Federated Short Mutual Fund Forecast - Polynomial Regression
| FSTIX Fund | USD 8.38 0.02 0.24% |
The Polynomial Regression forecasted value of Federated Short Term Income on the next trading day is expected to be 8.37 with a mean absolute deviation of 0.01 and the sum of the absolute errors of 0.85. Federated Mutual Fund Forecast is based on your current time horizon. Investors can use this forecasting interface to forecast Federated Short stock prices and determine the direction of Federated Short Term Income's future trends based on various well-known forecasting models. We recommend always using this module together with an analysis of Federated Short's historical fundamentals, such as revenue growth or operating cash flow patterns.
Check out Historical Fundamental Analysis of Federated Short to cross-verify your projections. Federated |
Most investors in Federated Short cannot accurately predict what will happen the next trading day because, historically, fund markets tend to be unpredictable and even illogical. Modeling turbulent structures requires applying different statistical methods, techniques, and algorithms to find hidden data structures or patterns within the Federated Short's time series price data and predict how it will affect future prices. One of these methodologies is forecasting, which interprets Federated Short's price structures and extracts relationships that further increase the generated results' accuracy.
Federated Short polinomial regression implements a single variable polynomial regression model using the daily prices as the independent variable. The coefficients of the regression for Federated Short Term Income as well as the accuracy indicators are determined from the period prices. Federated Short Polynomial Regression Price Forecast For the 9th of May
Given 90 days horizon, the Polynomial Regression forecasted value of Federated Short Term Income on the next trading day is expected to be 8.37 with a mean absolute deviation of 0.01, mean absolute percentage error of 0.0003, and the sum of the absolute errors of 0.85.Please note that although there have been many attempts to predict Federated Mutual Fund 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 Federated Short's next future price depends linearly on its previous prices and some stochastic term (i.e., imperfectly predictable multiplier).
Federated Short Mutual Fund Forecast Pattern
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Federated Short Forecasted Value
In the context of forecasting Federated Short's Mutual Fund 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. Federated Short's downside and upside margins for the forecasting period are 8.24 and 8.49, respectively. We have considered Federated Short'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 Polynomial Regression forecasting method's relative quality and the estimations of the prediction error of Federated Short mutual fund data series using in forecasting. Note that when a statistical model is used to represent Federated Short mutual fund, 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 | 110.0061 |
| Bias | Arithmetic mean of the errors | None |
| MAD | Mean absolute deviation | 0.0139 |
| MAPE | Mean absolute percentage error | 0.0017 |
| SAE | Sum of the absolute errors | 0.8465 |
Predictive Modules for Federated Short
There are currently many different techniques concerning forecasting the market as a whole, as well as predicting future values of individual securities such as Federated Short Term. Regardless of method or technology, however, to accurately forecast the mutual fund market is more a matter of luck rather than a particular technique. Nevertheless, trying to predict the mutual fund 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.Sophisticated investors, who have witnessed many market ups and downs, anticipate that the market will even out over time. This tendency of Federated Short's price to converge to an average value over time is called mean reversion. However, historically, high market prices usually discourage investors that believe in mean reversion to invest, while low prices are viewed as an opportunity to buy.
Other Forecasting Options for Federated Short
For every potential investor in Federated, whether a beginner or expert, Federated Short's price movement is the inherent factor that sparks whether it is viable to invest in it or hold it better. Federated Mutual Fund price charts are filled with many 'noises.' These noises can hugely alter the decision one can make regarding investing in Federated. Basic forecasting techniques help filter out the noise by identifying Federated Short's price trends.Federated Short 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 Federated Short mutual fund to make a market-neutral strategy. Peer analysis of Federated Short could also be used in its relative valuation, which is a method of valuing Federated Short by comparing valuation metrics with similar companies.
| Risk & Return | Correlation |
Federated Short Term Technical and Predictive Analytics
The mutual fund market is financially volatile. Despite the volatility, there exist limitless possibilities of gaining profits and building passive income portfolios. With the complexity of Federated Short'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 Federated Short's current price.| Cycle Indicators | ||
| Math Operators | ||
| Math Transform | ||
| Momentum Indicators | ||
| Overlap Studies | ||
| Pattern Recognition | ||
| Price Transform | ||
| Statistic Functions | ||
| Volatility Indicators | ||
| Volume Indicators |
Federated Short Market Strength Events
Market strength indicators help investors to evaluate how Federated Short mutual fund reacts to ongoing and evolving market conditions. The investors can use it to make informed decisions about market timing, and determine when trading Federated Short shares will generate the highest return on investment. By undertsting and applying Federated Short mutual fund market strength indicators, traders can identify Federated Short Term Income entry and exit signals to maximize returns.
| Daily Balance Of Power | 9.2 T | |||
| Rate Of Daily Change | 1.0 | |||
| Day Median Price | 8.38 | |||
| Day Typical Price | 8.38 | |||
| Price Action Indicator | 0.01 | |||
| Period Momentum Indicator | 0.02 | |||
| Relative Strength Index | 43.22 |
Federated Short Risk Indicators
The analysis of Federated Short'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 Federated Short's investment and either accepting that risk or mitigating it. Along with some essential techniques for forecasting federated mutual fund 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 | 0.1073 | |||
| Semi Deviation | 0.0993 | |||
| Standard Deviation | 0.1413 | |||
| Variance | 0.02 | |||
| Downside Variance | 0.0357 | |||
| Semi Variance | 0.0099 | |||
| Expected Short fall | (0.14) |
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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Try AI Portfolio ArchitectCheck out Historical Fundamental Analysis of Federated Short to cross-verify your projections. You can also try the Equity Forecasting module to use basic forecasting models to generate price predictions and determine price momentum.