Kaiser Aluminum Stock Forecast - Simple Exponential Smoothing
| KLU1 Stock | EUR 75.00 3.50 4.46% |
The Simple Exponential Smoothing forecasted value of Kaiser Aluminum on the next trading day is expected to be 75.00 with a mean absolute deviation of 1.40 and the sum of the absolute errors of 83.74. Kaiser Stock Forecast is based on your current time horizon. We recommend always using this module together with an analysis of Kaiser Aluminum's historical fundamentals, such as revenue growth or operating cash flow patterns.
Kaiser |
Kaiser Aluminum Simple Exponential Smoothing Price Forecast For the 27th of November
Given 90 days horizon, the Simple Exponential Smoothing forecasted value of Kaiser Aluminum on the next trading day is expected to be 75.00 with a mean absolute deviation of 1.40, mean absolute percentage error of 3.78, and the sum of the absolute errors of 83.74.Please note that although there have been many attempts to predict Kaiser Stock 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 Kaiser Aluminum's next future price depends linearly on its previous prices and some stochastic term (i.e., imperfectly predictable multiplier).
Kaiser Aluminum Stock Forecast Pattern
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Kaiser Aluminum Forecasted Value
In the context of forecasting Kaiser Aluminum's Stock 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. Kaiser Aluminum's downside and upside margins for the forecasting period are 72.18 and 77.82, respectively. We have considered Kaiser Aluminum'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 Kaiser Aluminum stock data series using in forecasting. Note that when a statistical model is used to represent Kaiser Aluminum stock, 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 | 117.6032 |
| Bias | Arithmetic mean of the errors | -0.147 |
| MAD | Mean absolute deviation | 1.3957 |
| MAPE | Mean absolute percentage error | 0.0203 |
| SAE | Sum of the absolute errors | 83.74 |
Predictive Modules for Kaiser Aluminum
There are currently many different techniques concerning forecasting the market as a whole, as well as predicting future values of individual securities such as Kaiser Aluminum. Regardless of method or technology, however, to accurately forecast the stock market is more a matter of luck rather than a particular technique. Nevertheless, trying to predict the stock 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 Kaiser Aluminum
For every potential investor in Kaiser, whether a beginner or expert, Kaiser Aluminum's price movement is the inherent factor that sparks whether it is viable to invest in it or hold it better. Kaiser Stock price charts are filled with many 'noises.' These noises can hugely alter the decision one can make regarding investing in Kaiser. Basic forecasting techniques help filter out the noise by identifying Kaiser Aluminum's price trends.Kaiser Aluminum 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 Kaiser Aluminum stock to make a market-neutral strategy. Peer analysis of Kaiser Aluminum could also be used in its relative valuation, which is a method of valuing Kaiser Aluminum by comparing valuation metrics with similar companies.
| Risk & Return | Correlation |
Kaiser Aluminum Technical and Predictive Analytics
The stock market is financially volatile. Despite the volatility, there exist limitless possibilities of gaining profits and building passive income portfolios. With the complexity of Kaiser Aluminum'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 Kaiser Aluminum's current price.| Cycle Indicators | ||
| Math Operators | ||
| Math Transform | ||
| Momentum Indicators | ||
| Overlap Studies | ||
| Pattern Recognition | ||
| Price Transform | ||
| Statistic Functions | ||
| Volatility Indicators | ||
| Volume Indicators |
Kaiser Aluminum Market Strength Events
Market strength indicators help investors to evaluate how Kaiser Aluminum stock reacts to ongoing and evolving market conditions. The investors can use it to make informed decisions about market timing, and determine when trading Kaiser Aluminum shares will generate the highest return on investment. By undertsting and applying Kaiser Aluminum stock market strength indicators, traders can identify Kaiser Aluminum entry and exit signals to maximize returns.
| Daily Balance Of Power | (9,223,372,036,855) | |||
| Rate Of Daily Change | 0.96 | |||
| Day Median Price | 75.0 | |||
| Day Typical Price | 75.0 | |||
| Price Action Indicator | (1.75) | |||
| Period Momentum Indicator | (3.50) |
Kaiser Aluminum Risk Indicators
The analysis of Kaiser Aluminum'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 Kaiser Aluminum's investment and either accepting that risk or mitigating it. Along with some essential techniques for forecasting kaiser stock 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 | 2.14 | |||
| Semi Deviation | 1.87 | |||
| Standard Deviation | 2.86 | |||
| Variance | 8.15 | |||
| Downside Variance | 5.12 | |||
| Semi Variance | 3.49 | |||
| Expected Short fall | (3.02) |
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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Additional Information and Resources on Investing in Kaiser Stock
When determining whether Kaiser Aluminum is a good investment, qualitative aspects like company management, corporate governance, and ethical practices play a significant role. A comparison with peer companies also provides context and helps to understand if Kaiser Stock is undervalued or overvalued. This multi-faceted approach, blending both quantitative and qualitative analysis, forms a solid foundation for making an informed investment decision about Kaiser Aluminum Stock. Highlighted below are key reports to facilitate an investment decision about Kaiser Aluminum Stock:Check out Historical Fundamental Analysis of Kaiser Aluminum to cross-verify your projections. For more detail on how to invest in Kaiser Stock please use our How to Invest in Kaiser Aluminum guide.You can also try the Performance Analysis module to check effects of mean-variance optimization against your current asset allocation.