FAT Brands Stock Forecast - Polynomial Regression
| FAT Stock | USD 5.38 0.02 0.37% |
The Polynomial Regression forecasted value of FAT Brands on the next trading day is expected to be 5.39 with a mean absolute deviation of 0.09 and the sum of the absolute errors of 5.52. FAT Stock Forecast is based on your current time horizon.
FAT |
FAT Brands Polynomial Regression Price Forecast For the 21st of December
Given 90 days horizon, the Polynomial Regression forecasted value of FAT Brands on the next trading day is expected to be 5.39 with a mean absolute deviation of 0.09, mean absolute percentage error of 0.01, and the sum of the absolute errors of 5.52.Please note that although there have been many attempts to predict FAT 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 FAT Brands' next future price depends linearly on its previous prices and some stochastic term (i.e., imperfectly predictable multiplier).
FAT Brands Stock Forecast Pattern
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FAT Brands Forecasted Value
In the context of forecasting FAT Brands' 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. FAT Brands' downside and upside margins for the forecasting period are 3.69 and 7.09, respectively. We have considered FAT Brands' 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 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.| AIC | Akaike Information Criteria | 115.5586 |
| Bias | Arithmetic mean of the errors | None |
| MAD | Mean absolute deviation | 0.089 |
| MAPE | Mean absolute percentage error | 0.0177 |
| SAE | Sum of the absolute errors | 5.5192 |
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.Other Forecasting Options for FAT Brands
For every potential investor in FAT, whether a beginner or expert, FAT Brands' price movement is the inherent factor that sparks whether it is viable to invest in it or hold it better. FAT Stock price charts are filled with many 'noises.' These noises can hugely alter the decision one can make regarding investing in FAT. Basic forecasting techniques help filter out the noise by identifying FAT Brands' price trends.FAT Brands 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 FAT Brands stock to make a market-neutral strategy. Peer analysis of FAT Brands could also be used in its relative valuation, which is a method of valuing FAT Brands by comparing valuation metrics with similar companies.
| Risk & Return | Correlation |
FAT Brands 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 FAT Brands' 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 FAT Brands' current price.| Cycle Indicators | ||
| Math Operators | ||
| Math Transform | ||
| Momentum Indicators | ||
| Overlap Studies | ||
| Pattern Recognition | ||
| Price Transform | ||
| Statistic Functions | ||
| Volatility Indicators | ||
| Volume Indicators |
FAT Brands Market Strength Events
Market strength indicators help investors to evaluate how FAT Brands stock reacts to ongoing and evolving market conditions. The investors can use it to make informed decisions about market timing, and determine when trading FAT Brands shares will generate the highest return on investment. By undertsting and applying FAT Brands stock market strength indicators, traders can identify FAT Brands entry and exit signals to maximize returns.
| Accumulation Distribution | 664.01 | |||
| Daily Balance Of Power | 0.0714 | |||
| Rate Of Daily Change | 1.0 | |||
| Day Median Price | 5.43 | |||
| Day Typical Price | 5.41 | |||
| Price Action Indicator | (0.04) | |||
| Period Momentum Indicator | 0.02 |
FAT Brands Risk Indicators
The analysis of FAT Brands' 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 FAT Brands' investment and either accepting that risk or mitigating it. Along with some essential techniques for forecasting fat 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 | 1.31 | |||
| Semi Deviation | 1.4 | |||
| Standard Deviation | 1.79 | |||
| Variance | 3.2 | |||
| Downside Variance | 3.09 | |||
| Semi Variance | 1.97 | |||
| Expected Short fall | (1.46) |
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 FAT Stock Analysis
When running FAT Brands' price analysis, check to measure FAT Brands' 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 FAT Brands is operating at the current time. Most of FAT Brands' value examination focuses on studying past and present price action to predict the probability of FAT Brands' future price movements. You can analyze the entity against its peers and the financial market as a whole to determine factors that move FAT Brands' price. Additionally, you may evaluate how the addition of FAT Brands to your portfolios can decrease your overall portfolio volatility.