Bmo Mid Term Ig Etf Market Value
| ZIC Etf | CAD 17.32 0.05 0.29% |
| Symbol | BMO |
BMO Mid '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 Mid'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 Mid.
| 02/20/2024 |
| 05/20/2024 |
If you would invest 0.00 in BMO Mid on February 20, 2024 and sell it all today you would earn a total of 0.00 from holding BMO Mid Term IG or generate 0.0% return on investment in BMO Mid over 90 days. BMO Mid is related to or competes with BMO Mid, BMO Mid, and BMO Long. IG Corporate Bond Index ETF has been designed to replicate, to the extent possible, the performance of the Bloomberg Bar... More
BMO Mid 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 Mid'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 Mid Term IG upside and downside potential and time the market with a certain degree of confidence.
| Downside Deviation | 0.2925 | |||
| Information Ratio | (0.26) | |||
| Maximum Drawdown | 1.69 | |||
| Value At Risk | (0.41) | |||
| Potential Upside | 0.4667 |
BMO Mid Market Risk Indicators
Today, many novice investors tend to focus exclusively on investment returns with little concern for BMO Mid's investment risk. Other traders do consider volatility but use just one or two very conventional indicators such as BMO Mid's standard deviation. In reality, there are many statistical measures that can use BMO Mid historical prices to predict the future BMO Mid's volatility.| Risk Adjusted Performance | 0.0344 | |||
| Jensen Alpha | 0.0062 | |||
| Total Risk Alpha | (0.03) | |||
| Sortino Ratio | (0.26) | |||
| Treynor Ratio | 0.182 |
Sophisticated investors, who have witnessed many market ups and downs, anticipate that the market will even out over time. This tendency of BMO Mid'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 Mid Term Backtested Returns
We consider BMO Mid very steady. BMO Mid Term secures Sharpe Ratio (or Efficiency) of 0.0758, which signifies that the etf had a 0.0758% return per unit of risk over the last 3 months. We have found twenty-nine technical indicators for BMO Mid Term IG, which you can use to evaluate the volatility of the entity. Please confirm BMO Mid's risk adjusted performance of 0.0344, and Mean Deviation of 0.2203 to double-check if the risk estimate we provide is consistent with the expected return of 0.0222%. The etf shows a Beta (market volatility) of 0.0672, which signifies not very significant fluctuations relative to the market. As returns on the market increase, BMO Mid's returns are expected to increase less than the market. However, during the bear market, the loss of holding BMO Mid is expected to be smaller as well.
Auto-correlation | 0.31 |
Below average predictability
BMO Mid Term IG has below average predictability. Overlapping area represents the amount of predictability between BMO Mid time series from 20th of February 2024 to 5th of April 2024 and 5th of April 2024 to 20th of May 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 Mid Term price movement. The serial correlation of 0.31 indicates that nearly 31.0% of current BMO Mid price fluctuation can be explain by its past prices.
| Correlation Coefficient | 0.31 | |
| Spearman Rank Test | 0.33 | |
| Residual Average | 0.0 | |
| Price Variance | 0.01 |
BMO Mid Term lagged returns against current returns
Autocorrelation, which is BMO Mid 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 Mid's etf expected returns. We can calculate the autocorrelation of BMO Mid returns to help us make a trade decision. For example, suppose you find that BMO Mid 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 Mid 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 Mid etf is displaying a positive serial correlation, investors will expect a positive pattern to continue. However, if BMO Mid etf is observed to have a negative serial correlation, investors will generally project negative sentiment on having a locked-in long position in BMO Mid etf over time.
Current vs Lagged Prices |
| Timeline |
BMO Mid Lagged Returns
When evaluating BMO Mid's market value, investors can use the concept of autocorrelation to see how much of an impact past prices of BMO Mid etf have on its future price. BMO Mid 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 Mid autocorrelation shows the relationship between BMO Mid etf current value and its past values and can show if there is a momentum factor associated with investing in BMO Mid Term IG.
Regressed Prices |
| Timeline |
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BMO Mid 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.