Goldman Sachs Mutual Fund Forecast - Simple Regression

GCRIX Fund  USD 18.48  0.00  0.00%   
The Simple Regression forecasted value of Goldman Sachs Centrated on the next trading day is expected to be 18.48 with a mean absolute deviation of 0 and the sum of the absolute errors of 0. Goldman Mutual Fund Forecast is based on your current time horizon.
  
Simple Regression model is a single variable regression model that attempts to put a straight line through Goldman Sachs price points. This line is defined by its gradient or slope, and the point at which it intercepts the x-axis. Mathematically, assuming the independent variable is X and the dependent variable is Y, then this line can be represented as: Y = intercept + slope * X.

Goldman Sachs Simple Regression Price Forecast For the 1st of December

Given 90 days horizon, the Simple Regression forecasted value of Goldman Sachs Centrated on the next trading day is expected to be 18.48 with a mean absolute deviation of 0, mean absolute percentage error of 0, and the sum of the absolute errors of 0.
Please note that although there have been many attempts to predict Goldman Mutual Fund prices using its time series forecasting, we generally do not recommend using it to place bets in the real market. The most commonly used models for forecasting predictions are the autoregressive models, which specify that Goldman Sachs' next future price depends linearly on its previous prices and some stochastic term (i.e., imperfectly predictable multiplier).

Goldman Sachs Mutual Fund Forecast Pattern

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Goldman Sachs Forecasted Value

In the context of forecasting Goldman Sachs' Mutual Fund value on the next trading day, we examine the predictive performance of the model to find good statistically significant boundaries of downside and upside scenarios. Goldman Sachs' downside and upside margins for the forecasting period are 18.48 and 18.48, respectively. We have considered Goldman Sachs' daily market price to evaluate the above model's predictive performance. Remember, however, there is no scientific proof or empirical evidence that traditional linear or nonlinear forecasting models outperform artificial intelligence and frequency domain models to provide accurate forecasts consistently.
Market Value
18.48
18.48
Expected Value
18.48
Upside

Model Predictive Factors

The below table displays some essential indicators generated by the model showing the Simple Regression forecasting method's relative quality and the estimations of the prediction error of Goldman Sachs mutual fund data series using in forecasting. Note that when a statistical model is used to represent Goldman Sachs 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.
AICAkaike Information Criteria55.1771
BiasArithmetic mean of the errors None
MADMean absolute deviation0.0
MAPEMean absolute percentage error0.0
SAESum of the absolute errors0.0
In general, regression methods applied to historical equity returns or prices series is an area of active research. In recent decades, new methods have been developed for robust regression of price series such as Goldman Sachs Centrated historical returns. These new methods are regression involving correlated responses such as growth curves and different regression methods accommodating various types of missing data.

Predictive Modules for Goldman Sachs

There are currently many different techniques concerning forecasting the market as a whole, as well as predicting future values of individual securities such as Goldman Sachs Centrated. Regardless of method or technology, however, to accurately forecast the mutual fund market is more a matter of luck rather than a particular technique. Nevertheless, trying to predict the mutual fund market accurately is still an essential part of the overall investment decision process. Using different forecasting techniques and comparing the results might improve your chances of accuracy even though unexpected events may often change the market sentiment and impact your forecasting results.
Hype
Prediction
LowEstimatedHigh
18.4818.4818.48
Details
Intrinsic
Valuation
LowRealHigh
18.4818.4818.48
Details
Bollinger
Band Projection (param)
LowMiddleHigh
18.4818.4818.48
Details

Other Forecasting Options for Goldman Sachs

For every potential investor in Goldman, whether a beginner or expert, Goldman Sachs' price movement is the inherent factor that sparks whether it is viable to invest in it or hold it better. Goldman Mutual Fund price charts are filled with many 'noises.' These noises can hugely alter the decision one can make regarding investing in Goldman. Basic forecasting techniques help filter out the noise by identifying Goldman Sachs' price trends.

Goldman Sachs Related Equities

One of the popular trading techniques among algorithmic traders is to use market-neutral strategies where every trade hedges away some risk. Because there are two separate transactions required, even if one position performs unexpectedly, the other equity can make up some of the losses. Below are some of the equities that can be combined with Goldman Sachs mutual fund to make a market-neutral strategy. Peer analysis of Goldman Sachs could also be used in its relative valuation, which is a method of valuing Goldman Sachs by comparing valuation metrics with similar companies.
 Risk & Return  Correlation

Goldman Sachs Centrated Technical and Predictive Analytics

The mutual fund market is financially volatile. Despite the volatility, there exist limitless possibilities of gaining profits and building passive income portfolios. With the complexity of Goldman Sachs' price movements, a comprehensive understanding of forecasting methods that an investor can rely on to make the right move is invaluable. These methods predict trends that assist an investor in predicting the movement of Goldman Sachs' current price.

Goldman Sachs Market Strength Events

Market strength indicators help investors to evaluate how Goldman Sachs mutual fund reacts to ongoing and evolving market conditions. The investors can use it to make informed decisions about market timing, and determine when trading Goldman Sachs shares will generate the highest return on investment. By undertsting and applying Goldman Sachs mutual fund market strength indicators, traders can identify Goldman Sachs Centrated entry and exit signals to maximize returns.

Also Currently Popular

Analyzing currently trending equities could be an opportunity to develop a better portfolio based on different market momentums that they can trigger. Utilizing the top trending stocks is also useful when creating a market-neutral strategy or pair trading technique involving a short or a long position in a currently trending equity.

Other Information on Investing in Goldman Mutual Fund

Goldman Sachs financial ratios help investors to determine whether Goldman Mutual Fund is cheap or expensive when compared to a particular measure, such as profits or enterprise value. In other words, they help investors to determine the cost of investment in Goldman with respect to the benefits of owning Goldman Sachs security.
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