023 and sell it all today you would earn a total of 0.00 from holding Columbia Research Enhanced or generate 0.0% return on investment in Columbia Research over 390 days. Columbia Research is related to or competes with QRAFT AI, Vesper Large, and Columbia ETF. The fund invests at least 80 percent of its assets in the securities of the index More
Columbia Research 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 Research'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 Research Enhanced upside and downside potential and time the market with a certain degree of confidence.
| Downside Deviation | 0.5161 | |||
| Information Ratio | (0.02) | |||
| Maximum Drawdown | 3.56 | |||
| Value At Risk | (0.79) | |||
| Potential Upside | 1.18 |
Columbia Research Market Risk Indicators
Today, many novice investors tend to focus exclusively on investment returns with little concern for Columbia Research's investment risk. Other traders do consider volatility but use just one or two very conventional indicators such as Columbia Research's standard deviation. In reality, there are many statistical measures that can use Columbia Research historical prices to predict the future Columbia Research's volatility.| Risk Adjusted Performance | 0.1373 | |||
| Jensen Alpha | 0.016 | |||
| Total Risk Alpha | (0.0003) | |||
| Sortino Ratio | (0.03) | |||
| Treynor Ratio | 0.1486 |
Sophisticated investors, who have witnessed many market ups and downs, anticipate that the market will even out over time. This tendency of Columbia Research's price to converge to an average value over time is called mean reversion. However, historically, high market prices usually discourage investors that believe in mean reversion to invest, while low prices are viewed as an opportunity to buy.
Columbia Research Backtested Returns
Currently, Columbia Research Enhanced is very steady. Columbia Research secures Sharpe Ratio (or Efficiency) of 0.19, which signifies that the etf had a 0.19% return per unit of risk over the last 3 months. We have found thirty technical indicators for Columbia Research Enhanced, which you can use to evaluate the volatility of the entity. Please confirm Columbia Research's Mean Deviation of 0.5234, risk adjusted performance of 0.1373, and Downside Deviation of 0.5161 to double-check if the risk estimate we provide is consistent with the expected return of 0.13%. The etf shows a Beta (market volatility) of 0.76, which signifies possible diversification benefits within a given portfolio. As returns on the market increase, Columbia Research's returns are expected to increase less than the market. However, during the bear market, the loss of holding Columbia Research is expected to be smaller as well.
Auto-correlation | 0.87 |
Very good predictability
Columbia Research Enhanced has very good predictability. Overlapping area represents the amount of predictability between Columbia Research time series from 6th of November 2023 to 19th of May 2024 and 19th of May 2024 to 30th 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 Research price movement. The serial correlation of 0.87 indicates that approximately 87.0% of current Columbia Research price fluctuation can be explain by its past prices.
| Correlation Coefficient | 0.87 | |
| Spearman Rank Test | 0.9 | |
| Residual Average | 0.0 | |
| Price Variance | 1.27 |
Columbia Research lagged returns against current returns
Autocorrelation, which is Columbia Research 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 Research's etf expected returns. We can calculate the autocorrelation of Columbia Research returns to help us make a trade decision. For example, suppose you find that Columbia Research 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 |
| Timeline |
Columbia Research 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 Research etf is displaying a positive serial correlation, investors will expect a positive pattern to continue. However, if Columbia Research etf is observed to have a negative serial correlation, investors will generally project negative sentiment on having a locked-in long position in Columbia Research etf over time.
Current vs Lagged Prices |
| Timeline |
Columbia Research Lagged Returns
When evaluating Columbia Research's market value, investors can use the concept of autocorrelation to see how much of an impact past prices of Columbia Research etf have on its future price. Columbia Research 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 Research autocorrelation shows the relationship between Columbia Research etf current value and its past values and can show if there is a momentum factor associated with investing in Columbia Research Enhanced.
Regressed Prices |
| Timeline |
Thematic Opportunities
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Check out Columbia Research Correlation, Columbia Research Volatility and Columbia Research Alpha and Beta module to complement your research on Columbia Research. You can also try the Idea Breakdown module to analyze constituents of all Macroaxis ideas. Macroaxis investment ideas are predefined, sector-focused investing themes.
Columbia Research 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.