Bank Of New Stock Market Value
| BK Stock | USD 56.29 1.04 1.88% |
| Symbol | Bank |
Bank of New York Price To Book Ratio
Is Bank of New York's industry 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 Bank of New York. If investors know Bank 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 Bank of New York listed above have to be considered, but the key to understanding future value is determining which factors weigh more heavily than others.
Quarterly Earnings Growth (0.68) | Dividend Share 1.58 | Earnings Share 3.87 | Revenue Per Share 22.17 | Quarterly Revenue Growth 0.084 |
The market value of Bank of New York is measured differently than its book value, which is the value of Bank that is recorded on the company's balance sheet. Investors also form their own opinion of Bank of New York's value that differs from its market value or its book value, called intrinsic value, which is Bank of New York'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 Bank of New York's market value can be influenced by many factors that don't directly affect Bank of New York'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 Bank of New York's value and its price as these two are different measures arrived at by different means. Investors typically determine if Bank of New York is a good investment by looking at such factors as earnings, sales, fundamental and technical indicators, competition as well as analyst projections. However, Bank of New York'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.
Bank of New York '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 Bank of New York'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 Bank of New York.
| 03/20/2024 |
| 04/19/2024 |
If you would invest 0.00 in Bank of New York on March 20, 2024 and sell it all today you would earn a total of 0.00 from holding Bank of New or generate 0.0% return on investment in Bank of New York over 30 days. Bank of New York is related to or competes with Northern Trust, Invesco Plc, Franklin Resources, T Rowe, State Street, SEI Investments, and Principal Financial. The Bank of New York Mellon Corporation provides a range of financial products and services in the United States and int... More
Bank of New York 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 Bank of New York'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 Bank of New upside and downside potential and time the market with a certain degree of confidence.
| Downside Deviation | 1.17 | |||
| Information Ratio | (0.06) | |||
| Maximum Drawdown | 4.67 | |||
| Value At Risk | (1.54) | |||
| Potential Upside | 1.49 |
Bank of New York Market Risk Indicators
Today, many novice investors tend to focus exclusively on investment returns with little concern for Bank of New York's investment risk. Other traders do consider volatility but use just one or two very conventional indicators such as Bank of New York's standard deviation. In reality, there are many statistical measures that can use Bank of New York historical prices to predict the future Bank of New York's volatility.| Risk Adjusted Performance | 0.0117 | |||
| Jensen Alpha | (0.06) | |||
| Total Risk Alpha | (0.09) | |||
| Sortino Ratio | (0.05) | |||
| Treynor Ratio | 0.0026 |
Sophisticated investors, who have witnessed many market ups and downs, anticipate that the market will even out over time. This tendency of Bank of New York'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.
Bank of New York Backtested Returns
We consider Bank of New York very steady. Bank of New York secures Sharpe Ratio (or Efficiency) of 0.0175, which signifies that the company had a 0.0175% return per unit of risk over the last 3 months. We have found twenty-eight technical indicators for Bank of New, which you can use to evaluate the volatility of the firm. Please confirm Bank of New York's Risk Adjusted Performance of 0.0117, downside deviation of 1.17, and Mean Deviation of 0.7065 to double-check if the risk estimate we provide is consistent with the expected return of 0.0169%. Bank of New York has a performance score of 1 on a scale of 0 to 100. The firm shows a Beta (market volatility) of 1.0, which signifies a somewhat significant risk relative to the market. Bank of New York returns are very sensitive to returns on the market. As the market goes up or down, Bank of New York is expected to follow. Bank of New York right now shows a risk of 0.97%. Please confirm Bank of New York downside variance, daily balance of power, and the relationship between the maximum drawdown and skewness , to decide if Bank of New York will be following its price patterns.
Auto-correlation | -0.62 |
Very good reverse predictability
Bank of New has very good reverse predictability. Overlapping area represents the amount of predictability between Bank of New York time series from 20th of March 2024 to 4th of April 2024 and 4th of April 2024 to 19th of April 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 Bank of New York price movement. The serial correlation of -0.62 indicates that roughly 62.0% of current Bank of New York price fluctuation can be explain by its past prices.
| Correlation Coefficient | -0.62 | |
| Spearman Rank Test | -0.1 | |
| Residual Average | 0.0 | |
| Price Variance | 1.07 |
Bank of New York lagged returns against current returns
Autocorrelation, which is Bank of New York 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 Bank of New York's stock expected returns. We can calculate the autocorrelation of Bank of New York returns to help us make a trade decision. For example, suppose you find that Bank of New York 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 |
Bank of New York 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 Bank of New York stock is displaying a positive serial correlation, investors will expect a positive pattern to continue. However, if Bank of New York stock is observed to have a negative serial correlation, investors will generally project negative sentiment on having a locked-in long position in Bank of New York stock over time.
Current vs Lagged Prices |
| Timeline |
Bank of New York Lagged Returns
When evaluating Bank of New York's market value, investors can use the concept of autocorrelation to see how much of an impact past prices of Bank of New York stock have on its future price. Bank of New York 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, Bank of New York autocorrelation shows the relationship between Bank of New York stock current value and its past values and can show if there is a momentum factor associated with investing in Bank of New.
Regressed Prices |
| Timeline |
Pair Trading with Bank of New York
One of the main advantages of trading using pair correlations is that every trade hedges away some risk. Because there are two separate transactions required, even if Bank of New York position performs unexpectedly, the other equity can make up some of the losses. Pair trading also minimizes risk from directional movements in the market. For example, if an entire industry or sector drops because of unexpected headlines, the short position in Bank of New York will appreciate offsetting losses from the drop in the long position's value.Moving together with Bank Stock
| 0.64 | BX | Blackstone Group Financial Report 18th of July 2024 | PairCorr |
| 0.78 | GS | Goldman Sachs Group Financial Report 17th of July 2024 | PairCorr |
The ability to find closely correlated positions to Bank of New York could be a great tool in your tax-loss harvesting strategies, allowing investors a quick way to find a similar-enough asset to replace Bank of New York when you sell it. If you don't do this, your portfolio allocation will be skewed against your target asset allocation. So, investors can't just sell and buy back Bank of New York - that would be a violation of the tax code under the "wash sale" rule, and this is why you need to find a similar enough asset and use the proceeds from selling Bank of New to buy it.
The correlation of Bank of New York is a statistical measure of how it moves in relation to other instruments. This measure is expressed in what is known as the correlation coefficient, which ranges between -1 and +1. A perfect positive correlation (i.e., a correlation coefficient of +1) implies that as Bank of New York moves, either up or down, the other security will move in the same direction. Alternatively, perfect negative correlation means that if Bank of New York moves in either direction, the perfectly negatively correlated security will move in the opposite direction. If the correlation is 0, the equities are not correlated; they are entirely random. A correlation greater than 0.8 is generally described as strong, whereas a correlation less than 0.5 is generally considered weak.
Correlation analysis and pair trading evaluation for Bank of New York can also be used as hedging techniques within a particular sector or industry or even over random equities to generate a better risk-adjusted return on your portfolios.Check out Bank of New York Correlation, Bank of New York Volatility and Bank of New York Alpha and Beta module to complement your research on Bank of New York. You can also try the Financial Widgets module to easily integrated Macroaxis content with over 30 different plug-and-play financial widgets.
Complementary Tools for Bank Stock analysis
When running Bank of New York's price analysis, check to measure Bank of New York'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 Bank of New York is operating at the current time. Most of Bank of New York's value examination focuses on studying past and present price action to predict the probability of Bank of New York's future price movements. You can analyze the entity against its peers and the financial market as a whole to determine factors that move Bank of New York's price. Additionally, you may evaluate how the addition of Bank of New York to your portfolios can decrease your overall portfolio volatility.
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Bank of New York technical stock 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, stock market cycles, or different charting patterns.