Health Biotchnology Mutual Fund Forecast - Polynomial Regression
| SHPAX Fund | USD 19.01 0.03 0.16% |
The Polynomial Regression forecasted value of Health Biotchnology Portfolio on the next trading day is expected to be 18.65 with a mean absolute deviation of 0.15 and the sum of the absolute errors of 9.35. Health Mutual Fund Forecast is based on your current time horizon. Investors can use this forecasting interface to forecast Health Biotchnology stock prices and determine the direction of Health Biotchnology Portfolio's future trends based on various well-known forecasting models. We recommend always using this module together with an analysis of Health Biotchnology's historical fundamentals, such as revenue growth or operating cash flow patterns.
Check out Historical Fundamental Analysis of Health Biotchnology to cross-verify your projections. Health |
Most investors in Health Biotchnology 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 Health Biotchnology's time series price data and predict how it will affect future prices. One of these methodologies is forecasting, which interprets Health Biotchnology's price structures and extracts relationships that further increase the generated results' accuracy.
Health Biotchnology polinomial regression implements a single variable polynomial regression model using the daily prices as the independent variable. The coefficients of the regression for Health Biotchnology Portfolio as well as the accuracy indicators are determined from the period prices. Health Biotchnology Polynomial Regression Price Forecast For the 29th of April
Given 90 days horizon, the Polynomial Regression forecasted value of Health Biotchnology Portfolio on the next trading day is expected to be 18.65 with a mean absolute deviation of 0.15, mean absolute percentage error of 0.04, and the sum of the absolute errors of 9.35.Please note that although there have been many attempts to predict Health 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 Health Biotchnology's next future price depends linearly on its previous prices and some stochastic term (i.e., imperfectly predictable multiplier).
Health Biotchnology Mutual Fund Forecast Pattern
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Health Biotchnology Forecasted Value
In the context of forecasting Health Biotchnology'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. Health Biotchnology's downside and upside margins for the forecasting period are 18.02 and 19.28, respectively. We have considered Health Biotchnology'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 Health Biotchnology mutual fund data series using in forecasting. Note that when a statistical model is used to represent Health Biotchnology 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 | 114.7786 |
| Bias | Arithmetic mean of the errors | None |
| MAD | Mean absolute deviation | 0.1532 |
| MAPE | Mean absolute percentage error | 0.0079 |
| SAE | Sum of the absolute errors | 9.3477 |
Predictive Modules for Health Biotchnology
There are currently many different techniques concerning forecasting the market as a whole, as well as predicting future values of individual securities such as Health Biotchnology. 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 Health Biotchnology'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 Health Biotchnology
For every potential investor in Health, whether a beginner or expert, Health Biotchnology's price movement is the inherent factor that sparks whether it is viable to invest in it or hold it better. Health Mutual Fund price charts are filled with many 'noises.' These noises can hugely alter the decision one can make regarding investing in Health. Basic forecasting techniques help filter out the noise by identifying Health Biotchnology's price trends.Health Biotchnology 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 Health Biotchnology mutual fund to make a market-neutral strategy. Peer analysis of Health Biotchnology could also be used in its relative valuation, which is a method of valuing Health Biotchnology by comparing valuation metrics with similar companies.
| Risk & Return | Correlation |
Health Biotchnology 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 Health Biotchnology'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 Health Biotchnology's current price.| Cycle Indicators | ||
| Math Operators | ||
| Math Transform | ||
| Momentum Indicators | ||
| Overlap Studies | ||
| Pattern Recognition | ||
| Price Transform | ||
| Statistic Functions | ||
| Volatility Indicators | ||
| Volume Indicators |
Health Biotchnology Market Strength Events
Market strength indicators help investors to evaluate how Health Biotchnology 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 Health Biotchnology shares will generate the highest return on investment. By undertsting and applying Health Biotchnology mutual fund market strength indicators, traders can identify Health Biotchnology Portfolio 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 | 19.01 | |||
| Day Typical Price | 19.01 | |||
| Price Action Indicator | (0.01) | |||
| Period Momentum Indicator | (0.03) | |||
| Relative Strength Index | 28.87 |
Health Biotchnology Risk Indicators
The analysis of Health Biotchnology'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 Health Biotchnology's investment and either accepting that risk or mitigating it. Along with some essential techniques for forecasting health 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 | 0.4856 | |||
| Standard Deviation | 0.6278 | |||
| Variance | 0.3941 |
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.
Some investors attempt to determine whether the market's mood is bullish or bearish by monitoring changes in market sentiment. Unlike more traditional methods such as technical analysis, investor sentiment usually refers to the aggregate attitude towards Health Biotchnology in the overall investment community. So, suppose investors can accurately measure the market's sentiment. In that case, they can use it for their benefit. For example, some tools to gauge market sentiment could be utilized using contrarian indexes, Health Biotchnology's short interest history, or implied volatility extrapolated from Health Biotchnology options trading.
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Check out Historical Fundamental Analysis of Health Biotchnology to cross-verify your projections. Note that the Health Biotchnology information on this page should be used as a complementary analysis to other Health Biotchnology'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 Watchlist Optimization module to optimize watchlists to build efficient portfolios or rebalance existing positions based on the mean-variance optimization algorithm.