ALM Offensif Fund Forecast - Simple Regression
0P00000GIZ | EUR 315.06 0.12 0.04% |
The Simple Regression forecasted value of ALM Offensif on the next trading day is expected to be 317.06 with a mean absolute deviation of 1.92 and the sum of the absolute errors of 116.99. ALM Fund Forecast is based on your current time horizon. Investors can use this forecasting interface to forecast ALM Offensif stock prices and determine the direction of ALM Offensif's future trends based on various well-known forecasting models. We recommend always using this module together with an analysis of ALM Offensif's historical fundamentals, such as revenue growth or operating cash flow patterns.
ALM |
ALM Offensif Simple Regression Price Forecast For the 25th of November
Given 90 days horizon, the Simple Regression forecasted value of ALM Offensif on the next trading day is expected to be 317.06 with a mean absolute deviation of 1.92, mean absolute percentage error of 6.04, and the sum of the absolute errors of 116.99.Please note that although there have been many attempts to predict ALM 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 ALM Offensif's next future price depends linearly on its previous prices and some stochastic term (i.e., imperfectly predictable multiplier).
ALM Offensif Fund Forecast Pattern
Backtest ALM Offensif | ALM Offensif Price Prediction | Buy or Sell Advice |
ALM Offensif Forecasted Value
In the context of forecasting ALM Offensif's 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. ALM Offensif's downside and upside margins for the forecasting period are 316.56 and 317.55, respectively. We have considered ALM Offensif'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 Simple Regression forecasting method's relative quality and the estimations of the prediction error of ALM Offensif fund data series using in forecasting. Note that when a statistical model is used to represent ALM Offensif 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 | 119.9089 |
Bias | Arithmetic mean of the errors | None |
MAD | Mean absolute deviation | 1.9178 |
MAPE | Mean absolute percentage error | 0.0062 |
SAE | Sum of the absolute errors | 116.9884 |
Predictive Modules for ALM Offensif
There are currently many different techniques concerning forecasting the market as a whole, as well as predicting future values of individual securities such as ALM Offensif. Regardless of method or technology, however, to accurately forecast the fund market is more a matter of luck rather than a particular technique. Nevertheless, trying to predict the 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.Other Forecasting Options for ALM Offensif
For every potential investor in ALM, whether a beginner or expert, ALM Offensif's price movement is the inherent factor that sparks whether it is viable to invest in it or hold it better. ALM Fund price charts are filled with many 'noises.' These noises can hugely alter the decision one can make regarding investing in ALM. Basic forecasting techniques help filter out the noise by identifying ALM Offensif's price trends.ALM Offensif 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 ALM Offensif fund to make a market-neutral strategy. Peer analysis of ALM Offensif could also be used in its relative valuation, which is a method of valuing ALM Offensif by comparing valuation metrics with similar companies.
Risk & Return | Correlation |
ALM Offensif Technical and Predictive Analytics
The fund market is financially volatile. Despite the volatility, there exist limitless possibilities of gaining profits and building passive income portfolios. With the complexity of ALM Offensif'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 ALM Offensif's current price.Cycle Indicators | ||
Math Operators | ||
Math Transform | ||
Momentum Indicators | ||
Overlap Studies | ||
Pattern Recognition | ||
Price Transform | ||
Statistic Functions | ||
Volatility Indicators | ||
Volume Indicators |
ALM Offensif Market Strength Events
Market strength indicators help investors to evaluate how ALM Offensif fund reacts to ongoing and evolving market conditions. The investors can use it to make informed decisions about market timing, and determine when trading ALM Offensif shares will generate the highest return on investment. By undertsting and applying ALM Offensif fund market strength indicators, traders can identify ALM Offensif entry and exit signals to maximize returns.
Daily Balance Of Power | 9.2 T | |||
Rate Of Daily Change | 1.0 | |||
Day Median Price | 315.06 | |||
Day Typical Price | 315.06 | |||
Price Action Indicator | 0.06 | |||
Period Momentum Indicator | 0.12 | |||
Relative Strength Index | 27.85 |
ALM Offensif Risk Indicators
The analysis of ALM Offensif'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 ALM Offensif's investment and either accepting that risk or mitigating it. Along with some essential techniques for forecasting alm 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 | 0.3552 | |||
Semi Deviation | 0.3583 | |||
Standard Deviation | 0.4912 | |||
Variance | 0.2413 | |||
Downside Variance | 0.2506 | |||
Semi Variance | 0.1284 | |||
Expected Short fall | (0.38) |
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
Other Information on Investing in ALM Fund
ALM Offensif financial ratios help investors to determine whether ALM Fund is cheap or expensive when compared to a particular measure, such as profits or enterprise value. In other words, they help investors to determine the cost of investment in ALM with respect to the benefits of owning ALM Offensif security.
Funds Screener Find actively-traded funds from around the world traded on over 30 global exchanges | |
Alpha Finder Use alpha and beta coefficients to find investment opportunities after accounting for the risk | |
Global Correlations Find global opportunities by holding instruments from different markets | |
My Watchlist Analysis Analyze my current watchlist and to refresh optimization strategy. Macroaxis watchlist is based on self-learning algorithm to remember stocks you like |