Pacer Benchmark Data Etf Market Value
| SRVR Etf | USD 32.02 0.26 0.82% |
| Symbol | Pacer |
The market value of Pacer Benchmark Data is measured differently than its book value, which is the value of Pacer that is recorded on the company's balance sheet. Investors also form their own opinion of Pacer Benchmark's value that differs from its market value or its book value, called intrinsic value, which is Pacer Benchmark'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 Pacer Benchmark's market value can be influenced by many factors that don't directly affect Pacer Benchmark'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 Pacer Benchmark's value and its price as these two are different measures arrived at by different means. Investors typically determine if Pacer Benchmark is a good investment by looking at such factors as earnings, sales, fundamental and technical indicators, competition as well as analyst projections. However, Pacer Benchmark'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.
Pacer Benchmark '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 Pacer Benchmark'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 Pacer Benchmark.
| 10/29/2024 |
| 11/28/2024 |
If you would invest 0.00 in Pacer Benchmark on October 29, 2024 and sell it all today you would earn a total of 0.00 from holding Pacer Benchmark Data or generate 0.0% return on investment in Pacer Benchmark over 30 days. Pacer Benchmark is related to or competes with Pacer Benchmark, First Trust, Global X, and ProShares Online. The index is generally composed of equity securities of developed markets companies that derive at least 85 percent of t... More
Pacer Benchmark 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 Pacer Benchmark'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 Pacer Benchmark Data upside and downside potential and time the market with a certain degree of confidence.
| Downside Deviation | 1.03 | |||
| Information Ratio | (0.04) | |||
| Maximum Drawdown | 6.1 | |||
| Value At Risk | (1.44) | |||
| Potential Upside | 1.38 |
Pacer Benchmark Market Risk Indicators
Today, many novice investors tend to focus exclusively on investment returns with little concern for Pacer Benchmark's investment risk. Other traders do consider volatility but use just one or two very conventional indicators such as Pacer Benchmark's standard deviation. In reality, there are many statistical measures that can use Pacer Benchmark historical prices to predict the future Pacer Benchmark's volatility.| Risk Adjusted Performance | 0.0775 | |||
| Jensen Alpha | 0.0936 | |||
| Total Risk Alpha | (0.06) | |||
| Sortino Ratio | (0.03) | |||
| Treynor Ratio | (0.86) |
Sophisticated investors, who have witnessed many market ups and downs, anticipate that the market will even out over time. This tendency of Pacer Benchmark'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.
Pacer Benchmark Data Backtested Returns
Currently, Pacer Benchmark Data is very steady. Pacer Benchmark Data maintains Sharpe Ratio (i.e., Efficiency) of 0.11, which implies the entity had a 0.11% return per unit of risk over the last 3 months. We have found twenty-nine technical indicators for Pacer Benchmark Data, which you can use to evaluate the volatility of the etf. Please check Pacer Benchmark's Coefficient Of Variation of 1013.06, risk adjusted performance of 0.0775, and Semi Deviation of 0.9089 to confirm if the risk estimate we provide is consistent with the expected return of 0.1%. The etf holds a Beta of -0.096, which implies not very significant fluctuations relative to the market. As returns on the market increase, returns on owning Pacer Benchmark are expected to decrease at a much lower rate. During the bear market, Pacer Benchmark is likely to outperform the market.
Auto-correlation | -0.72 |
Almost perfect reverse predictability
Pacer Benchmark Data has almost perfect reverse predictability. Overlapping area represents the amount of predictability between Pacer Benchmark time series from 29th of October 2024 to 13th of November 2024 and 13th of November 2024 to 28th 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 Pacer Benchmark Data price movement. The serial correlation of -0.72 indicates that around 72.0% of current Pacer Benchmark price fluctuation can be explain by its past prices.
| Correlation Coefficient | -0.72 | |
| Spearman Rank Test | -0.83 | |
| Residual Average | 0.0 | |
| Price Variance | 0.3 |
Pacer Benchmark Data lagged returns against current returns
Autocorrelation, which is Pacer Benchmark 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 Pacer Benchmark's etf expected returns. We can calculate the autocorrelation of Pacer Benchmark returns to help us make a trade decision. For example, suppose you find that Pacer Benchmark 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 |
Pacer Benchmark 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 Pacer Benchmark etf is displaying a positive serial correlation, investors will expect a positive pattern to continue. However, if Pacer Benchmark etf is observed to have a negative serial correlation, investors will generally project negative sentiment on having a locked-in long position in Pacer Benchmark etf over time.
Current vs Lagged Prices |
| Timeline |
Pacer Benchmark Lagged Returns
When evaluating Pacer Benchmark's market value, investors can use the concept of autocorrelation to see how much of an impact past prices of Pacer Benchmark etf have on its future price. Pacer Benchmark 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, Pacer Benchmark autocorrelation shows the relationship between Pacer Benchmark etf current value and its past values and can show if there is a momentum factor associated with investing in Pacer Benchmark Data.
Regressed Prices |
| Timeline |
Pair Trading with Pacer Benchmark
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 Pacer Benchmark 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 Pacer Benchmark will appreciate offsetting losses from the drop in the long position's value.Moving together with Pacer Etf
The ability to find closely correlated positions to Pacer Benchmark could be a great tool in your tax-loss harvesting strategies, allowing investors a quick way to find a similar-enough asset to replace Pacer Benchmark 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 Pacer Benchmark - 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 Pacer Benchmark Data to buy it.
The correlation of Pacer Benchmark 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 Pacer Benchmark moves, either up or down, the other security will move in the same direction. Alternatively, perfect negative correlation means that if Pacer Benchmark Data 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 Pacer Benchmark 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 Pacer Benchmark Correlation, Pacer Benchmark Volatility and Pacer Benchmark Alpha and Beta module to complement your research on Pacer Benchmark. You can also try the USA ETFs module to find actively traded Exchange Traded Funds (ETF) in USA.
Pacer Benchmark 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.