FAT Brands Stock Forecast - Simple Regression

Simple Regression model is a single variable regression model that attempts to put a straight line through FAT Brands 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.

FAT Brands Simple Regression Price Forecast For the 26th of July

Given 90 days horizon, the Simple Regression forecasted value of FAT Brands on the next trading day is expected to be 2.91 with a mean absolute deviation of 0.49, mean absolute percentage error of 0.36, and the sum of the absolute errors of 29.70.

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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 FAT Brands stock data series using in forecasting. Note that when a statistical model is used to represent FAT Brands 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. 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 FAT Brands 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 FAT Brands

There are currently many different techniques concerning forecasting the market as a whole, as well as predicting future values of individual securities such as FAT Brands. 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.

Hype Prediction Low Estimated High 0.15 2.95 16.30 Details

Intrinsic Valuation Low Real High 0.14 2.87 16.22 Details

Bollinger Band Projection (param) Low Middle High 2.13 3.68 5.24 Details

Please note, it is not enough to conduct a financial or market analysis of a single entity such as FAT Brands. Your research has to be compared to or analyzed against FAT Brands' peers to derive any actionable benefits. When done correctly, FAT Brands' competitive analysis will give you plenty of quantitative and qualitative data to validate your investment decisions or develop an entirely new strategy toward taking a position in FAT Brands.

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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.

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