Bmo Nasdaq 100 Etf Market Value
| ZQQ Etf | CAD 123.14 0.42 0.34% |
| Symbol | BMO |
BMO NASDAQ '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 BMO NASDAQ'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 BMO NASDAQ.
| 05/06/2022 |
| 04/25/2024 |
If you would invest 0.00 in BMO NASDAQ on May 6, 2022 and sell it all today you would earn a total of 0.00 from holding BMO NASDAQ 100 or generate 0.0% return on investment in BMO NASDAQ over 720 days. BMO NASDAQ is related to or competes with BMO SP, IShares NASDAQ, BMO SPTSX, IShares SPTSX, and IShares Core. BMO Nasdaq 100 Equity Index ETF seeks to replicate, to the extent possible, the performance of a NASDAQ listed companies... More
BMO NASDAQ 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 BMO NASDAQ'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 BMO NASDAQ 100 upside and downside potential and time the market with a certain degree of confidence.
| Downside Deviation | 1.07 | |||
| Information Ratio | (0.07) | |||
| Maximum Drawdown | 5.18 | |||
| Value At Risk | (1.70) | |||
| Potential Upside | 1.6 |
BMO NASDAQ Market Risk Indicators
Today, many novice investors tend to focus exclusively on investment returns with little concern for BMO NASDAQ's investment risk. Other traders do consider volatility but use just one or two very conventional indicators such as BMO NASDAQ's standard deviation. In reality, there are many statistical measures that can use BMO NASDAQ historical prices to predict the future BMO NASDAQ's volatility.| Risk Adjusted Performance | 0.0127 | |||
| Jensen Alpha | (0.08) | |||
| Total Risk Alpha | (0.13) | |||
| Sortino Ratio | (0.07) | |||
| Treynor Ratio | 0.0043 |
Sophisticated investors, who have witnessed many market ups and downs, anticipate that the market will even out over time. This tendency of BMO NASDAQ'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.
BMO NASDAQ 100 Backtested Returns
We consider BMO NASDAQ very steady. BMO NASDAQ 100 secures Sharpe Ratio (or Efficiency) of 0.0137, which signifies that the etf had a 0.0137% return per unit of risk over the last 3 months. We have found twenty-nine technical indicators for BMO NASDAQ 100, which you can use to evaluate the volatility of the entity. Please confirm BMO NASDAQ's mean deviation of 0.8517, and Risk Adjusted Performance of 0.0127 to double-check if the risk estimate we provide is consistent with the expected return of 0.0147%. The etf shows a Beta (market volatility) of 1.09, which signifies a somewhat significant risk relative to the market. BMO NASDAQ returns are very sensitive to returns on the market. As the market goes up or down, BMO NASDAQ is expected to follow.
Auto-correlation | 0.23 |
Weak predictability
BMO NASDAQ 100 has weak predictability. Overlapping area represents the amount of predictability between BMO NASDAQ time series from 6th of May 2022 to 1st of May 2023 and 1st of May 2023 to 25th 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 BMO NASDAQ 100 price movement. The serial correlation of 0.23 indicates that over 23.0% of current BMO NASDAQ price fluctuation can be explain by its past prices.
| Correlation Coefficient | 0.23 | |
| Spearman Rank Test | 0.08 | |
| Residual Average | 0.0 | |
| Price Variance | 94.0 |
BMO NASDAQ 100 lagged returns against current returns
Autocorrelation, which is BMO NASDAQ 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 BMO NASDAQ's etf expected returns. We can calculate the autocorrelation of BMO NASDAQ returns to help us make a trade decision. For example, suppose you find that BMO NASDAQ 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 |
BMO NASDAQ 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 BMO NASDAQ etf is displaying a positive serial correlation, investors will expect a positive pattern to continue. However, if BMO NASDAQ etf is observed to have a negative serial correlation, investors will generally project negative sentiment on having a locked-in long position in BMO NASDAQ etf over time.
Current vs Lagged Prices |
| Timeline |
BMO NASDAQ Lagged Returns
When evaluating BMO NASDAQ's market value, investors can use the concept of autocorrelation to see how much of an impact past prices of BMO NASDAQ etf have on its future price. BMO NASDAQ 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, BMO NASDAQ autocorrelation shows the relationship between BMO NASDAQ etf current value and its past values and can show if there is a momentum factor associated with investing in BMO NASDAQ 100.
Regressed Prices |
| Timeline |
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BMO NASDAQ 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.