Managed Volatility Fund Market Value
| BRBPX Fund | USD 16.66 0.01 0.06% |
| Symbol | Managed |
Managed Volatility '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 Managed Volatility's mutual fund 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 Managed Volatility.
| 05/06/2022 |
| 04/25/2024 |
If you would invest 0.00 in Managed Volatility on May 6, 2022 and sell it all today you would earn a total of 0.00 from holding Managed Volatility Fund or generate 0.0% return on investment in Managed Volatility over 720 days. Managed Volatility is related to or competes with Gateway Fund, and Jpmorgan Hedged. Up to 75 percent of its total assets may be invested in common stocks and options on any size companies on which options... More
Managed Volatility 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 Managed Volatility's mutual fund 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 Managed Volatility Fund upside and downside potential and time the market with a certain degree of confidence.
| Downside Deviation | 0.3786 | |||
| Information Ratio | (0.26) | |||
| Maximum Drawdown | 1.27 | |||
| Value At Risk | (0.42) | |||
| Potential Upside | 0.4266 |
Managed Volatility Market Risk Indicators
Today, many novice investors tend to focus exclusively on investment returns with little concern for Managed Volatility's investment risk. Other traders do consider volatility but use just one or two very conventional indicators such as Managed Volatility's standard deviation. In reality, there are many statistical measures that can use Managed Volatility historical prices to predict the future Managed Volatility's volatility.| Risk Adjusted Performance | 0.0608 | |||
| Jensen Alpha | (0) | |||
| Total Risk Alpha | (0.01) | |||
| Sortino Ratio | (0.17) | |||
| Treynor Ratio | 0.0748 |
Sophisticated investors, who have witnessed many market ups and downs, anticipate that the market will even out over time. This tendency of Managed Volatility'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.
Managed Volatility Backtested Returns
We consider Managed Volatility very steady. Managed Volatility has Sharpe Ratio of 0.1, which conveys that the entity had a 0.1% return per unit of risk over the last 3 months. We have found twenty-eight technical indicators for Managed Volatility, which you can use to evaluate the volatility of the fund. Please verify Managed Volatility's Downside Deviation of 0.3786, mean deviation of 0.1733, and Risk Adjusted Performance of 0.0608 to check out if the risk estimate we provide is consistent with the expected return of 0.0267%. The fund secures a Beta (Market Risk) of 0.28, which conveys not very significant fluctuations relative to the market. As returns on the market increase, Managed Volatility's returns are expected to increase less than the market. However, during the bear market, the loss of holding Managed Volatility is expected to be smaller as well.
Auto-correlation | 0.87 |
Very good predictability
Managed Volatility Fund has very good predictability. Overlapping area represents the amount of predictability between Managed Volatility 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 Managed Volatility price movement. The serial correlation of 0.87 indicates that approximately 87.0% of current Managed Volatility price fluctuation can be explain by its past prices.
| Correlation Coefficient | 0.87 | |
| Spearman Rank Test | 0.88 | |
| Residual Average | 0.0 | |
| Price Variance | 0.48 |
Managed Volatility lagged returns against current returns
Autocorrelation, which is Managed Volatility mutual fund'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 Managed Volatility's mutual fund expected returns. We can calculate the autocorrelation of Managed Volatility returns to help us make a trade decision. For example, suppose you find that Managed Volatility has exhibited high autocorrelation historically, and you observe that the mutual fund 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 |
Managed Volatility 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 Managed Volatility mutual fund is displaying a positive serial correlation, investors will expect a positive pattern to continue. However, if Managed Volatility mutual fund is observed to have a negative serial correlation, investors will generally project negative sentiment on having a locked-in long position in Managed Volatility mutual fund over time.
Current vs Lagged Prices |
| Timeline |
Managed Volatility Lagged Returns
When evaluating Managed Volatility's market value, investors can use the concept of autocorrelation to see how much of an impact past prices of Managed Volatility mutual fund have on its future price. Managed Volatility 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, Managed Volatility autocorrelation shows the relationship between Managed Volatility mutual fund current value and its past values and can show if there is a momentum factor associated with investing in Managed Volatility Fund.
Regressed Prices |
| Timeline |
Some investors attempt to determine whether the market's mood is bullish or bearish by monitoring changes in market sentiment. Unlike more traditional methods such as technical analysis, investor sentiment usually refers to the aggregate attitude towards Managed Volatility in the overall investment community. So, suppose investors can accurately measure the market's sentiment. In that case, they can use it for their benefit. For example, some tools to gauge market sentiment could be utilized using contrarian indexes, Managed Volatility's short interest history, or implied volatility extrapolated from Managed Volatility options trading.
Pair Trading with Managed Volatility
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 Managed Volatility 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 Managed Volatility will appreciate offsetting losses from the drop in the long position's value.Moving together with Managed Mutual Fund
| 0.92 | BRAGX | Aggressive Investors | PairCorr |
| 0.79 | JHQCX | Jpmorgan Hedged Equity | PairCorr |
| 0.8 | JHEQX | Jpmorgan Hedged Equity | PairCorr |
The ability to find closely correlated positions to Managed Volatility could be a great tool in your tax-loss harvesting strategies, allowing investors a quick way to find a similar-enough asset to replace Managed Volatility 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 Managed Volatility - 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 Managed Volatility Fund to buy it.
The correlation of Managed Volatility 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 Managed Volatility moves, either up or down, the other security will move in the same direction. Alternatively, perfect negative correlation means that if Managed Volatility 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 Managed Volatility 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 Managed Volatility Correlation, Managed Volatility Volatility and Managed Volatility Alpha and Beta module to complement your research on Managed Volatility. You can also try the Instant Ratings module to determine any equity ratings based on digital recommendations. Macroaxis instant equity ratings are based on combination of fundamental analysis and risk-adjusted market performance.
Managed Volatility technical mutual fund 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, fund market cycles, or different charting patterns.