Short Term Mutual Fund Forecast - Naive Prediction
| TWACX Fund | USD 8.85 0.01 0.11% |
The Naive Prediction forecasted value of Short Term Government Fund on the next trading day is expected to be 8.83 with a mean absolute deviation of 0.01 and the sum of the absolute errors of 0.65. Short Mutual Fund Forecast is based on your current time horizon. Investors can use this forecasting interface to forecast Short Term stock prices and determine the direction of Short Term Government Fund's future trends based on various well-known forecasting models. We recommend always using this module together with an analysis of Short Term's historical fundamentals, such as revenue growth or operating cash flow patterns.
Check out Historical Fundamental Analysis of Short Term to cross-verify your projections. Short |
Most investors in Short Term 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 Short Term's time series price data and predict how it will affect future prices. One of these methodologies is forecasting, which interprets Short Term's price structures and extracts relationships that further increase the generated results' accuracy.
A naive forecasting model for Short Term is a special case of the moving average forecasting where the number of periods used for smoothing is one. Therefore, the forecast of Short Term Government Fund value for a given trading day is simply the observed value for the previous period. Due to the simplistic nature of the naive forecasting model, it can only be used to forecast up to one period. Short Term Naive Prediction Price Forecast For the 25th of May
Given 90 days horizon, the Naive Prediction forecasted value of Short Term Government Fund on the next trading day is expected to be 8.83 with a mean absolute deviation of 0.01, mean absolute percentage error of 0.0002, and the sum of the absolute errors of 0.65.Please note that although there have been many attempts to predict Short 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 Short Term's next future price depends linearly on its previous prices and some stochastic term (i.e., imperfectly predictable multiplier).
Short Term Mutual Fund Forecast Pattern
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Short Term Forecasted Value
In the context of forecasting Short Term'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. Short Term's downside and upside margins for the forecasting period are 8.70 and 8.95, respectively. We have considered Short Term'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 Short Term mutual fund data series using in forecasting. Note that when a statistical model is used to represent Short Term 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 | 111.2397 |
| Bias | Arithmetic mean of the errors | None |
| MAD | Mean absolute deviation | 0.0104 |
| MAPE | Mean absolute percentage error | 0.0012 |
| SAE | Sum of the absolute errors | 0.6471 |
Predictive Modules for Short Term
There are currently many different techniques concerning forecasting the market as a whole, as well as predicting future values of individual securities such as Short Term Government. 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 Short Term'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 Short Term
For every potential investor in Short, whether a beginner or expert, Short Term's price movement is the inherent factor that sparks whether it is viable to invest in it or hold it better. Short Mutual Fund price charts are filled with many 'noises.' These noises can hugely alter the decision one can make regarding investing in Short. Basic forecasting techniques help filter out the noise by identifying Short Term's price trends.Short Term 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 Short Term mutual fund to make a market-neutral strategy. Peer analysis of Short Term could also be used in its relative valuation, which is a method of valuing Short Term by comparing valuation metrics with similar companies.
| Risk & Return | Correlation |
Short Term Government 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 Short Term'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 Short Term's current price.| Cycle Indicators | ||
| Math Operators | ||
| Math Transform | ||
| Momentum Indicators | ||
| Overlap Studies | ||
| Pattern Recognition | ||
| Price Transform | ||
| Statistic Functions | ||
| Volatility Indicators | ||
| Volume Indicators |
Short Term Market Strength Events
Market strength indicators help investors to evaluate how Short Term 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 Short Term shares will generate the highest return on investment. By undertsting and applying Short Term mutual fund market strength indicators, traders can identify Short Term Government Fund entry and exit signals to maximize returns.
| Daily Balance Of Power | (9,223,372,036,855) | |||
| Rate Of Daily Change | 1.0 | |||
| Day Median Price | 8.85 | |||
| Day Typical Price | 8.85 | |||
| Price Action Indicator | (0.01) | |||
| Period Momentum Indicator | (0.01) | |||
| Relative Strength Index | 54.72 |
Short Term Risk Indicators
The analysis of Short Term'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 Short Term's investment and either accepting that risk or mitigating it. Along with some essential techniques for forecasting short 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 | 0.0836 | |||
| Semi Deviation | 0.0896 | |||
| Standard Deviation | 0.1221 | |||
| Variance | 0.0149 | |||
| Downside Variance | 0.0326 | |||
| Semi Variance | 0.008 | |||
| 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.
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.| MSFT | Microsoft | |
| GM | General Motors | |
| CRM | Salesforce |
Check out Historical Fundamental Analysis of Short Term to cross-verify your projections. Note that the Short Term Government information on this page should be used as a complementary analysis to other Short Term's statistical models used to find the right mix of equity instruments to add to your existing portfolios or create a brand new portfolio. You can also try the Transaction History module to view history of all your transactions and understand their impact on performance.