Matthew Ryan - Mfs Emerging Fund Manager
EMLKX Fund | USD 5.36 0.02 0.37% |
Matthew W. Ryan, CFA, is an investment officer of Massachusetts Financial Services Company and a portfolio manager of the firms emerging market debt, strategic income, and highyield bond portfolios. Before joining the firm in 1997, Matt worked for four years as an economist at the International Monetary Fund and for five years as an international economist with the U.S. Department of the Treasury. He was named a portfolio manager at MFS in 1998. He is a graduate of Williams College and earned a masters degree in international economics and foreign policy from Johns Hopkins University. Matt also holds the Chartered Financial Analyst designation.
Phone | 800-225-2606 |
Mfs Emerging Management Performance (%)
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Mfs Fund Performance Indicators
The ability to make a profit is the ultimate goal of any investor. But to identify the right mutual fund is not an easy task. Is Mfs Emerging a good investment? Although profit is still the single most important financial element of any organization, multiple performance indicators can help investors identify the equity that they will appreciate over time.
Total Asset | 445.73 M | ||||
Annual Yield | 0.05 % | ||||
Year To Date Return | 13.08 % | ||||
One Year Return | 0.31 % | ||||
Three Year Return | (2.17) % | ||||
Five Year Return | (0.06) % | ||||
Net Asset | 325.92 M | ||||
Last Dividend Paid | 0.02 | ||||
Cash Position Weight | 8.70 % | ||||
Bond Positions Weight | 87.17 % |
Pair Trading with Mfs Emerging
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 Mfs Emerging 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 Mfs Emerging will appreciate offsetting losses from the drop in the long position's value.The ability to find closely correlated positions to Mfs Emerging could be a great tool in your tax-loss harvesting strategies, allowing investors a quick way to find a similar-enough asset to replace Mfs Emerging 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 Mfs Emerging - 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 Mfs Emerging Markets to buy it.
The correlation of Mfs Emerging 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 Mfs Emerging moves, either up or down, the other security will move in the same direction. Alternatively, perfect negative correlation means that if Mfs Emerging Markets 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 Mfs Emerging 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 Investing Opportunities to better understand how to build diversified portfolios, which includes a position in Mfs Emerging Markets. Also, note that the market value of any mutual fund could be tightly coupled with the direction of predictive economic indicators such as signals in industry. You can also try the Watchlist Optimization module to optimize watchlists to build efficient portfolios or rebalance existing positions based on the mean-variance optimization algorithm.