UBS AG Etf Forecast - Polynomial Regression
| FBGX Etf | USD 830.46 5.28 0.64% |
The Polynomial Regression forecasted value of UBS AG FI on the next trading day is expected to be 820.48 with a mean absolute deviation of 17.38 and the sum of the absolute errors of 1,078. UBS Etf Forecast is based on your current time horizon. Investors can use this forecasting interface to forecast UBS AG stock prices and determine the direction of UBS AG FI's future trends based on various well-known forecasting models. We recommend always using this module together with an analysis of UBS AG's historical fundamentals, such as revenue growth or operating cash flow patterns.
Check out Historical Fundamental Analysis of UBS AG to cross-verify your projections. UBS |
Most investors in UBS AG cannot accurately predict what will happen the next trading day because, historically, etf 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 UBS AG's time series price data and predict how it will affect future prices. One of these methodologies is forecasting, which interprets UBS AG's price structures and extracts relationships that further increase the generated results' accuracy.
UBS AG polinomial regression implements a single variable polynomial regression model using the daily prices as the independent variable. The coefficients of the regression for UBS AG FI as well as the accuracy indicators are determined from the period prices. UBS AG Polynomial Regression Price Forecast For the 11th of May 2024
Given 90 days horizon, the Polynomial Regression forecasted value of UBS AG FI on the next trading day is expected to be 820.48 with a mean absolute deviation of 17.38, mean absolute percentage error of 426.57, and the sum of the absolute errors of 1,078.Please note that although there have been many attempts to predict UBS Etf 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 AG's next future price depends linearly on its previous prices and some stochastic term (i.e., imperfectly predictable multiplier).
UBS AG Etf Forecast Pattern
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UBS AG Forecasted Value
In the context of forecasting UBS AG's Etf 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 AG's downside and upside margins for the forecasting period are 818.50 and 822.45, respectively. We have considered UBS AG'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 UBS AG etf data series using in forecasting. Note that when a statistical model is used to represent UBS AG etf, 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 | 126.0042 |
| Bias | Arithmetic mean of the errors | None |
| MAD | Mean absolute deviation | 17.3837 |
| MAPE | Mean absolute percentage error | 0.0216 |
| SAE | Sum of the absolute errors | 1077.792 |
Predictive Modules for UBS AG
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 AG FI. Regardless of method or technology, however, to accurately forecast the etf market is more a matter of luck rather than a particular technique. Nevertheless, trying to predict the etf 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 UBS AG'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 UBS AG
For every potential investor in UBS, whether a beginner or expert, UBS AG's price movement is the inherent factor that sparks whether it is viable to invest in it or hold it better. UBS Etf 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 AG's price trends.UBS AG 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 AG etf to make a market-neutral strategy. Peer analysis of UBS AG could also be used in its relative valuation, which is a method of valuing UBS AG by comparing valuation metrics with similar companies.
| Risk & Return | Correlation |
UBS AG FI Technical and Predictive Analytics
The etf market is financially volatile. Despite the volatility, there exist limitless possibilities of gaining profits and building passive income portfolios. With the complexity of UBS AG'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 AG's current price.| Cycle Indicators | ||
| Math Operators | ||
| Math Transform | ||
| Momentum Indicators | ||
| Overlap Studies | ||
| Pattern Recognition | ||
| Price Transform | ||
| Statistic Functions | ||
| Volatility Indicators | ||
| Volume Indicators |
UBS AG Market Strength Events
Market strength indicators help investors to evaluate how UBS AG etf reacts to ongoing and evolving market conditions. The investors can use it to make informed decisions about market timing, and determine when trading UBS AG shares will generate the highest return on investment. By undertsting and applying UBS AG etf market strength indicators, traders can identify UBS AG FI entry and exit signals to maximize returns.
| Daily Balance Of Power | 9.2 T | |||
| Rate Of Daily Change | 1.01 | |||
| Day Median Price | 830.46 | |||
| Day Typical Price | 830.46 | |||
| Price Action Indicator | 2.64 | |||
| Period Momentum Indicator | 5.28 |
UBS AG Risk Indicators
The analysis of UBS AG'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 AG's investment and either accepting that risk or mitigating it. Along with some essential techniques for forecasting ubs etf 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 | 1.49 | |||
| Semi Deviation | 1.62 | |||
| Standard Deviation | 1.96 | |||
| Variance | 3.83 | |||
| Downside Variance | 2.93 | |||
| Semi Variance | 2.63 | |||
| Expected Short fall | (1.71) |
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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Try AI Portfolio ArchitectCheck out Historical Fundamental Analysis of UBS AG to cross-verify your projections. Note that the UBS AG FI information on this page should be used as a complementary analysis to other UBS AG'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 Commodity Directory module to find actively traded commodities issued by global exchanges.
The market value of UBS AG FI is measured differently than its book value, which is the value of UBS that is recorded on the company's balance sheet. Investors also form their own opinion of UBS AG's value that differs from its market value or its book value, called intrinsic value, which is UBS AG's true underlying value. Investors use various methods to calculate intrinsic value and buy a stock when its market value falls below its intrinsic value. Because UBS AG's market value can be influenced by many factors that don't directly affect UBS AG's underlying business (such as a pandemic or basic market pessimism), market value can vary widely from intrinsic value.
Please note, there is a significant difference between UBS AG's value and its price as these two are different measures arrived at by different means. Investors typically determine if UBS AG is a good investment by looking at such factors as earnings, sales, fundamental and technical indicators, competition as well as analyst projections. However, UBS AG's price is the amount at which it trades on the open market and represents the number that a seller and buyer find agreeable to each party.