Miller Opportunity Trust Fund Market Value
| LMOPX Fund | USD 27.92 0.06 0.21% |
| Symbol | Miller |
Miller Opportunity '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 Miller Opportunity'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 Miller Opportunity.
| 02/18/2024 |
| 04/18/2024 |
If you would invest 0.00 in Miller Opportunity on February 18, 2024 and sell it all today you would earn a total of 0.00 from holding Miller Opportunity Trust or generate 0.0% return on investment in Miller Opportunity over 60 days. Miller Opportunity is related to or competes with Miller Opportunity, Miller Income, Miller Income, Miller Income, Miller Income, Miller Opportunity, and Miller Opportunity. The fund normally makes investments that, in the portfolio managers opinion, offer the opportunity for long-term growth ... More
Miller Opportunity 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 Miller Opportunity'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 Miller Opportunity Trust upside and downside potential and time the market with a certain degree of confidence.
| Downside Deviation | 1.32 | |||
| Information Ratio | 0.0177 | |||
| Maximum Drawdown | 5.12 | |||
| Value At Risk | (1.65) | |||
| Potential Upside | 1.89 |
Miller Opportunity Market Risk Indicators
Today, many novice investors tend to focus exclusively on investment returns with little concern for Miller Opportunity's investment risk. Other traders do consider volatility but use just one or two very conventional indicators such as Miller Opportunity's standard deviation. In reality, there are many statistical measures that can use Miller Opportunity historical prices to predict the future Miller Opportunity's volatility.| Risk Adjusted Performance | 0.0456 | |||
| Jensen Alpha | 0.0667 | |||
| Total Risk Alpha | (0.02) | |||
| Sortino Ratio | 0.0157 | |||
| Treynor Ratio | (5.97) |
Sophisticated investors, who have witnessed many market ups and downs, anticipate that the market will even out over time. This tendency of Miller Opportunity'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.
Miller Opportunity Trust Backtested Returns
We consider Miller Opportunity somewhat reliable. Miller Opportunity Trust has Sharpe Ratio of 0.11, which conveys that the entity had a 0.11% return per unit of risk over the last 3 months. We have found twenty-eight technical indicators for Miller Opportunity, which you can use to evaluate the volatility of the fund. Please verify Miller Opportunity's Risk Adjusted Performance of 0.0456, downside deviation of 1.32, and Mean Deviation of 0.951 to check out if the risk estimate we provide is consistent with the expected return of 0.12%. The fund secures a Beta (Market Risk) of -0.0111, which conveys not very significant fluctuations relative to the market. As returns on the market increase, returns on owning Miller Opportunity are expected to decrease at a much lower rate. During the bear market, Miller Opportunity is likely to outperform the market.
Auto-correlation | -0.32 |
Poor reverse predictability
Miller Opportunity Trust has poor reverse predictability. Overlapping area represents the amount of predictability between Miller Opportunity time series from 18th of February 2024 to 19th of March 2024 and 19th of March 2024 to 18th 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 Miller Opportunity Trust price movement. The serial correlation of -0.32 indicates that nearly 32.0% of current Miller Opportunity price fluctuation can be explain by its past prices.
| Correlation Coefficient | -0.32 | |
| Spearman Rank Test | -0.35 | |
| Residual Average | 0.0 | |
| Price Variance | 0.39 |
Miller Opportunity Trust lagged returns against current returns
Autocorrelation, which is Miller Opportunity 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 Miller Opportunity's mutual fund expected returns. We can calculate the autocorrelation of Miller Opportunity returns to help us make a trade decision. For example, suppose you find that Miller Opportunity 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 |
Miller Opportunity 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 Miller Opportunity mutual fund is displaying a positive serial correlation, investors will expect a positive pattern to continue. However, if Miller Opportunity mutual fund is observed to have a negative serial correlation, investors will generally project negative sentiment on having a locked-in long position in Miller Opportunity mutual fund over time.
Current vs Lagged Prices |
| Timeline |
Miller Opportunity Lagged Returns
When evaluating Miller Opportunity's market value, investors can use the concept of autocorrelation to see how much of an impact past prices of Miller Opportunity mutual fund have on its future price. Miller Opportunity 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, Miller Opportunity autocorrelation shows the relationship between Miller Opportunity mutual fund current value and its past values and can show if there is a momentum factor associated with investing in Miller Opportunity Trust.
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 Miller Opportunity 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, Miller Opportunity's short interest history, or implied volatility extrapolated from Miller Opportunity options trading.
Building efficient market-beating portfolios requires time, education, and a lot of computing power!
The Portfolio Architect is an AI-driven system that provides multiple benefits to our users by leveraging cutting-edge machine learning algorithms, statistical analysis, and predictive modeling to automate the process of asset selection and portfolio construction, saving time and reducing human error for individual and institutional investors.
Try AI Portfolio ArchitectCheck out Miller Opportunity Correlation, Miller Opportunity Volatility and Miller Opportunity Alpha and Beta module to complement your research on Miller Opportunity. You can also try the Sectors module to list of equity sectors categorizing publicly traded companies based on their primary business activities.
Miller Opportunity 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.