Sangoma Technologies Stock Forecast - Polynomial Regression
| SANG Stock | USD 6.22 0.12 1.97% |
The Polynomial Regression forecasted value of Sangoma Technologies Corp on the next trading day is expected to be 6.42 with a mean absolute deviation of 0.15 and the sum of the absolute errors of 9.33. Sangoma Stock Forecast is based on your current time horizon. We recommend always using this module together with an analysis of Sangoma Technologies' historical fundamentals, such as revenue growth or operating cash flow patterns.
Sangoma |
Sangoma Technologies Polynomial Regression Price Forecast For the 26th of November
Given 90 days horizon, the Polynomial Regression forecasted value of Sangoma Technologies Corp on the next trading day is expected to be 6.42 with a mean absolute deviation of 0.15, mean absolute percentage error of 0.04, and the sum of the absolute errors of 9.33.Please note that although there have been many attempts to predict Sangoma 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 Sangoma Technologies' next future price depends linearly on its previous prices and some stochastic term (i.e., imperfectly predictable multiplier).
Sangoma Technologies Stock Forecast Pattern
| Backtest Sangoma Technologies | Sangoma Technologies Price Prediction | Buy or Sell Advice |
Sangoma Technologies Forecasted Value
In the context of forecasting Sangoma Technologies' 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. Sangoma Technologies' downside and upside margins for the forecasting period are 3.43 and 9.42, respectively. We have considered Sangoma Technologies' 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 Sangoma Technologies stock data series using in forecasting. Note that when a statistical model is used to represent Sangoma Technologies 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 | 114.8822 |
| Bias | Arithmetic mean of the errors | None |
| MAD | Mean absolute deviation | 0.153 |
| MAPE | Mean absolute percentage error | 0.0262 |
| SAE | Sum of the absolute errors | 9.33 |
Predictive Modules for Sangoma Technologies
There are currently many different techniques concerning forecasting the market as a whole, as well as predicting future values of individual securities such as Sangoma Technologies Corp. 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 Sangoma Technologies
For every potential investor in Sangoma, whether a beginner or expert, Sangoma Technologies' price movement is the inherent factor that sparks whether it is viable to invest in it or hold it better. Sangoma Stock price charts are filled with many 'noises.' These noises can hugely alter the decision one can make regarding investing in Sangoma. Basic forecasting techniques help filter out the noise by identifying Sangoma Technologies' price trends.Sangoma Technologies 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 Sangoma Technologies stock to make a market-neutral strategy. Peer analysis of Sangoma Technologies could also be used in its relative valuation, which is a method of valuing Sangoma Technologies by comparing valuation metrics with similar companies.
| Risk & Return | Correlation |
Sangoma Technologies Corp 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 Sangoma Technologies' 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 Sangoma Technologies' current price.| Cycle Indicators | ||
| Math Operators | ||
| Math Transform | ||
| Momentum Indicators | ||
| Overlap Studies | ||
| Pattern Recognition | ||
| Price Transform | ||
| Statistic Functions | ||
| Volatility Indicators | ||
| Volume Indicators |
Sangoma Technologies Market Strength Events
Market strength indicators help investors to evaluate how Sangoma Technologies stock reacts to ongoing and evolving market conditions. The investors can use it to make informed decisions about market timing, and determine when trading Sangoma Technologies shares will generate the highest return on investment. By undertsting and applying Sangoma Technologies stock market strength indicators, traders can identify Sangoma Technologies Corp entry and exit signals to maximize returns.
Sangoma Technologies Risk Indicators
The analysis of Sangoma Technologies' 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 Sangoma Technologies' investment and either accepting that risk or mitigating it. Along with some essential techniques for forecasting sangoma 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 | 2.11 | |||
| Semi Deviation | 2.36 | |||
| Standard Deviation | 3.0 | |||
| Variance | 8.99 | |||
| Downside Variance | 6.76 | |||
| Semi Variance | 5.59 | |||
| Expected Short fall | (2.54) |
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.
Currently Active Assets on Macroaxis
When determining whether Sangoma Technologies Corp is a strong investment it is important to analyze Sangoma Technologies' 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 Sangoma Technologies' future performance. For an informed investment choice regarding Sangoma Stock, refer to the following important reports:Check out Historical Fundamental Analysis of Sangoma Technologies to cross-verify your projections. You can also try the Instant Ratings module to determine any equity ratings based on digital recommendations. Macroaxis instant equity ratings are based on combination of fundamental analysis and risk-adjusted market performance.
Is Application Software space expected to grow? Or is there an opportunity to expand the business' product line in the future? Factors like these will boost the valuation of Sangoma Technologies. If investors know Sangoma will grow in the future, the company's valuation will be higher. The financial industry is built on trying to define current growth potential and future valuation accurately. All the valuation information about Sangoma Technologies listed above have to be considered, but the key to understanding future value is determining which factors weigh more heavily than others.
Earnings Share (0.25) | Revenue Per Share 7.335 | Quarterly Revenue Growth (0.05) | Return On Assets (0) | Return On Equity (0.03) |
The market value of Sangoma Technologies Corp is measured differently than its book value, which is the value of Sangoma that is recorded on the company's balance sheet. Investors also form their own opinion of Sangoma Technologies' value that differs from its market value or its book value, called intrinsic value, which is Sangoma Technologies' true underlying value. Investors use various methods to calculate intrinsic value and buy a stock when its market value falls below its intrinsic value. Because Sangoma Technologies' market value can be influenced by many factors that don't directly affect Sangoma Technologies' underlying business (such as a pandemic or basic market pessimism), market value can vary widely from intrinsic value.
Please note, there is a significant difference between Sangoma Technologies' value and its price as these two are different measures arrived at by different means. Investors typically determine if Sangoma Technologies is a good investment by looking at such factors as earnings, sales, fundamental and technical indicators, competition as well as analyst projections. However, Sangoma Technologies' price is the amount at which it trades on the open market and represents the number that a seller and buyer find agreeable to each party.