The Alger Etf Market Value
| AWEG Etf | 27.70 0.15 0.54% |
| Symbol | Alger |
The market value of Alger ETF is measured differently than its book value, which is the value of Alger that is recorded on the company's balance sheet. Investors also form their own opinion of Alger ETF's value that differs from its market value or its book value, called intrinsic value, which is Alger ETF'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 Alger ETF's market value can be influenced by many factors that don't directly affect Alger ETF'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 Alger ETF's value and its price as these two are different measures arrived at by different means. Investors typically determine if Alger ETF is a good investment by looking at such factors as earnings, sales, fundamental and technical indicators, competition as well as analyst projections. However, Alger ETF'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.
Alger ETF '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 Alger ETF'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 Alger ETF.
| 08/29/2024 |
| 11/27/2024 |
If you would invest 0.00 in Alger ETF on August 29, 2024 and sell it all today you would earn a total of 0.00 from holding The Alger ETF or generate 0.0% return on investment in Alger ETF over 90 days. Alger ETF is related to or competes with JPMorgan Fundamental, Vanguard Mid, SPDR SP, SPDR SP, Direxion Daily, DBX ETF, and Nuveen ESG. Alger ETF is entity of United States. It is traded as Etf on NYSE ARCA exchange. More
Alger ETF 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 Alger ETF'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 The Alger ETF upside and downside potential and time the market with a certain degree of confidence.
| Downside Deviation | 1.09 | |||
| Information Ratio | 0.0238 | |||
| Maximum Drawdown | 6.57 | |||
| Value At Risk | (1.73) | |||
| Potential Upside | 1.61 |
Alger ETF Market Risk Indicators
Today, many novice investors tend to focus exclusively on investment returns with little concern for Alger ETF's investment risk. Other traders do consider volatility but use just one or two very conventional indicators such as Alger ETF's standard deviation. In reality, there are many statistical measures that can use Alger ETF historical prices to predict the future Alger ETF's volatility.| Risk Adjusted Performance | 0.1083 | |||
| Jensen Alpha | (0) | |||
| Total Risk Alpha | (0.03) | |||
| Sortino Ratio | 0.0251 | |||
| Treynor Ratio | 0.1191 |
Sophisticated investors, who have witnessed many market ups and downs, anticipate that the market will even out over time. This tendency of Alger ETF'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.
Alger ETF Backtested Returns
At this point, Alger ETF is very steady. Alger ETF secures Sharpe Ratio (or Efficiency) of 0.15, which signifies that the etf had a 0.15% return per unit of standard deviation over the last 3 months. We have found twenty-eight technical indicators for The Alger ETF, which you can use to evaluate the volatility of the entity. Please confirm Alger ETF's Semi Deviation of 0.8419, risk adjusted performance of 0.1083, and Mean Deviation of 0.8623 to double-check if the risk estimate we provide is consistent with the expected return of 0.17%. The etf shows a Beta (market volatility) of 1.24, 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, Alger ETF will likely underperform.
Auto-correlation | 0.44 |
Average predictability
The Alger ETF has average predictability. Overlapping area represents the amount of predictability between Alger ETF time series from 29th of August 2024 to 13th of October 2024 and 13th of October 2024 to 27th 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 Alger ETF price movement. The serial correlation of 0.44 indicates that just about 44.0% of current Alger ETF price fluctuation can be explain by its past prices.
| Correlation Coefficient | 0.44 | |
| Spearman Rank Test | 0.03 | |
| Residual Average | 0.0 | |
| Price Variance | 0.98 |
Alger ETF lagged returns against current returns
Autocorrelation, which is Alger ETF 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 Alger ETF's etf expected returns. We can calculate the autocorrelation of Alger ETF returns to help us make a trade decision. For example, suppose you find that Alger ETF 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 |
Alger ETF 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 Alger ETF etf is displaying a positive serial correlation, investors will expect a positive pattern to continue. However, if Alger ETF etf is observed to have a negative serial correlation, investors will generally project negative sentiment on having a locked-in long position in Alger ETF etf over time.
Current vs Lagged Prices |
| Timeline |
Alger ETF Lagged Returns
When evaluating Alger ETF's market value, investors can use the concept of autocorrelation to see how much of an impact past prices of Alger ETF etf have on its future price. Alger ETF 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, Alger ETF autocorrelation shows the relationship between Alger ETF etf current value and its past values and can show if there is a momentum factor associated with investing in The Alger ETF.
Regressed Prices |
| Timeline |
Currently Active Assets on Macroaxis
When determining whether Alger ETF is a good investment, qualitative aspects like company management, corporate governance, and ethical practices play a significant role. A comparison with peer companies also provides context and helps to understand if Alger Etf is undervalued or overvalued. This multi-faceted approach, blending both quantitative and qualitative analysis, forms a solid foundation for making an informed investment decision about The Alger Etf. Highlighted below are key reports to facilitate an investment decision about The Alger Etf:Check out Alger ETF Correlation, Alger ETF Volatility and Alger ETF Alpha and Beta module to complement your research on Alger ETF. You can also try the Pattern Recognition module to use different Pattern Recognition models to time the market across multiple global exchanges.
Alger ETF 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.