Financial Industries Mutual Fund Forecast - Polynomial Regression
| FIDCX Fund | USD 14.20 0.00 0.00% |
The Polynomial Regression forecasted value of Financial Industries Fund on the next trading day is expected to be 14.61 with a mean absolute deviation of 0.15 and the sum of the absolute errors of 9.38. Financial Mutual Fund Forecast is based on your current time horizon. Investors can use this forecasting interface to forecast Financial Industries stock prices and determine the direction of Financial Industries Fund's future trends based on various well-known forecasting models. We recommend always using this module together with an analysis of Financial Industries' historical fundamentals, such as revenue growth or operating cash flow patterns.
Check out Historical Fundamental Analysis of Financial Industries to cross-verify your projections. Financial |
Most investors in Financial Industries 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 Financial Industries' time series price data and predict how it will affect future prices. One of these methodologies is forecasting, which interprets Financial Industries' price structures and extracts relationships that further increase the generated results' accuracy.
Financial Industries polinomial regression implements a single variable polynomial regression model using the daily prices as the independent variable. The coefficients of the regression for Financial Industries Fund as well as the accuracy indicators are determined from the period prices. Financial Industries Polynomial Regression Price Forecast For the 30th of May
Given 90 days horizon, the Polynomial Regression forecasted value of Financial Industries Fund on the next trading day is expected to be 14.61 with a mean absolute deviation of 0.15, mean absolute percentage error of 0.03, and the sum of the absolute errors of 9.38.Please note that although there have been many attempts to predict Financial 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 Financial Industries' next future price depends linearly on its previous prices and some stochastic term (i.e., imperfectly predictable multiplier).
Financial Industries Mutual Fund Forecast Pattern
| Backtest Financial Industries | Financial Industries Price Prediction | Buy or Sell Advice |
Financial Industries Forecasted Value
In the context of forecasting Financial Industries' 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. Financial Industries' downside and upside margins for the forecasting period are 13.87 and 15.35, respectively. We have considered Financial Industries' 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 Financial Industries mutual fund data series using in forecasting. Note that when a statistical model is used to represent Financial Industries 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 | 114.747 |
| Bias | Arithmetic mean of the errors | None |
| MAD | Mean absolute deviation | 0.1537 |
| MAPE | Mean absolute percentage error | 0.011 |
| SAE | Sum of the absolute errors | 9.3775 |
Predictive Modules for Financial Industries
There are currently many different techniques concerning forecasting the market as a whole, as well as predicting future values of individual securities such as Financial Industries. 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 Financial Industries' 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 Financial Industries
For every potential investor in Financial, whether a beginner or expert, Financial Industries' price movement is the inherent factor that sparks whether it is viable to invest in it or hold it better. Financial Mutual Fund price charts are filled with many 'noises.' These noises can hugely alter the decision one can make regarding investing in Financial. Basic forecasting techniques help filter out the noise by identifying Financial Industries' price trends.Financial Industries 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 Financial Industries mutual fund to make a market-neutral strategy. Peer analysis of Financial Industries could also be used in its relative valuation, which is a method of valuing Financial Industries by comparing valuation metrics with similar companies.
| Risk & Return | Correlation |
Financial Industries 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 Financial Industries' 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 Financial Industries' current price.| Cycle Indicators | ||
| Math Operators | ||
| Math Transform | ||
| Momentum Indicators | ||
| Overlap Studies | ||
| Pattern Recognition | ||
| Price Transform | ||
| Statistic Functions | ||
| Volatility Indicators | ||
| Volume Indicators |
Financial Industries Market Strength Events
Market strength indicators help investors to evaluate how Financial Industries 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 Financial Industries shares will generate the highest return on investment. By undertsting and applying Financial Industries mutual fund market strength indicators, traders can identify Financial Industries Fund entry and exit signals to maximize returns.
Financial Industries Risk Indicators
The analysis of Financial Industries' 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 Financial Industries' investment and either accepting that risk or mitigating it. Along with some essential techniques for forecasting financial 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 | 0.5914 | |||
| Semi Deviation | 0.7058 | |||
| Standard Deviation | 0.7389 | |||
| Variance | 0.5459 | |||
| Downside Variance | 0.6714 | |||
| Semi Variance | 0.4982 | |||
| Expected Short fall | (0.61) |
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.Check out Historical Fundamental Analysis of Financial Industries to cross-verify your projections. Note that the Financial Industries information on this page should be used as a complementary analysis to other Financial Industries' 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 Idea Analyzer module to analyze all characteristics, volatility and risk-adjusted return of Macroaxis ideas.