Loomis Sayles Mutual Fund Forecast - Triple Exponential Smoothing
| NERNX Fund | USD 11.28 0.03 0.27% |
The Triple Exponential Smoothing forecasted value of Loomis Sayles E on the next trading day is expected to be 11.27 with a mean absolute deviation of 0.04 and the sum of the absolute errors of 2.30. Loomis Mutual Fund Forecast is based on your current time horizon. Investors can use this forecasting interface to forecast Loomis Sayles stock prices and determine the direction of Loomis Sayles E's future trends based on various well-known forecasting models. We recommend always using this module together with an analysis of Loomis Sayles' historical fundamentals, such as revenue growth or operating cash flow patterns.
Check out Historical Fundamental Analysis of Loomis Sayles to cross-verify your projections. Loomis |
Most investors in Loomis Sayles 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 Loomis Sayles' time series price data and predict how it will affect future prices. One of these methodologies is forecasting, which interprets Loomis Sayles' price structures and extracts relationships that further increase the generated results' accuracy.
Triple exponential smoothing for Loomis Sayles - also known as the Winters method - is a refinement of the popular double exponential smoothing model with the addition of periodicity (seasonality) component. Simple exponential smoothing technique works best with data where there are no trend or seasonality components to the data. When Loomis Sayles prices exhibit either an increasing or decreasing trend over time, simple exponential smoothing forecasts tend to lag behind observations. Double exponential smoothing is designed to address this type of data series by taking into account any trend in Loomis Sayles price movement. However, neither of these exponential smoothing models address any seasonality of Loomis Sayles E. Loomis Sayles Triple Exponential Smoothing Price Forecast For the 25th of April
Given 90 days horizon, the Triple Exponential Smoothing forecasted value of Loomis Sayles E on the next trading day is expected to be 11.27 with a mean absolute deviation of 0.04, mean absolute percentage error of 0, and the sum of the absolute errors of 2.30.Please note that although there have been many attempts to predict Loomis 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 Loomis Sayles' next future price depends linearly on its previous prices and some stochastic term (i.e., imperfectly predictable multiplier).
Loomis Sayles Mutual Fund Forecast Pattern
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Loomis Sayles Forecasted Value
In the context of forecasting Loomis Sayles' 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. Loomis Sayles' downside and upside margins for the forecasting period are 10.86 and 11.69, respectively. We have considered Loomis Sayles' 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 Triple Exponential Smoothing forecasting method's relative quality and the estimations of the prediction error of Loomis Sayles mutual fund data series using in forecasting. Note that when a statistical model is used to represent Loomis Sayles 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 | Huge |
| Bias | Arithmetic mean of the errors | 0.0071 |
| MAD | Mean absolute deviation | 0.0384 |
| MAPE | Mean absolute percentage error | 0.0034 |
| SAE | Sum of the absolute errors | 2.3028 |
Predictive Modules for Loomis Sayles
There are currently many different techniques concerning forecasting the market as a whole, as well as predicting future values of individual securities such as Loomis Sayles E. 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 Loomis Sayles' 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 Loomis Sayles
For every potential investor in Loomis, whether a beginner or expert, Loomis Sayles' price movement is the inherent factor that sparks whether it is viable to invest in it or hold it better. Loomis Mutual Fund price charts are filled with many 'noises.' These noises can hugely alter the decision one can make regarding investing in Loomis. Basic forecasting techniques help filter out the noise by identifying Loomis Sayles' price trends.Loomis Sayles 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 Loomis Sayles mutual fund to make a market-neutral strategy. Peer analysis of Loomis Sayles could also be used in its relative valuation, which is a method of valuing Loomis Sayles by comparing valuation metrics with similar companies.
| Risk & Return | Correlation |
Loomis Sayles E 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 Loomis Sayles' 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 Loomis Sayles' current price.| Cycle Indicators | ||
| Math Operators | ||
| Math Transform | ||
| Momentum Indicators | ||
| Overlap Studies | ||
| Pattern Recognition | ||
| Price Transform | ||
| Statistic Functions | ||
| Volatility Indicators | ||
| Volume Indicators |
Loomis Sayles Market Strength Events
Market strength indicators help investors to evaluate how Loomis Sayles 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 Loomis Sayles shares will generate the highest return on investment. By undertsting and applying Loomis Sayles mutual fund market strength indicators, traders can identify Loomis Sayles E entry and exit signals to maximize returns.
| Daily Balance Of Power | 9.2 T | |||
| Rate Of Daily Change | 1.0 | |||
| Day Median Price | 11.28 | |||
| Day Typical Price | 11.28 | |||
| Price Action Indicator | 0.015 | |||
| Period Momentum Indicator | 0.03 |
Loomis Sayles Risk Indicators
The analysis of Loomis Sayles' 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 Loomis Sayles' investment and either accepting that risk or mitigating it. Along with some essential techniques for forecasting loomis 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 | 0.313 | |||
| Standard Deviation | 0.4021 | |||
| Variance | 0.1617 |
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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