PUMA SE Pink Sheet Forecast - Simple Exponential Smoothing
| PUMSY Stock | USD 4.63 0.13 2.89% |
The Simple Exponential Smoothing forecasted value of PUMA SE on the next trading day is expected to be 4.63 with a mean absolute deviation of 0.07 and the sum of the absolute errors of 3.98. PUMA Pink Sheet Forecast is based on your current time horizon.
PUMA |
PUMA SE Simple Exponential Smoothing Price Forecast For the 27th of November
Given 90 days horizon, the Simple Exponential Smoothing forecasted value of PUMA SE on the next trading day is expected to be 4.63 with a mean absolute deviation of 0.07, mean absolute percentage error of 0.01, and the sum of the absolute errors of 3.98.Please note that although there have been many attempts to predict PUMA Pink Sheet 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 PUMA SE's next future price depends linearly on its previous prices and some stochastic term (i.e., imperfectly predictable multiplier).
PUMA SE Pink Sheet Forecast Pattern
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PUMA SE Forecasted Value
In the context of forecasting PUMA SE's Pink Sheet 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. PUMA SE's downside and upside margins for the forecasting period are 2.61 and 6.65, respectively. We have considered PUMA SE'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 Exponential Smoothing forecasting method's relative quality and the estimations of the prediction error of PUMA SE pink sheet data series using in forecasting. Note that when a statistical model is used to represent PUMA SE pink sheet, 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 | 111.4177 |
| Bias | Arithmetic mean of the errors | -0.005 |
| MAD | Mean absolute deviation | 0.0663 |
| MAPE | Mean absolute percentage error | 0.0152 |
| SAE | Sum of the absolute errors | 3.98 |
Predictive Modules for PUMA SE
There are currently many different techniques concerning forecasting the market as a whole, as well as predicting future values of individual securities such as PUMA SE. Regardless of method or technology, however, to accurately forecast the pink sheet market is more a matter of luck rather than a particular technique. Nevertheless, trying to predict the pink sheet 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 PUMA SE'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 PUMA SE
For every potential investor in PUMA, whether a beginner or expert, PUMA SE's price movement is the inherent factor that sparks whether it is viable to invest in it or hold it better. PUMA Pink Sheet price charts are filled with many 'noises.' These noises can hugely alter the decision one can make regarding investing in PUMA. Basic forecasting techniques help filter out the noise by identifying PUMA SE's price trends.PUMA SE 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 PUMA SE pink sheet to make a market-neutral strategy. Peer analysis of PUMA SE could also be used in its relative valuation, which is a method of valuing PUMA SE by comparing valuation metrics with similar companies.
| Risk & Return | Correlation |
PUMA SE Technical and Predictive Analytics
The pink sheet market is financially volatile. Despite the volatility, there exist limitless possibilities of gaining profits and building passive income portfolios. With the complexity of PUMA SE'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 PUMA SE's current price.| Cycle Indicators | ||
| Math Operators | ||
| Math Transform | ||
| Momentum Indicators | ||
| Overlap Studies | ||
| Pattern Recognition | ||
| Price Transform | ||
| Statistic Functions | ||
| Volatility Indicators | ||
| Volume Indicators |
PUMA SE Market Strength Events
Market strength indicators help investors to evaluate how PUMA SE pink sheet reacts to ongoing and evolving market conditions. The investors can use it to make informed decisions about market timing, and determine when trading PUMA SE shares will generate the highest return on investment. By undertsting and applying PUMA SE pink sheet market strength indicators, traders can identify PUMA SE entry and exit signals to maximize returns.
PUMA SE Risk Indicators
The analysis of PUMA SE'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 PUMA SE's investment and either accepting that risk or mitigating it. Along with some essential techniques for forecasting puma pink sheet 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 | 1.56 | |||
| Semi Deviation | 1.59 | |||
| Standard Deviation | 2.04 | |||
| Variance | 4.15 | |||
| Downside Variance | 3.19 | |||
| Semi Variance | 2.54 | |||
| Expected Short fall | (1.92) |
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.Additional Tools for PUMA Pink Sheet Analysis
When running PUMA SE's price analysis, check to measure PUMA SE's market volatility, profitability, liquidity, solvency, efficiency, growth potential, financial leverage, and other vital indicators. We have many different tools that can be utilized to determine how healthy PUMA SE is operating at the current time. Most of PUMA SE's value examination focuses on studying past and present price action to predict the probability of PUMA SE's future price movements. You can analyze the entity against its peers and the financial market as a whole to determine factors that move PUMA SE's price. Additionally, you may evaluate how the addition of PUMA SE to your portfolios can decrease your overall portfolio volatility.