Credit Suisse Floating Fund Market Value
| CHICX Fund | USD 6.40 0.01 0.16% |
| Symbol | Credit |
Credit Suisse '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 Credit Suisse'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 Credit Suisse.
| 03/25/2024 |
| 04/24/2024 |
If you would invest 0.00 in Credit Suisse on March 25, 2024 and sell it all today you would earn a total of 0.00 from holding Credit Suisse Floating or generate 0.0% return on investment in Credit Suisse over 30 days. Credit Suisse is related to or competes with Oppenheimer Senior, Floating Rate, Floating Rate, Floating Rate, Floating Rate, Floating Rate, and Floating Rate. The fund normally invests at least 80 percent of its net assets, plus any borrowings for investment purposes, in high yi... More
Credit Suisse 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 Credit Suisse'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 Credit Suisse Floating upside and downside potential and time the market with a certain degree of confidence.
| Information Ratio | (0.49) | |||
| Maximum Drawdown | 0.9451 | |||
| Value At Risk | (0.16) | |||
| Potential Upside | 0.158 |
Credit Suisse Market Risk Indicators
Today, many novice investors tend to focus exclusively on investment returns with little concern for Credit Suisse's investment risk. Other traders do consider volatility but use just one or two very conventional indicators such as Credit Suisse's standard deviation. In reality, there are many statistical measures that can use Credit Suisse historical prices to predict the future Credit Suisse's volatility.| Risk Adjusted Performance | 0.0694 | |||
| Jensen Alpha | 0.0128 | |||
| Total Risk Alpha | (0.01) | |||
| Treynor Ratio | 0.9838 |
Sophisticated investors, who have witnessed many market ups and downs, anticipate that the market will even out over time. This tendency of Credit Suisse'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.
Credit Suisse Floating Backtested Returns
We consider Credit Suisse very steady. Credit Suisse Floating secures Sharpe Ratio (or Efficiency) of 0.17, which signifies that the fund had a 0.17% return per unit of standard deviation over the last 3 months. We have found twenty-one technical indicators for Credit Suisse Floating, which you can use to evaluate the volatility of the entity. Please confirm Credit Suisse's mean deviation of 0.0672, and Risk Adjusted Performance of 0.0694 to double-check if the risk estimate we provide is consistent with the expected return of 0.0251%. The fund shows a Beta (market volatility) of 0.0142, which signifies not very significant fluctuations relative to the market. As returns on the market increase, Credit Suisse's returns are expected to increase less than the market. However, during the bear market, the loss of holding Credit Suisse is expected to be smaller as well.
Auto-correlation | -0.83 |
Excellent reverse predictability
Credit Suisse Floating has excellent reverse predictability. Overlapping area represents the amount of predictability between Credit Suisse 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 Credit Suisse Floating price movement. The serial correlation of -0.83 indicates that around 83.0% of current Credit Suisse price fluctuation can be explain by its past prices.
| Correlation Coefficient | -0.83 | |
| Spearman Rank Test | -0.57 | |
| Residual Average | 0.0 | |
| Price Variance | 0.0 |
Credit Suisse Floating lagged returns against current returns
Autocorrelation, which is Credit Suisse 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 Credit Suisse's mutual fund expected returns. We can calculate the autocorrelation of Credit Suisse returns to help us make a trade decision. For example, suppose you find that Credit Suisse 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 |
Credit Suisse 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 Credit Suisse mutual fund is displaying a positive serial correlation, investors will expect a positive pattern to continue. However, if Credit Suisse mutual fund is observed to have a negative serial correlation, investors will generally project negative sentiment on having a locked-in long position in Credit Suisse mutual fund over time.
Current vs Lagged Prices |
| Timeline |
Credit Suisse Lagged Returns
When evaluating Credit Suisse's market value, investors can use the concept of autocorrelation to see how much of an impact past prices of Credit Suisse mutual fund have on its future price. Credit Suisse 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, Credit Suisse autocorrelation shows the relationship between Credit Suisse mutual fund current value and its past values and can show if there is a momentum factor associated with investing in Credit Suisse Floating.
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 Credit Suisse 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, Credit Suisse's short interest history, or implied volatility extrapolated from Credit Suisse options trading.
Pair Trading with Credit Suisse
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 Credit Suisse 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 Credit Suisse will appreciate offsetting losses from the drop in the long position's value.Moving together with Credit Mutual Fund
| 1.0 | CHIAX | Credit Suisse Floating | PairCorr |
| 0.74 | CRSOX | Credit Suisse Modity | PairCorr |
| 0.74 | CRSCX | Credit Suisse Modity | PairCorr |
| 0.74 | CRSAX | Credit Suisse Modity | PairCorr |
Moving against Credit Mutual Fund
| 0.45 | GAAGX | Gmo Alternative Allo | PairCorr |
| 0.44 | GAAKX | Gmo Alternative Allo | PairCorr |
The ability to find closely correlated positions to Credit Suisse could be a great tool in your tax-loss harvesting strategies, allowing investors a quick way to find a similar-enough asset to replace Credit Suisse 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 Credit Suisse - 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 Credit Suisse Floating to buy it.
The correlation of Credit Suisse 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 Credit Suisse moves, either up or down, the other security will move in the same direction. Alternatively, perfect negative correlation means that if Credit Suisse Floating 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 Credit Suisse 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 Credit Suisse Correlation, Credit Suisse Volatility and Credit Suisse Alpha and Beta module to complement your research on Credit Suisse. Note that the Credit Suisse Floating information on this page should be used as a complementary analysis to other Credit Suisse'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 Financial Widgets module to easily integrated Macroaxis content with over 30 different plug-and-play financial widgets.
Credit Suisse 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.