CREDIT SUISSE Mutual Fund Forward View - Polynomial Regression
| CSQIX Fund | USD 8.20 0.02 0.24% |
The Polynomial Regression forecast shown here for CREDIT SUISSE is reference data produced from its historical price series. The projected value and error measures below serve as reference information. This data is provided for reference and analytical review. The Polynomial Regression output serves as one input among many for analytical review.
The Polynomial Regression forecasted value of Credit Suisse Multialternative on the next trading day is expected to be 8.00 with a mean absolute deviation of 0.06 and the sum of the absolute errors of 3.67.A single variable polynomial regression model attempts to put a curve through the CREDIT SUISSE historical price points. Mathematically, assuming the independent variable is X and the dependent variable is Y, this line can be indicated as: Y = a0 + a1*X + a2*X2 + a3*X3 + ... + am*Xm This Polynomial Regression reference page for CREDIT SUISSE presents model-generated projections from historical price data for informational purposes. Polynomial Regression Price Forecast For the 27th of March
Given 90 days horizon, the Polynomial Regression forecasted value of Credit Suisse Multialternative on the next trading day is expected to be 8.00 with a mean absolute deviation of 0.06 , mean absolute percentage error of 0.0049 , and the sum of the absolute errors of 3.67 .Please note that although there have been many attempts to predict CREDIT Mutual Fund prices using its time series forecasting, we generally do not suggest using it to place bets in the real market. The most commonly used models for forecasting predictions are the autoregressive models, which specify that CREDIT SUISSE's next future price depends linearly on its previous prices and some stochastic term (i.e., imperfectly predictable multiplier).
Mutual Fund Forecast Pattern
| Backtest CREDIT SUISSE | CREDIT SUISSE Price Prediction | Research Analysis |
Forecasted Value
For the next trading day, Macroaxis evaluates CREDIT SUISSE's predictive range by looking for statistically meaningful downside and upside boundaries. Used properly, these levels provide context around forecast dispersion rather than certainty about the next closing print.
Model Predictive Factors
The below table displays some essential indicators generated by the model showing the Polynomial Regression forecasting method's relative quality and the estimations of the prediction error of CREDIT SUISSE mutual fund data series using in forecasting. Note that when a statistical model is used to represent CREDIT SUISSE mutual fund, the representation will rarely be exact; so some information will be lost using the model to explain the process. AIC estimates the relative amount of information lost by a given model: the less information a model loses, the higher its quality.| AIC | Akaike Information Criteria | 114.6334 |
| Bias | Arithmetic mean of the errors | None |
| MAD | Mean absolute deviation | 0.0593 |
| MAPE | Mean absolute percentage error | 0.0072 |
| SAE | Sum of the absolute errors | 3.6741 |
Other Forecasting Options for CREDIT SUISSE
The distribution of CREDIT SUISSE's daily returns is typically non-normal, with fatter tails than a Gaussian model predicts. This can reveal hidden support and resistance zones in CREDIT SUISSE's chart that simple price charts miss. The slope of CREDIT SUISSE's linear regression channel quantifies trend direction and strength over a chosen lookback period. Divergences between OBV and price can foreshadow trend changes in CREDIT.CREDIT SUISSE Related Equities
Sizing up CREDIT SUISSE against these stocks within the Multistrategy space shows how it compares on key financial measures. Return on equity across these peers shows how well each firm turns capital into profit. When CREDIT SUISSE breaks from its peer group on a key metric, it often signals a firm-level change worth exploring.
| Risk & Return | Correlation |
CREDIT SUISSE Market Strength Events
Market strength indicators for CREDIT SUISSE give insight into the mutual fund's responsiveness to broader forces. These indicators are useful for traders seeking optimal timing for positions in Credit Suisse Multialternative. Market strength analysis for Credit Suisse Multialternative works best when combined with volume and volatility data. For CREDIT SUISSE, strength indicators are a practical complement to price and fundamental analysis.
| Rate Of Daily Change | 1.0 | |||
| Day Median Price | 8.2 | |||
| Day Typical Price | 8.2 | |||
| Price Action Indicator | 0.01 | |||
| Period Momentum Indicator | 0.02 |
CREDIT SUISSE Risk Indicators
A thorough review of CREDIT SUISSE's risk indicators is an important first step in forecasting its price. Quantifying the risk involved in CREDIT SUISSE's allows investors to make better decisions about entry, sizing, and hedging. The assessment of CREDIT SUISSE's risk indicators plays a key role in managing investment exposure. Identifying the magnitude of risk in CREDIT SUISSE's provides context to choose between accepting or hedging exposure.
| Mean Deviation | 0.4176 | |||
| Semi Deviation | 0.6322 | |||
| Standard Deviation | 0.5642 | |||
| Variance | 0.3183 | |||
| Downside Variance | 0.5817 | |||
| Semi Variance | 0.3997 | |||
| Expected Short fall | -0.42 |
Please note, the risk measures we provide can be used independently or collectively to perform a risk assessment. When comparing two potential investments, we recommend comparing similar equities with homogenous growth potential and valuation from related markets to determine which investment holds the most risk.
Story Coverage note for CREDIT SUISSE
The amount of media and story coverage tied to Credit Suisse Multialternative can signal where market attention is concentrating at the moment. The stronger process compares story flow with performance, theme classification, and the level of short-term market interest.
Other Macroaxis Stories
Macroaxis story coverage is designed for a broad investing audience that ranges from self-directed traders to advisers, researchers, and institutional market participants. The content is intended to support people who want a more structured path from headline information to portfolio action.