Beta ETF (Poland) Market Value

ETFBNDXPL   208.00  0.15  0.07%   
Beta ETF's market value is the price at which a share of Beta ETF trades on a public exchange. It measures the collective expectations of Beta ETF Nasdaq 100 investors about its performance. Beta ETF is trading at 208.00 as of the 15th of December 2024; that is 0.07% up since the beginning of the trading day. The etf's open price was 207.85.
With this module, you can estimate the performance of a buy and hold strategy of Beta ETF Nasdaq 100 and determine expected loss or profit from investing in Beta ETF over a given investment horizon. Check out Investing Opportunities to better understand how to build diversified portfolios. Also, note that the market value of any etf could be closely tied with the direction of predictive economic indicators such as signals in board of governors.
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Beta ETF '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 Beta ETF'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 Beta ETF.
0.00
11/15/2024
No Change 0.00  0.0 
In 30 days
12/15/2024
0.00
If you would invest  0.00  in Beta ETF on November 15, 2024 and sell it all today you would earn a total of 0.00 from holding Beta ETF Nasdaq 100 or generate 0.0% return on investment in Beta ETF over 30 days.

Beta ETF 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 Beta ETF'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 Beta ETF Nasdaq 100 upside and downside potential and time the market with a certain degree of confidence.

Beta ETF Market Risk Indicators

Today, many novice investors tend to focus exclusively on investment returns with little concern for Beta ETF's investment risk. Other traders do consider volatility but use just one or two very conventional indicators such as Beta ETF's standard deviation. In reality, there are many statistical measures that can use Beta ETF historical prices to predict the future Beta ETF's volatility.

Beta ETF Nasdaq Backtested Returns

At this stage we consider Beta Etf to be very steady. Beta ETF Nasdaq secures Sharpe Ratio (or Efficiency) of 0.21, which signifies that the etf had a 0.21% return per unit of risk over the last 3 months. We have found twenty-nine technical indicators for Beta ETF Nasdaq 100, which you can use to evaluate the volatility of the entity. Please confirm Beta ETF's Mean Deviation of 0.6311, risk adjusted performance of 0.1859, and Downside Deviation of 1.05 to double-check if the risk estimate we provide is consistent with the expected return of 0.18%. The etf shows a Beta (market volatility) of 0.34, which signifies possible diversification benefits within a given portfolio. As returns on the market increase, Beta ETF's returns are expected to increase less than the market. However, during the bear market, the loss of holding Beta ETF is expected to be smaller as well.

Auto-correlation

    
  0.76  

Good predictability

Beta ETF Nasdaq 100 has good predictability. Overlapping area represents the amount of predictability between Beta ETF time series from 15th of November 2024 to 30th of November 2024 and 30th of November 2024 to 15th of December 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 Beta ETF Nasdaq price movement. The serial correlation of 0.76 indicates that around 76.0% of current Beta ETF price fluctuation can be explain by its past prices.
Correlation Coefficient0.76
Spearman Rank Test0.83
Residual Average0.0
Price Variance3.95

Beta ETF Nasdaq lagged returns against current returns

Autocorrelation, which is Beta ETF 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 Beta ETF's etf expected returns. We can calculate the autocorrelation of Beta ETF returns to help us make a trade decision. For example, suppose you find that Beta ETF 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  

Beta ETF 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 Beta ETF etf is displaying a positive serial correlation, investors will expect a positive pattern to continue. However, if Beta ETF etf is observed to have a negative serial correlation, investors will generally project negative sentiment on having a locked-in long position in Beta ETF etf over time.
   Current vs Lagged Prices   
       Timeline  

Beta ETF Lagged Returns

When evaluating Beta ETF's market value, investors can use the concept of autocorrelation to see how much of an impact past prices of Beta ETF etf have on its future price. Beta ETF 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, Beta ETF autocorrelation shows the relationship between Beta ETF etf current value and its past values and can show if there is a momentum factor associated with investing in Beta ETF Nasdaq 100.
   Regressed Prices   
       Timeline  

Pair Trading with Beta ETF

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 Beta ETF 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 Beta ETF will appreciate offsetting losses from the drop in the long position's value.
The ability to find closely correlated positions to Beta ETF could be a great tool in your tax-loss harvesting strategies, allowing investors a quick way to find a similar-enough asset to replace Beta ETF 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 Beta ETF - 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 Beta ETF Nasdaq 100 to buy it.
The correlation of Beta ETF 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 Beta ETF moves, either up or down, the other security will move in the same direction. Alternatively, perfect negative correlation means that if Beta ETF Nasdaq 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 Beta ETF 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.
Pair CorrelationCorrelation Matching