Bny Mellon Etf Market Value
| BKUI Etf | USD 49.69 0.01 0.02% |
| Symbol | BNY |
The market value of BNY Mellon ETF is measured differently than its book value, which is the value of BNY that is recorded on the company's balance sheet. Investors also form their own opinion of BNY Mellon's value that differs from its market value or its book value, called intrinsic value, which is BNY Mellon'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 BNY Mellon's market value can be influenced by many factors that don't directly affect BNY Mellon'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 BNY Mellon's value and its price as these two are different measures arrived at by different means. Investors typically determine if BNY Mellon is a good investment by looking at such factors as earnings, sales, fundamental and technical indicators, competition as well as analyst projections. However, BNY Mellon'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.
BNY Mellon '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 BNY Mellon'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 BNY Mellon.
| 10/26/2024 |
| 11/25/2024 |
If you would invest 0.00 in BNY Mellon on October 26, 2024 and sell it all today you would earn a total of 0.00 from holding BNY Mellon ETF or generate 0.0% return on investment in BNY Mellon over 30 days. BNY Mellon is related to or competes with SPDR SSgA, and SPDR Bloomberg. The fund normally invests at least 80 percent of its net assets in investment grade, U.S More
BNY Mellon 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 BNY Mellon'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 BNY Mellon ETF upside and downside potential and time the market with a certain degree of confidence.
| Information Ratio | (4.24) | |||
| Maximum Drawdown | 0.1622 | |||
| Potential Upside | 0.0608 |
BNY Mellon Market Risk Indicators
Today, many novice investors tend to focus exclusively on investment returns with little concern for BNY Mellon's investment risk. Other traders do consider volatility but use just one or two very conventional indicators such as BNY Mellon's standard deviation. In reality, there are many statistical measures that can use BNY Mellon historical prices to predict the future BNY Mellon's volatility.| Risk Adjusted Performance | 0.2224 | |||
| Jensen Alpha | 0.0068 | |||
| Total Risk Alpha | 0.0032 | |||
| Treynor Ratio | 1.57 |
Sophisticated investors, who have witnessed many market ups and downs, anticipate that the market will even out over time. This tendency of BNY Mellon'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.
BNY Mellon ETF Backtested Returns
BNY Mellon is very steady at the moment. BNY Mellon ETF secures Sharpe Ratio (or Efficiency) of 0.65, which signifies that the etf had a 0.65% return per unit of volatility over the last 3 months. We have found twenty-three technical indicators for BNY Mellon ETF, which you can use to evaluate the volatility of the entity. Please confirm BNY Mellon's risk adjusted performance of 0.2224, and Mean Deviation of 0.0206 to double-check if the risk estimate we provide is consistent with the expected return of 0.0174%. The etf shows a Beta (market volatility) of 0.0047, which signifies not very significant fluctuations relative to the market. As returns on the market increase, BNY Mellon's returns are expected to increase less than the market. However, during the bear market, the loss of holding BNY Mellon is expected to be smaller as well.
Auto-correlation | 0.90 |
Excellent predictability
BNY Mellon ETF has excellent predictability. Overlapping area represents the amount of predictability between BNY Mellon time series from 26th of October 2024 to 10th of November 2024 and 10th of November 2024 to 25th 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 BNY Mellon ETF price movement. The serial correlation of 0.9 indicates that approximately 90.0% of current BNY Mellon price fluctuation can be explain by its past prices.
| Correlation Coefficient | 0.9 | |
| Spearman Rank Test | 0.96 | |
| Residual Average | 0.0 | |
| Price Variance | 0.0 |
BNY Mellon ETF lagged returns against current returns
Autocorrelation, which is BNY Mellon 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 BNY Mellon's etf expected returns. We can calculate the autocorrelation of BNY Mellon returns to help us make a trade decision. For example, suppose you find that BNY Mellon 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 |
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BNY Mellon 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 BNY Mellon etf is displaying a positive serial correlation, investors will expect a positive pattern to continue. However, if BNY Mellon etf is observed to have a negative serial correlation, investors will generally project negative sentiment on having a locked-in long position in BNY Mellon etf over time.
Current vs Lagged Prices |
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BNY Mellon Lagged Returns
When evaluating BNY Mellon's market value, investors can use the concept of autocorrelation to see how much of an impact past prices of BNY Mellon etf have on its future price. BNY Mellon 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, BNY Mellon autocorrelation shows the relationship between BNY Mellon etf current value and its past values and can show if there is a momentum factor associated with investing in BNY Mellon ETF.
Regressed Prices |
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Currently Active Assets on Macroaxis
When determining whether BNY Mellon ETF offers a strong return on investment in its stock, a comprehensive analysis is essential. The process typically begins with a thorough review of BNY Mellon'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 Bny Mellon Etf. Outlined below are crucial reports that will aid in making a well-informed decision on Bny Mellon Etf:Check out BNY Mellon Correlation, BNY Mellon Volatility and BNY Mellon Alpha and Beta module to complement your research on BNY Mellon. You can also try the Companies Directory module to evaluate performance of over 100,000 Stocks, Funds, and ETFs against different fundamentals.
BNY Mellon 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.