Mfs Mid Cap Fund Market Value
| OTCJX Fund | USD 29.50 0.49 1.69% |
| Symbol | Mfs |
Mfs 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 Mfs Mid'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 Mid.
| 03/25/2024 |
| 04/24/2024 |
If you would invest 0.00 in Mfs Mid on March 25, 2024 and sell it all today you would earn a total of 0.00 from holding Mfs Mid Cap or generate 0.0% return on investment in Mfs Mid over 30 days. Mfs Mid is related to or competes with HUMANA, Barloworld, Morningstar Unconstrained, High Yield, Thrivent High, Via Renewables, and T Rowe. The fund invests at least 80 percent of the funds net assets in issuers with medium market capitalizations More
Mfs 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 Mfs Mid'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 Mid Cap upside and downside potential and time the market with a certain degree of confidence.
| Downside Deviation | 0.9399 | |||
| Information Ratio | (0.04) | |||
| Maximum Drawdown | 3.86 | |||
| Value At Risk | (1.35) | |||
| Potential Upside | 1.65 |
Mfs Mid Market Risk Indicators
Today, many novice investors tend to focus exclusively on investment returns with little concern for Mfs Mid's investment risk. Other traders do consider volatility but use just one or two very conventional indicators such as Mfs Mid's standard deviation. In reality, there are many statistical measures that can use Mfs Mid historical prices to predict the future Mfs Mid's volatility.| Risk Adjusted Performance | 0.0456 | |||
| Jensen Alpha | (0.05) | |||
| Total Risk Alpha | (0.07) | |||
| Sortino Ratio | (0.04) | |||
| Treynor Ratio | 0.0436 |
Sophisticated investors, who have witnessed many market ups and downs, anticipate that the market will even out over time. This tendency of Mfs 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.
Mfs Mid Cap Backtested Returns
We consider Mfs Mid out of control. Mfs Mid Cap has Sharpe Ratio of 0.0599, which conveys that the entity had a 0.0599% return per unit of risk over the last 3 months. We have found twenty-seven technical indicators for Mfs Mid, which you can use to evaluate the volatility of the fund. Please verify Mfs Mid's Mean Deviation of 0.7313, risk adjusted performance of 0.0456, and Downside Deviation of 0.9399 to check out if the risk estimate we provide is consistent with the expected return of 0.055%. The fund secures a Beta (Market Risk) of 1.18, which conveys a somewhat significant risk relative to the market. As the market goes up, the company is expected to outperform it. However, if the market returns are negative, Mfs Mid will likely underperform.
Auto-correlation | 0.56 |
Modest predictability
Mfs Mid Cap has modest predictability. Overlapping area represents the amount of predictability between Mfs Mid time series from 25th of March 2024 to 9th of April 2024 and 9th of April 2024 to 24th 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 Mfs Mid Cap price movement. The serial correlation of 0.56 indicates that roughly 56.0% of current Mfs Mid price fluctuation can be explain by its past prices.
| Correlation Coefficient | 0.56 | |
| Spearman Rank Test | 0.66 | |
| Residual Average | 0.0 | |
| Price Variance | 0.47 |
Mfs Mid Cap lagged returns against current returns
Autocorrelation, which is Mfs Mid 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 Mid's mutual fund expected returns. We can calculate the autocorrelation of Mfs Mid returns to help us make a trade decision. For example, suppose you find that Mfs Mid 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 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 Mfs Mid mutual fund is displaying a positive serial correlation, investors will expect a positive pattern to continue. However, if Mfs Mid 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 Mid mutual fund over time.
Current vs Lagged Prices |
| Timeline |
Mfs Mid Lagged Returns
When evaluating Mfs Mid's market value, investors can use the concept of autocorrelation to see how much of an impact past prices of Mfs Mid mutual fund have on its future price. Mfs 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, Mfs Mid autocorrelation shows the relationship between Mfs Mid mutual fund current value and its past values and can show if there is a momentum factor associated with investing in Mfs Mid Cap.
Regressed Prices |
| Timeline |
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Mfs Mid 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.