Hyperscale Data, Stock Market Value
| GPUS Stock | 6.61 0.25 3.93% |
| Symbol | Hyperscale |
Is Internet Software & Services (discontinued effective close of September 28, 2018) space expected to grow? Or is there an opportunity to expand the business' product line in the future? Factors like these will boost the valuation of Hyperscale Data,. If investors know Hyperscale will grow in the future, the company's valuation will be higher. The financial industry is built on trying to define current growth potential and future valuation accurately. All the valuation information about Hyperscale Data, listed above have to be considered, but the key to understanding future value is determining which factors weigh more heavily than others.
Earnings Share 2 K | Revenue Per Share 11.986 | Quarterly Revenue Growth (0.40) | Return On Assets (0.12) | Return On Equity (2.09) |
The market value of Hyperscale Data, is measured differently than its book value, which is the value of Hyperscale that is recorded on the company's balance sheet. Investors also form their own opinion of Hyperscale Data,'s value that differs from its market value or its book value, called intrinsic value, which is Hyperscale Data,'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 Hyperscale Data,'s market value can be influenced by many factors that don't directly affect Hyperscale Data,'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 Hyperscale Data,'s value and its price as these two are different measures arrived at by different means. Investors typically determine if Hyperscale Data, is a good investment by looking at such factors as earnings, sales, fundamental and technical indicators, competition as well as analyst projections. However, Hyperscale Data,'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.
Hyperscale Data, '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 Hyperscale Data,'s stock 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 Hyperscale Data,.
| 10/27/2024 |
| 11/26/2024 |
If you would invest 0.00 in Hyperscale Data, on October 27, 2024 and sell it all today you would earn a total of 0.00 from holding Hyperscale Data, or generate 0.0% return on investment in Hyperscale Data, over 30 days. Hyperscale Data, is related to or competes with Merit Medical, Artisan Partners, Pinterest, RadNet, Weibo Corp, Arrow Electronics, and Meiwu Technology. Hyperscale Data, is entity of United States More
Hyperscale Data, 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 Hyperscale Data,'s stock 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 Hyperscale Data, upside and downside potential and time the market with a certain degree of confidence.
| Downside Deviation | 6.46 | |||
| Information Ratio | 0.1224 | |||
| Maximum Drawdown | 3176.47 | |||
| Value At Risk | (8.70) | |||
| Potential Upside | 10.0 |
Hyperscale Data, Market Risk Indicators
Today, many novice investors tend to focus exclusively on investment returns with little concern for Hyperscale Data,'s investment risk. Other traders do consider volatility but use just one or two very conventional indicators such as Hyperscale Data,'s standard deviation. In reality, there are many statistical measures that can use Hyperscale Data, historical prices to predict the future Hyperscale Data,'s volatility.| Risk Adjusted Performance | 0.1041 | |||
| Jensen Alpha | 50.16 | |||
| Total Risk Alpha | (13.57) | |||
| Sortino Ratio | 7.39 | |||
| Treynor Ratio | (2.51) |
Sophisticated investors, who have witnessed many market ups and downs, anticipate that the market will even out over time. This tendency of Hyperscale Data,'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.
Hyperscale Data, Backtested Returns
Hyperscale Data, holds Efficiency (Sharpe) Ratio of -0.0175, which attests that the entity had a -0.0175% return per unit of risk over the last 3 months. Hyperscale Data, exposes twenty-eight different technical indicators, which can help you to evaluate volatility embedded in its price movement. Please check out Hyperscale Data,'s Market Risk Adjusted Performance of (2.50), coefficient of variation of 814.79, and Risk Adjusted Performance of 0.1041 to validate the risk estimate we provide. The company retains a Market Volatility (i.e., Beta) of -19.05, which attests to a somewhat significant risk relative to the market. As returns on the market increase, returns on owning Hyperscale Data, are expected to decrease by larger amounts. On the other hand, during market turmoil, Hyperscale Data, is expected to outperform it. At this point, Hyperscale Data, has a negative expected return of -0.0784%. Please make sure to check out Hyperscale Data,'s potential upside, as well as the relationship between the accumulation distribution and period momentum indicator , to decide if Hyperscale Data, performance from the past will be repeated at some point in the near future.
Auto-correlation | 0.20 |
Weak predictability
Hyperscale Data, has weak predictability. Overlapping area represents the amount of predictability between Hyperscale Data, time series from 27th of October 2024 to 11th of November 2024 and 11th of November 2024 to 26th 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 Hyperscale Data, price movement. The serial correlation of 0.2 indicates that over 20.0% of current Hyperscale Data, price fluctuation can be explain by its past prices.
| Correlation Coefficient | 0.2 | |
| Spearman Rank Test | -0.51 | |
| Residual Average | 0.0 | |
| Price Variance | 0.24 |
Hyperscale Data, lagged returns against current returns
Autocorrelation, which is Hyperscale Data, stock'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 Hyperscale Data,'s stock expected returns. We can calculate the autocorrelation of Hyperscale Data, returns to help us make a trade decision. For example, suppose you find that Hyperscale Data, has exhibited high autocorrelation historically, and you observe that the stock 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 |
Hyperscale Data, 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 Hyperscale Data, stock is displaying a positive serial correlation, investors will expect a positive pattern to continue. However, if Hyperscale Data, stock is observed to have a negative serial correlation, investors will generally project negative sentiment on having a locked-in long position in Hyperscale Data, stock over time.
Current vs Lagged Prices |
| Timeline |
Hyperscale Data, Lagged Returns
When evaluating Hyperscale Data,'s market value, investors can use the concept of autocorrelation to see how much of an impact past prices of Hyperscale Data, stock have on its future price. Hyperscale Data, 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, Hyperscale Data, autocorrelation shows the relationship between Hyperscale Data, stock current value and its past values and can show if there is a momentum factor associated with investing in Hyperscale Data,.
Regressed Prices |
| Timeline |
Thematic Opportunities
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Additional Tools for Hyperscale Stock Analysis
When running Hyperscale Data,'s price analysis, check to measure Hyperscale Data,'s market volatility, profitability, liquidity, solvency, efficiency, growth potential, financial leverage, and other vital indicators. We have many different tools that can be utilized to determine how healthy Hyperscale Data, is operating at the current time. Most of Hyperscale Data,'s value examination focuses on studying past and present price action to predict the probability of Hyperscale Data,'s future price movements. You can analyze the entity against its peers and the financial market as a whole to determine factors that move Hyperscale Data,'s price. Additionally, you may evaluate how the addition of Hyperscale Data, to your portfolios can decrease your overall portfolio volatility.