Cyber Hornet Sp Etf Market Value
| ZZZ Etf | USD 25.29 0.68 2.76% |
| Symbol | Cyber |
The market value of Cyber Hornet SP is measured differently than its book value, which is the value of Cyber that is recorded on the company's balance sheet. Investors also form their own opinion of Cyber Hornet's value that differs from its market value or its book value, called intrinsic value, which is Cyber Hornet'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 Cyber Hornet's market value can be influenced by many factors that don't directly affect Cyber Hornet'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 Cyber Hornet's value and its price as these two are different measures arrived at by different means. Investors typically determine if Cyber Hornet is a good investment by looking at such factors as earnings, sales, fundamental and technical indicators, competition as well as analyst projections. However, Cyber Hornet'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.
Cyber Hornet '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 Cyber Hornet'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 Cyber Hornet.
| 12/15/2024 |
| 03/15/2025 |
If you would invest 0.00 in Cyber Hornet on December 15, 2024 and sell it all today you would earn a total of 0.00 from holding Cyber Hornet SP or generate 0.0% return on investment in Cyber Hornet over 90 days. Cyber Hornet is related to or competes with Tennessee Valley, Nano Labs, and CompX International. Cyber Hornet is entity of United States. It is traded as Etf on NASDAQ exchange. More
Cyber Hornet 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 Cyber Hornet'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 Cyber Hornet SP upside and downside potential and time the market with a certain degree of confidence.
| Information Ratio | (0.04) | |||
| Maximum Drawdown | 8.13 | |||
| Value At Risk | (2.69) | |||
| Potential Upside | 2.31 |
Cyber Hornet Market Risk Indicators
Today, many novice investors tend to focus exclusively on investment returns with little concern for Cyber Hornet's investment risk. Other traders do consider volatility but use just one or two very conventional indicators such as Cyber Hornet's standard deviation. In reality, there are many statistical measures that can use Cyber Hornet historical prices to predict the future Cyber Hornet's volatility.| Risk Adjusted Performance | (0.09) | |||
| Jensen Alpha | (0.10) | |||
| Total Risk Alpha | 0.0178 | |||
| Treynor Ratio | (0.27) |
Cyber Hornet SP Backtested Returns
Cyber Hornet SP secures Sharpe Ratio (or Efficiency) of -0.12, which signifies that the etf had a -0.12 % return per unit of standard deviation over the last 3 months. Cyber Hornet SP exposes twenty-one different technical indicators, which can help you to evaluate volatility embedded in its price movement. Please confirm Cyber Hornet's mean deviation of 1.12, and Risk Adjusted Performance of (0.09) to double-check the risk estimate we provide. The etf shows a Beta (market volatility) of 0.61, which signifies possible diversification benefits within a given portfolio. As returns on the market increase, Cyber Hornet's returns are expected to increase less than the market. However, during the bear market, the loss of holding Cyber Hornet is expected to be smaller as well.
Auto-correlation | -0.4 |
Poor reverse predictability
Cyber Hornet SP has poor reverse predictability. Overlapping area represents the amount of predictability between Cyber Hornet time series from 15th of December 2024 to 29th of January 2025 and 29th of January 2025 to 15th of March 2025. The more autocorrelation exist between current time interval and its lagged values, the more accurately you can make projection about the future pattern of Cyber Hornet SP price movement. The serial correlation of -0.4 indicates that just about 40.0% of current Cyber Hornet price fluctuation can be explain by its past prices.
| Correlation Coefficient | -0.4 | |
| Spearman Rank Test | -0.06 | |
| Residual Average | 0.0 | |
| Price Variance | 0.92 |
Cyber Hornet SP lagged returns against current returns
Autocorrelation, which is Cyber Hornet 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 Cyber Hornet's etf expected returns. We can calculate the autocorrelation of Cyber Hornet returns to help us make a trade decision. For example, suppose you find that Cyber Hornet 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 |
Cyber Hornet 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 Cyber Hornet etf is displaying a positive serial correlation, investors will expect a positive pattern to continue. However, if Cyber Hornet etf is observed to have a negative serial correlation, investors will generally project negative sentiment on having a locked-in long position in Cyber Hornet etf over time.
Current vs Lagged Prices |
| Timeline |
Cyber Hornet Lagged Returns
When evaluating Cyber Hornet's market value, investors can use the concept of autocorrelation to see how much of an impact past prices of Cyber Hornet etf have on its future price. Cyber Hornet 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, Cyber Hornet autocorrelation shows the relationship between Cyber Hornet etf current value and its past values and can show if there is a momentum factor associated with investing in Cyber Hornet SP.
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 Cyber Hornet SP offers a strong return on investment in its stock, a comprehensive analysis is essential. The process typically begins with a thorough review of Cyber Hornet'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 Cyber Hornet Sp Etf. Outlined below are crucial reports that will aid in making a well-informed decision on Cyber Hornet Sp Etf:Check out Cyber Hornet Correlation, Cyber Hornet Volatility and Cyber Hornet Alpha and Beta module to complement your research on Cyber Hornet. You can also try the Correlation Analysis module to reduce portfolio risk simply by holding instruments which are not perfectly correlated.
Cyber Hornet 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.