Morgan Stanley China Fund Market Value
| CAF Fund | USD 12.04 0.03 0.25% |
| Symbol | Morgan |
Morgan Stanley '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 Morgan Stanley's 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 Morgan Stanley.
| 03/20/2024 |
| 04/19/2024 |
If you would invest 0.00 in Morgan Stanley on March 20, 2024 and sell it all today you would earn a total of 0.00 from holding Morgan Stanley China or generate 0.0% return on investment in Morgan Stanley over 30 days. Morgan Stanley is related to or competes with Mexico Closed, NXG NextGen, Central Europe, Japan Smaller, MFS Charter, European Equity, and Aberdeen Japan. Morgan Stanley China A Share Fund, Inc. is a closed ended equity mutual fund launched and managed by Morgan Stanley Inve... More
Morgan Stanley 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 Morgan Stanley's 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 Morgan Stanley China upside and downside potential and time the market with a certain degree of confidence.
| Information Ratio | (0.06) | |||
| Maximum Drawdown | 9.18 | |||
| Value At Risk | (1.93) | |||
| Potential Upside | 1.96 |
Morgan Stanley Market Risk Indicators
Today, many novice investors tend to focus exclusively on investment returns with little concern for Morgan Stanley's investment risk. Other traders do consider volatility but use just one or two very conventional indicators such as Morgan Stanley's standard deviation. In reality, there are many statistical measures that can use Morgan Stanley historical prices to predict the future Morgan Stanley's volatility.| Risk Adjusted Performance | (0) | |||
| Jensen Alpha | (0.06) | |||
| Total Risk Alpha | (0.16) | |||
| Treynor Ratio | (0.05) |
Sophisticated investors, who have witnessed many market ups and downs, anticipate that the market will even out over time. This tendency of Morgan Stanley'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.
Morgan Stanley China Backtested Returns
We consider Morgan Stanley not too volatile. Morgan Stanley China has Sharpe Ratio of 0.0058, which conveys that the entity had a 0.0058% return per unit of risk over the last 3 months. We have found twenty-three technical indicators for Morgan Stanley, which you can use to evaluate the volatility of the fund. Please verify Morgan Stanley's Standard Deviation of 1.37, mean deviation of 0.9826, and Risk Adjusted Performance of (0) to check out if the risk estimate we provide is consistent with the expected return of 0.008%. The fund secures a Beta (Market Risk) of 0.57, which conveys possible diversification benefits within a given portfolio. As returns on the market increase, Morgan Stanley's returns are expected to increase less than the market. However, during the bear market, the loss of holding Morgan Stanley is expected to be smaller as well.
Auto-correlation | -0.18 |
Insignificant reverse predictability
Morgan Stanley China has insignificant reverse predictability. Overlapping area represents the amount of predictability between Morgan Stanley time series from 20th of March 2024 to 4th of April 2024 and 4th of April 2024 to 19th 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 Morgan Stanley China price movement. The serial correlation of -0.18 indicates that over 18.0% of current Morgan Stanley price fluctuation can be explain by its past prices.
| Correlation Coefficient | -0.18 | |
| Spearman Rank Test | -0.28 | |
| Residual Average | 0.0 | |
| Price Variance | 0.01 |
Morgan Stanley China lagged returns against current returns
Autocorrelation, which is Morgan Stanley 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 Morgan Stanley's fund expected returns. We can calculate the autocorrelation of Morgan Stanley returns to help us make a trade decision. For example, suppose you find that Morgan Stanley has exhibited high autocorrelation historically, and you observe that the 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 |
Morgan Stanley 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 Morgan Stanley fund is displaying a positive serial correlation, investors will expect a positive pattern to continue. However, if Morgan Stanley fund is observed to have a negative serial correlation, investors will generally project negative sentiment on having a locked-in long position in Morgan Stanley fund over time.
Current vs Lagged Prices |
| Timeline |
Morgan Stanley Lagged Returns
When evaluating Morgan Stanley's market value, investors can use the concept of autocorrelation to see how much of an impact past prices of Morgan Stanley fund have on its future price. Morgan Stanley 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, Morgan Stanley autocorrelation shows the relationship between Morgan Stanley fund current value and its past values and can show if there is a momentum factor associated with investing in Morgan Stanley China.
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 Morgan Stanley 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, Morgan Stanley's short interest history, or implied volatility extrapolated from Morgan Stanley options trading.
Pair Trading with Morgan Stanley
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 Morgan Stanley 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 Morgan Stanley will appreciate offsetting losses from the drop in the long position's value.Moving against Morgan Fund
| 0.59 | FKIQX | Franklin Income | PairCorr |
| 0.59 | MSTVX | Morningstar Alternatives | PairCorr |
| 0.58 | SAEXX | State Street Institu | PairCorr |
| 0.56 | ALEFX | Alps/alerian Energy | PairCorr |
| 0.54 | FIJFX | Materials Portfolio | PairCorr |
The ability to find closely correlated positions to Morgan Stanley could be a great tool in your tax-loss harvesting strategies, allowing investors a quick way to find a similar-enough asset to replace Morgan Stanley 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 Morgan Stanley - 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 Morgan Stanley China to buy it.
The correlation of Morgan Stanley 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 Morgan Stanley moves, either up or down, the other security will move in the same direction. Alternatively, perfect negative correlation means that if Morgan Stanley China 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 Morgan Stanley 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 Morgan Stanley Correlation, Morgan Stanley Volatility and Morgan Stanley Alpha and Beta module to complement your research on Morgan Stanley. For more detail on how to invest in Morgan Fund please use our How to Invest in Morgan Stanley guide.Note that the Morgan Stanley China information on this page should be used as a complementary analysis to other Morgan Stanley'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 Fundamental Analysis module to view fundamental data based on most recent published financial statements.
Morgan Stanley technical 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.