General Motors Stock Forecast - Polynomial Regression
| GMCO34 Stock | BRL 83.64 0.35 0.42% |
The Polynomial Regression forecasted value of General Motors on the next trading day is expected to be 83.82 with a mean absolute deviation of 1.65 and the sum of the absolute errors of 100.58. General Stock Forecast is based on your current time horizon. Investors can use this forecasting interface to forecast General Motors stock prices and determine the direction of General Motors's future trends based on various well-known forecasting models. We recommend always using this module together with an analysis of General Motors' historical fundamentals, such as revenue growth or operating cash flow patterns.
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General Motors Polynomial Regression Price Forecast For the 3rd of December
Given 90 days horizon, the Polynomial Regression forecasted value of General Motors on the next trading day is expected to be 83.82 with a mean absolute deviation of 1.65, mean absolute percentage error of 4.44, and the sum of the absolute errors of 100.58.Please note that although there have been many attempts to predict General 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 General Motors' next future price depends linearly on its previous prices and some stochastic term (i.e., imperfectly predictable multiplier).
General Motors Stock Forecast Pattern
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General Motors Forecasted Value
In the context of forecasting General Motors' 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. General Motors' downside and upside margins for the forecasting period are 80.79 and 86.86, respectively. We have considered General Motors' 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 General Motors stock data series using in forecasting. Note that when a statistical model is used to represent General Motors 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 | 119.6016 |
| Bias | Arithmetic mean of the errors | None |
| MAD | Mean absolute deviation | 1.6489 |
| MAPE | Mean absolute percentage error | 0.023 |
| SAE | Sum of the absolute errors | 100.5849 |
Predictive Modules for General Motors
There are currently many different techniques concerning forecasting the market as a whole, as well as predicting future values of individual securities such as General Motors. 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.Other Forecasting Options for General Motors
For every potential investor in General, whether a beginner or expert, General Motors' price movement is the inherent factor that sparks whether it is viable to invest in it or hold it better. General Stock price charts are filled with many 'noises.' These noises can hugely alter the decision one can make regarding investing in General. Basic forecasting techniques help filter out the noise by identifying General Motors' price trends.General Motors 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 General Motors stock to make a market-neutral strategy. Peer analysis of General Motors could also be used in its relative valuation, which is a method of valuing General Motors by comparing valuation metrics with similar companies.
| Risk & Return | Correlation |
General Motors 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 General Motors' 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 General Motors' current price.| Cycle Indicators | ||
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General Motors Market Strength Events
Market strength indicators help investors to evaluate how General Motors stock reacts to ongoing and evolving market conditions. The investors can use it to make informed decisions about market timing, and determine when trading General Motors shares will generate the highest return on investment. By undertsting and applying General Motors stock market strength indicators, traders can identify General Motors entry and exit signals to maximize returns.
General Motors Risk Indicators
The analysis of General Motors' 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 General Motors' investment and either accepting that risk or mitigating it. Along with some essential techniques for forecasting general 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 | 1.94 | |||
| Semi Deviation | 2.57 | |||
| Standard Deviation | 3.0 | |||
| Variance | 8.98 | |||
| Downside Variance | 10.03 | |||
| Semi Variance | 6.62 | |||
| Expected Short fall | (2.01) |
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.
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.Additional Information and Resources on Investing in General Stock
When determining whether General Motors is a strong investment it is important to analyze General Motors' competitive position within its industry, examining market share, product or service uniqueness, and competitive advantages. Beyond financials and market position, potential investors should also consider broader economic conditions, industry trends, and any regulatory or geopolitical factors that may impact General Motors' future performance. For an informed investment choice regarding General Stock, refer to the following important reports:Check out Historical Fundamental Analysis of General Motors to cross-verify your projections. For information on how to trade General Stock refer to our How to Trade General Stock guide.You can also try the Fundamental Analysis module to view fundamental data based on most recent published financial statements.