Arrow Etf Trust Etf Market Value
| GYLD Etf | USD 13.02 0.02 0.15% |
| Symbol | Arrow |
The market value of Arrow ETF Trust is measured differently than its book value, which is the value of Arrow that is recorded on the company's balance sheet. Investors also form their own opinion of Arrow ETF's value that differs from its market value or its book value, called intrinsic value, which is Arrow 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 Arrow ETF's market value can be influenced by many factors that don't directly affect Arrow 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 Arrow ETF's value and its price as these two are different measures arrived at by different means. Investors typically determine if Arrow ETF is a good investment by looking at such factors as earnings, sales, fundamental and technical indicators, competition as well as analyst projections. However, Arrow 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.
Arrow 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 Arrow 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 Arrow ETF.
| 10/28/2024 |
| 11/27/2024 |
If you would invest 0.00 in Arrow ETF on October 28, 2024 and sell it all today you would earn a total of 0.00 from holding Arrow ETF Trust or generate 0.0% return on investment in Arrow ETF over 30 days. Arrow ETF is related to or competes with IShares Morningstar, Amplify High, First Trust, SPDR SSgA, and Invesco CEF. The fund uses a passive or indexing investment approach to seek to track the price and yield performance of the index More
Arrow 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 Arrow 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 Arrow ETF Trust upside and downside potential and time the market with a certain degree of confidence.
| Downside Deviation | 0.8087 | |||
| Information Ratio | (0.21) | |||
| Maximum Drawdown | 3.29 | |||
| Value At Risk | (0.97) | |||
| Potential Upside | 0.8534 |
Arrow ETF Market Risk Indicators
Today, many novice investors tend to focus exclusively on investment returns with little concern for Arrow ETF's investment risk. Other traders do consider volatility but use just one or two very conventional indicators such as Arrow ETF's standard deviation. In reality, there are many statistical measures that can use Arrow ETF historical prices to predict the future Arrow ETF's volatility.| Risk Adjusted Performance | 0.0054 | |||
| Jensen Alpha | (0.03) | |||
| Total Risk Alpha | (0.1) | |||
| Sortino Ratio | (0.15) | |||
| Treynor Ratio | (0.02) |
Arrow ETF Trust Backtested Returns
Arrow ETF Trust secures Sharpe Ratio (or Efficiency) of -0.0069, which signifies that the etf had a -0.0069% return per unit of risk over the last 3 months. Arrow ETF Trust exposes thirty different technical indicators, which can help you to evaluate volatility embedded in its price movement. Please confirm Arrow ETF's Risk Adjusted Performance of 0.0054, mean deviation of 0.4034, and Downside Deviation of 0.8087 to double-check the risk estimate we provide. The etf shows a Beta (market volatility) of 0.24, which signifies not very significant fluctuations relative to the market. As returns on the market increase, Arrow ETF's returns are expected to increase less than the market. However, during the bear market, the loss of holding Arrow ETF is expected to be smaller as well.
Auto-correlation | 0.21 |
Weak predictability
Arrow ETF Trust has weak predictability. Overlapping area represents the amount of predictability between Arrow ETF time series from 28th of October 2024 to 12th of November 2024 and 12th of November 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 Arrow ETF Trust price movement. The serial correlation of 0.21 indicates that over 21.0% of current Arrow ETF price fluctuation can be explain by its past prices.
| Correlation Coefficient | 0.21 | |
| Spearman Rank Test | -0.6 | |
| Residual Average | 0.0 | |
| Price Variance | 0.0 |
Arrow ETF Trust lagged returns against current returns
Autocorrelation, which is Arrow 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 Arrow ETF's etf expected returns. We can calculate the autocorrelation of Arrow ETF returns to help us make a trade decision. For example, suppose you find that Arrow 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 |
Arrow 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 Arrow ETF etf is displaying a positive serial correlation, investors will expect a positive pattern to continue. However, if Arrow ETF etf is observed to have a negative serial correlation, investors will generally project negative sentiment on having a locked-in long position in Arrow ETF etf over time.
Current vs Lagged Prices |
| Timeline |
Arrow ETF Lagged Returns
When evaluating Arrow ETF's market value, investors can use the concept of autocorrelation to see how much of an impact past prices of Arrow ETF etf have on its future price. Arrow 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, Arrow ETF autocorrelation shows the relationship between Arrow ETF etf current value and its past values and can show if there is a momentum factor associated with investing in Arrow ETF Trust.
Regressed Prices |
| Timeline |
Also Currently Popular
Analyzing currently trending equities could be an opportunity to develop a better portfolio based on different market momentums that they can trigger. Utilizing the top trending stocks is also useful when creating a market-neutral strategy or pair trading technique involving a short or a long position in a currently trending equity.When determining whether Arrow ETF Trust is a strong investment it is important to analyze Arrow ETF's competitive position within its industry, examining market share, product or service uniqueness, and competitive advantages. Beyond financials and market position, potential investors should also consider broader economic conditions, industry trends, and any regulatory or geopolitical factors that may impact Arrow ETF's future performance. For an informed investment choice regarding Arrow Etf, refer to the following important reports:Check out Arrow ETF Correlation, Arrow ETF Volatility and Arrow ETF Alpha and Beta module to complement your research on Arrow ETF. You can also try the ETFs module to find actively traded Exchange Traded Funds (ETF) from around the world.
Arrow 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.