Mfs Total Return Fund Market Value
| MRBGX Fund | USD 9.31 0.05 0.54% |
| Symbol | Mfs |
Mfs Total '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 Mfs Total'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 Mfs Total.
| 11/06/2023 |
| 05/04/2024 |
If you would invest 0.00 in Mfs Total on November 6, 2023 and sell it all today you would earn a total of 0.00 from holding Mfs Total Return or generate 0.0% return on investment in Mfs Total over 180 days. Mfs Total is related to or competes with Mfs Prudent, Mfs Prudent, Mfs Prudent, Mfs Prudent, Mfs Prudent, Mfs Prudent, and Mfs Lifetime. The fund normally invests at least 80 percent of its net assets in debt instruments More
Mfs Total 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 Mfs Total'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 Mfs Total Return upside and downside potential and time the market with a certain degree of confidence.
| Information Ratio | (0.28) | |||
| Maximum Drawdown | 1.71 | |||
| Value At Risk | (0.65) | |||
| Potential Upside | 0.5285 |
Mfs Total Market Risk Indicators
Today, many novice investors tend to focus exclusively on investment returns with little concern for Mfs Total's investment risk. Other traders do consider volatility but use just one or two very conventional indicators such as Mfs Total's standard deviation. In reality, there are many statistical measures that can use Mfs Total historical prices to predict the future Mfs Total's volatility.| Risk Adjusted Performance | (0.05) | |||
| Jensen Alpha | (0.05) | |||
| Total Risk Alpha | (0.07) | |||
| Treynor Ratio | (0.12) |
Sophisticated investors, who have witnessed many market ups and downs, anticipate that the market will even out over time. This tendency of Mfs Total'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.
Mfs Total Return Backtested Returns
Mfs Total Return has Sharpe Ratio of -0.0227, which conveys that the entity had a -0.0227% return per unit of risk over the last 3 months. Mfs Total exposes twenty-one different technical indicators, which can help you to evaluate volatility embedded in its price movement. Please verify Mfs Total's Standard Deviation of 0.3707, risk adjusted performance of (0.05), and Mean Deviation of 0.2927 to check out the risk estimate we provide. 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, Mfs Total's returns are expected to increase less than the market. However, during the bear market, the loss of holding Mfs Total is expected to be smaller as well.
Auto-correlation | -0.21 |
Weak reverse predictability
Mfs Total Return has weak reverse predictability. Overlapping area represents the amount of predictability between Mfs Total time series from 6th of November 2023 to 4th of February 2024 and 4th of February 2024 to 4th 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 Mfs Total Return price movement. The serial correlation of -0.21 indicates that over 21.0% of current Mfs Total price fluctuation can be explain by its past prices.
| Correlation Coefficient | -0.21 | |
| Spearman Rank Test | -0.53 | |
| Residual Average | 0.0 | |
| Price Variance | 0.01 |
Mfs Total Return lagged returns against current returns
Autocorrelation, which is Mfs Total 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 Mfs Total's mutual fund expected returns. We can calculate the autocorrelation of Mfs Total returns to help us make a trade decision. For example, suppose you find that Mfs Total 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 |
Mfs Total 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 Mfs Total mutual fund is displaying a positive serial correlation, investors will expect a positive pattern to continue. However, if Mfs Total mutual fund is observed to have a negative serial correlation, investors will generally project negative sentiment on having a locked-in long position in Mfs Total mutual fund over time.
Current vs Lagged Prices |
| Timeline |
Mfs Total Lagged Returns
When evaluating Mfs Total's market value, investors can use the concept of autocorrelation to see how much of an impact past prices of Mfs Total mutual fund have on its future price. Mfs Total 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, Mfs Total autocorrelation shows the relationship between Mfs Total mutual fund current value and its past values and can show if there is a momentum factor associated with investing in Mfs Total Return.
Regressed Prices |
| Timeline |
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Mfs Total 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.