Goldman Sachs Stock Forecast - Polynomial Regression
| GS Stock | USD 596.11 14.18 2.44% |
The Polynomial Regression forecasted value of Goldman Sachs Group on the next trading day is expected to be 610.51 with a mean absolute deviation of 11.38 and the sum of the absolute errors of 694.33. Goldman Stock Forecast is based on your current time horizon.
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Goldman Sachs Polynomial Regression Price Forecast For the 23rd of November
Given 90 days horizon, the Polynomial Regression forecasted value of Goldman Sachs Group on the next trading day is expected to be 610.51 with a mean absolute deviation of 11.38, mean absolute percentage error of 214.55, and the sum of the absolute errors of 694.33.Please note that although there have been many attempts to predict Goldman Stock 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 Stock Forecast Pattern
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Goldman Sachs Forecasted Value
In the context of forecasting Goldman Sachs' Stock 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 608.28 and 612.73, 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.
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 Goldman Sachs stock data series using in forecasting. Note that when a statistical model is used to represent Goldman Sachs stock, 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 | 123.479 |
| Bias | Arithmetic mean of the errors | None |
| MAD | Mean absolute deviation | 11.3825 |
| MAPE | Mean absolute percentage error | 0.0214 |
| SAE | Sum of the absolute errors | 694.3305 |
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 Group. Regardless of method or technology, however, to accurately forecast the stock market is more a matter of luck rather than a particular technique. Nevertheless, trying to predict the stock 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.Sophisticated investors, who have witnessed many market ups and downs, anticipate that the market will even out over time. This tendency of Goldman Sachs' 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.
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 Stock 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 stock 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 Group Technical and Predictive Analytics
The stock 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.| Cycle Indicators | ||
| Math Operators | ||
| Math Transform | ||
| Momentum Indicators | ||
| Overlap Studies | ||
| Pattern Recognition | ||
| Price Transform | ||
| Statistic Functions | ||
| Volatility Indicators | ||
| Volume Indicators |
Goldman Sachs Market Strength Events
Market strength indicators help investors to evaluate how Goldman Sachs stock 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 stock market strength indicators, traders can identify Goldman Sachs Group entry and exit signals to maximize returns.
Goldman Sachs Risk Indicators
The analysis of Goldman Sachs' basic risk indicators is one of the essential steps in accurately forecasting its future price. The process involves identifying the amount of risk involved in Goldman Sachs' investment and either accepting that risk or mitigating it. Along with some essential techniques for forecasting goldman stock prices, we also provide a set of basic risk indicators that can assist in the individual investment decision or help in hedging the risk of your existing portfolios.
| Mean Deviation | 1.32 | |||
| Semi Deviation | 1.27 | |||
| Standard Deviation | 2.21 | |||
| Variance | 4.88 | |||
| Downside Variance | 2.62 | |||
| Semi Variance | 1.62 | |||
| Expected Short fall | (1.48) |
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.
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Additional Tools for Goldman Stock Analysis
When running Goldman Sachs' price analysis, check to measure Goldman Sachs' market volatility, profitability, liquidity, solvency, efficiency, growth potential, financial leverage, and other vital indicators. We have many different tools that can be utilized to determine how healthy Goldman Sachs is operating at the current time. Most of Goldman Sachs' value examination focuses on studying past and present price action to predict the probability of Goldman Sachs' future price movements. You can analyze the entity against its peers and the financial market as a whole to determine factors that move Goldman Sachs' price. Additionally, you may evaluate how the addition of Goldman Sachs to your portfolios can decrease your overall portfolio volatility.