CPC Stock Forecast - Polynomial Regression
| CPC Stock | 18,200 500.00 2.82% |
CPC |
CPC Polynomial Regression Price Forecast For the 3rd of December
Given 90 days horizon, the Polynomial Regression forecasted value of CPC on the next trading day is expected to be 17,980 with a mean absolute deviation of 139.45, mean absolute percentage error of 38,650, and the sum of the absolute errors of 8,506.Please note that although there have been many attempts to predict CPC 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 CPC's next future price depends linearly on its previous prices and some stochastic term (i.e., imperfectly predictable multiplier).
CPC Stock Forecast Pattern
CPC Forecasted Value
In the context of forecasting CPC'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. CPC's downside and upside margins for the forecasting period are 17,979 and 17,982, respectively. We have considered CPC'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 CPC stock data series using in forecasting. Note that when a statistical model is used to represent CPC 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 | 128.6728 |
| Bias | Arithmetic mean of the errors | None |
| MAD | Mean absolute deviation | 139.4491 |
| MAPE | Mean absolute percentage error | 0.0077 |
| SAE | Sum of the absolute errors | 8506.393 |
Predictive Modules for CPC
There are currently many different techniques concerning forecasting the market as a whole, as well as predicting future values of individual securities such as CPC. 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 CPC
For every potential investor in CPC, whether a beginner or expert, CPC's price movement is the inherent factor that sparks whether it is viable to invest in it or hold it better. CPC Stock price charts are filled with many 'noises.' These noises can hugely alter the decision one can make regarding investing in CPC. Basic forecasting techniques help filter out the noise by identifying CPC's price trends.CPC 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 CPC stock to make a market-neutral strategy. Peer analysis of CPC could also be used in its relative valuation, which is a method of valuing CPC by comparing valuation metrics with similar companies.
| Risk & Return | Correlation |
CPC 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 CPC'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 CPC's current price.| Cycle Indicators | ||
| Math Operators | ||
| Math Transform | ||
| Momentum Indicators | ||
| Overlap Studies | ||
| Pattern Recognition | ||
| Price Transform | ||
| Statistic Functions | ||
| Volatility Indicators | ||
| Volume Indicators |
CPC Market Strength Events
Market strength indicators help investors to evaluate how CPC stock reacts to ongoing and evolving market conditions. The investors can use it to make informed decisions about market timing, and determine when trading CPC shares will generate the highest return on investment. By undertsting and applying CPC stock market strength indicators, traders can identify CPC entry and exit signals to maximize returns.
| Accumulation Distribution | 0.0275 | |||
| Daily Balance Of Power | 1.0 | |||
| Rate Of Daily Change | 1.03 | |||
| Day Median Price | 17950.0 | |||
| Day Typical Price | 18033.33 | |||
| Market Facilitation Index | 500.0 | |||
| Price Action Indicator | 500.0 | |||
| Period Momentum Indicator | 500.0 |
CPC Risk Indicators
The analysis of CPC'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 CPC's investment and either accepting that risk or mitigating it. Along with some essential techniques for forecasting cpc 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 | 0.8648 | |||
| Semi Deviation | 1.13 | |||
| Standard Deviation | 1.4 | |||
| Variance | 1.95 | |||
| Downside Variance | 3.08 | |||
| Semi Variance | 1.27 | |||
| Expected Short fall | (1.45) |
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.
Pair Trading with CPC
One of the main advantages of trading using pair correlations is that every trade hedges away some risk. Because there are two separate transactions required, even if CPC position performs unexpectedly, the other equity can make up some of the losses. Pair trading also minimizes risk from directional movements in the market. For example, if an entire industry or sector drops because of unexpected headlines, the short position in CPC will appreciate offsetting losses from the drop in the long position's value.The ability to find closely correlated positions to CPC could be a great tool in your tax-loss harvesting strategies, allowing investors a quick way to find a similar-enough asset to replace CPC when you sell it. If you don't do this, your portfolio allocation will be skewed against your target asset allocation. So, investors can't just sell and buy back CPC - that would be a violation of the tax code under the "wash sale" rule, and this is why you need to find a similar enough asset and use the proceeds from selling CPC to buy it.
The correlation of CPC is a statistical measure of how it moves in relation to other instruments. This measure is expressed in what is known as the correlation coefficient, which ranges between -1 and +1. A perfect positive correlation (i.e., a correlation coefficient of +1) implies that as CPC moves, either up or down, the other security will move in the same direction. Alternatively, perfect negative correlation means that if CPC moves in either direction, the perfectly negatively correlated security will move in the opposite direction. If the correlation is 0, the equities are not correlated; they are entirely random. A correlation greater than 0.8 is generally described as strong, whereas a correlation less than 0.5 is generally considered weak.
Correlation analysis and pair trading evaluation for CPC can also be used as hedging techniques within a particular sector or industry or even over random equities to generate a better risk-adjusted return on your portfolios.