Fortinet Stock Forecast - Naive Prediction
| F1TN34 Stock | BRL 293.00 1.58 0.54% |
The Naive Prediction forecasted value of Fortinet on the next trading day is expected to be 291.90 with a mean absolute deviation of 6.53 and the sum of the absolute errors of 398.29. Fortinet Stock Forecast is based on your current time horizon. Investors can use this forecasting interface to forecast Fortinet stock prices and determine the direction of Fortinet's future trends based on various well-known forecasting models. We recommend always using this module together with an analysis of Fortinet's historical fundamentals, such as revenue growth or operating cash flow patterns.
Fortinet |
Fortinet Naive Prediction Price Forecast For the 23rd of December
Given 90 days horizon, the Naive Prediction forecasted value of Fortinet on the next trading day is expected to be 291.90 with a mean absolute deviation of 6.53, mean absolute percentage error of 78.19, and the sum of the absolute errors of 398.29.Please note that although there have been many attempts to predict Fortinet 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 Fortinet's next future price depends linearly on its previous prices and some stochastic term (i.e., imperfectly predictable multiplier).
Fortinet Stock Forecast Pattern
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Fortinet Forecasted Value
In the context of forecasting Fortinet's 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. Fortinet's downside and upside margins for the forecasting period are 289.63 and 294.16, respectively. We have considered Fortinet's 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 Naive Prediction forecasting method's relative quality and the estimations of the prediction error of Fortinet stock data series using in forecasting. Note that when a statistical model is used to represent Fortinet 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 | 122.4697 |
| Bias | Arithmetic mean of the errors | None |
| MAD | Mean absolute deviation | 6.5294 |
| MAPE | Mean absolute percentage error | 0.0264 |
| SAE | Sum of the absolute errors | 398.2944 |
Predictive Modules for Fortinet
There are currently many different techniques concerning forecasting the market as a whole, as well as predicting future values of individual securities such as Fortinet. 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 Fortinet
For every potential investor in Fortinet, whether a beginner or expert, Fortinet's price movement is the inherent factor that sparks whether it is viable to invest in it or hold it better. Fortinet Stock price charts are filled with many 'noises.' These noises can hugely alter the decision one can make regarding investing in Fortinet. Basic forecasting techniques help filter out the noise by identifying Fortinet's price trends.Fortinet 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 Fortinet stock to make a market-neutral strategy. Peer analysis of Fortinet could also be used in its relative valuation, which is a method of valuing Fortinet by comparing valuation metrics with similar companies.
| Risk & Return | Correlation |
Fortinet 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 Fortinet's 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 Fortinet's current price.| Cycle Indicators | ||
| Math Operators | ||
| Math Transform | ||
| Momentum Indicators | ||
| Overlap Studies | ||
| Pattern Recognition | ||
| Price Transform | ||
| Statistic Functions | ||
| Volatility Indicators | ||
| Volume Indicators |
Fortinet Market Strength Events
Market strength indicators help investors to evaluate how Fortinet stock reacts to ongoing and evolving market conditions. The investors can use it to make informed decisions about market timing, and determine when trading Fortinet shares will generate the highest return on investment. By undertsting and applying Fortinet stock market strength indicators, traders can identify Fortinet entry and exit signals to maximize returns.
Fortinet Risk Indicators
The analysis of Fortinet's 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 Fortinet's investment and either accepting that risk or mitigating it. Along with some essential techniques for forecasting fortinet 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 | 1.46 | |||
| Semi Deviation | 0.8224 | |||
| Standard Deviation | 2.29 | |||
| Variance | 5.23 | |||
| Downside Variance | 2.57 | |||
| Semi Variance | 0.6763 | |||
| Expected Short fall | (2.41) |
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 Fortinet Stock
When determining whether Fortinet is a good investment, qualitative aspects like company management, corporate governance, and ethical practices play a significant role. A comparison with peer companies also provides context and helps to understand if Fortinet Stock is undervalued or overvalued. This multi-faceted approach, blending both quantitative and qualitative analysis, forms a solid foundation for making an informed investment decision about Fortinet Stock. Highlighted below are key reports to facilitate an investment decision about Fortinet Stock:Check out Historical Fundamental Analysis of Fortinet to cross-verify your projections. For information on how to trade Fortinet Stock refer to our How to Trade Fortinet Stock guide.You can also try the Watchlist Optimization module to optimize watchlists to build efficient portfolios or rebalance existing positions based on the mean-variance optimization algorithm.