UBS Institutional Fund Forecast - Simple Exponential Smoothing

0P00014F7Y   1,267  0.00  0.00%   
The Simple Exponential Smoothing forecasted value of UBS Institutional on the next trading day is expected to be 1,267 with a mean absolute deviation of 7.09 and the sum of the absolute errors of 425.61. Investors can use prediction functions to forecast UBS Institutional's fund prices and determine the direction of UBS Institutional's future trends based on various well-known forecasting models. However, exclusively looking at the historical price movement is usually misleading.
  
UBS Institutional simple exponential smoothing forecast is a very popular model used to produce a smoothed price series. Whereas in simple Moving Average models the past observations for UBS Institutional are weighted equally, Exponential Smoothing assigns exponentially decreasing weights as UBS Institutional prices get older.

UBS Institutional Simple Exponential Smoothing Price Forecast For the 10th of January

Given 90 days horizon, the Simple Exponential Smoothing forecasted value of UBS Institutional on the next trading day is expected to be 1,267 with a mean absolute deviation of 7.09, mean absolute percentage error of 90.17, and the sum of the absolute errors of 425.61.
Please note that although there have been many attempts to predict UBS 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 UBS Institutional's next future price depends linearly on its previous prices and some stochastic term (i.e., imperfectly predictable multiplier).

UBS Institutional Fund Forecast Pattern

UBS Institutional Forecasted Value

In the context of forecasting UBS Institutional'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. UBS Institutional's downside and upside margins for the forecasting period are 1,267 and 1,268, respectively. We have considered UBS Institutional'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.
Market Value
1,267
1,267
Expected Value
1,268
Upside

Model Predictive Factors

The below table displays some essential indicators generated by the model showing the Simple Exponential Smoothing forecasting method's relative quality and the estimations of the prediction error of UBS Institutional fund data series using in forecasting. Note that when a statistical model is used to represent UBS Institutional 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.
AICAkaike Information Criteria120.7743
BiasArithmetic mean of the errors 0.7228
MADMean absolute deviation7.0935
MAPEMean absolute percentage error0.0056
SAESum of the absolute errors425.61
This simple exponential smoothing model begins by setting UBS Institutional forecast for the second period equal to the observation of the first period. In other words, recent UBS Institutional observations are given relatively more weight in forecasting than the older observations.

Predictive Modules for UBS Institutional

There are currently many different techniques concerning forecasting the market as a whole, as well as predicting future values of individual securities such as UBS Institutional. 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 UBS Institutional

For every potential investor in UBS, whether a beginner or expert, UBS Institutional's price movement is the inherent factor that sparks whether it is viable to invest in it or hold it better. UBS Fund price charts are filled with many 'noises.' These noises can hugely alter the decision one can make regarding investing in UBS. Basic forecasting techniques help filter out the noise by identifying UBS Institutional's price trends.

UBS Institutional 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 UBS Institutional fund to make a market-neutral strategy. Peer analysis of UBS Institutional could also be used in its relative valuation, which is a method of valuing UBS Institutional by comparing valuation metrics with similar companies.
 Risk & Return  Correlation

UBS Institutional 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 UBS Institutional'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 UBS Institutional's current price.

UBS Institutional Market Strength Events

Market strength indicators help investors to evaluate how UBS Institutional fund reacts to ongoing and evolving market conditions. The investors can use it to make informed decisions about market timing, and determine when trading UBS Institutional shares will generate the highest return on investment. By undertsting and applying UBS Institutional fund market strength indicators, traders can identify UBS Institutional entry and exit signals to maximize returns.

UBS Institutional Risk Indicators

The analysis of UBS Institutional'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 UBS Institutional's investment and either accepting that risk or mitigating it. Along with some essential techniques for forecasting ubs 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.
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.
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
ETFs
Find actively traded Exchange Traded Funds (ETF) from around the world
Portfolio Backtesting
Avoid under-diversification and over-optimization by backtesting your portfolios
Sync Your Broker
Sync your existing holdings, watchlists, positions or portfolios from thousands of online brokerage services, banks, investment account aggregators and robo-advisors.