Mfs E Equity Fund Market Value
MRGAX Fund | USD 50.15 0.09 0.18% |
Symbol | Mfs |
Mfs E '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 E'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 E.
06/03/2023 |
| 03/29/2024 |
If you would invest 0.00 in Mfs E on June 3, 2023 and sell it all today you would earn a total of 0.00 from holding Mfs E Equity or generate 0.0% return on investment in Mfs E over 300 days. Mfs E is related to or competes with Blackrock High, Buffalo High, Lord Abbett, Siit High, Jpmorgan High, Msift High, and Simt High. The fund normally invests at least 80 percent of the funds net assets in equity securities More
Mfs E 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 E'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 E Equity upside and downside potential and time the market with a certain degree of confidence.
Downside Deviation | 0.6227 | |||
Information Ratio | 0.0556 | |||
Maximum Drawdown | 3.02 | |||
Value At Risk | (0.88) | |||
Potential Upside | 1.19 |
Mfs E Market Risk Indicators
Today, many novice investors tend to focus exclusively on investment returns with little concern for Mfs E's investment risk. Other traders do consider volatility but use just one or two very conventional indicators such as Mfs E's standard deviation. In reality, there are many statistical measures that can use Mfs E historical prices to predict the future Mfs E's volatility.Risk Adjusted Performance | 0.1434 | |||
Jensen Alpha | 0.1628 | |||
Total Risk Alpha | 0.0104 | |||
Sortino Ratio | 0.0604 | |||
Treynor Ratio | (70.66) |
Sophisticated investors, who have witnessed many market ups and downs, anticipate that the market will even out over time. This tendency of Mfs E'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 E Equity Backtested Returns
We consider Mfs E very steady. Mfs E Equity has Sharpe Ratio of 0.25, which conveys that the entity had a 0.25% return per unit of risk over the last 3 months. We have found twenty-seven technical indicators for Mfs E, which you can use to evaluate the volatility of the fund. Please verify Mfs E's Mean Deviation of 0.5318, downside deviation of 0.6227, and Risk Adjusted Performance of 0.1434 to check out if the risk estimate we provide is consistent with the expected return of 0.17%. The fund secures a Beta (Market Risk) of -0.0023, which conveys not very significant fluctuations relative to the market. As returns on the market increase, returns on owning Mfs E are expected to decrease at a much lower rate. During the bear market, Mfs E is likely to outperform the market.
Auto-correlation | -0.37 |
Poor reverse predictability
Mfs E Equity has poor reverse predictability. Overlapping area represents the amount of predictability between Mfs E time series from 3rd of June 2023 to 31st of October 2023 and 31st of October 2023 to 29th of March 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 E Equity price movement. The serial correlation of -0.37 indicates that just about 37.0% of current Mfs E price fluctuation can be explain by its past prices.
Correlation Coefficient | -0.37 | |
Spearman Rank Test | -0.39 | |
Residual Average | 0.0 | |
Price Variance | 6.95 |
Mfs E Equity lagged returns against current returns
Autocorrelation, which is Mfs E 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 E's mutual fund expected returns. We can calculate the autocorrelation of Mfs E returns to help us make a trade decision. For example, suppose you find that Mfs E 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 E 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 E mutual fund is displaying a positive serial correlation, investors will expect a positive pattern to continue. However, if Mfs E 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 E mutual fund over time.
Current vs Lagged Prices |
Timeline |
Mfs E Lagged Returns
When evaluating Mfs E's market value, investors can use the concept of autocorrelation to see how much of an impact past prices of Mfs E mutual fund have on its future price. Mfs E 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 E autocorrelation shows the relationship between Mfs E mutual fund current value and its past values and can show if there is a momentum factor associated with investing in Mfs E Equity.
Regressed Prices |
Timeline |
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 Mfs E Correlation, Mfs E Volatility and Mfs E Alpha and Beta module to complement your research on Mfs E. Note that the Mfs E Equity information on this page should be used as a complementary analysis to other Mfs E's statistical models used to find the right mix of equity instruments to add to your existing portfolios or create a brand new portfolio. You can also try the Portfolio Suggestion module to get suggestions outside of your existing asset allocation including your own model portfolios.
Complementary Tools for Mfs Mutual Fund analysis
When running Mfs E's price analysis, check to measure Mfs E's market volatility, profitability, liquidity, solvency, efficiency, growth potential, financial leverage, and other vital indicators. We have many different tools that can be utilized to determine how healthy Mfs E is operating at the current time. Most of Mfs E's value examination focuses on studying past and present price action to predict the probability of Mfs E's future price movements. You can analyze the entity against its peers and the financial market as a whole to determine factors that move Mfs E's price. Additionally, you may evaluate how the addition of Mfs E to your portfolios can decrease your overall portfolio volatility.
Stocks Directory Find actively traded stocks across global markets | |
Theme Ratings Determine theme ratings based on digital equity recommendations. Macroaxis theme ratings are based on combination of fundamental analysis and risk-adjusted market performance | |
Risk-Return Analysis View associations between returns expected from investment and the risk you assume | |
Watchlist Optimization Optimize watchlists to build efficient portfolios or rebalance existing positions based on the mean-variance optimization algorithm | |
Portfolio Manager State of the art Portfolio Manager to monitor and improve performance of your invested capital | |
Premium Stories Follow Macroaxis premium stories from verified contributors across different equity types, categories and coverage scope |
Mfs E 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.