Electric Car Stock Forecast - Naive Prediction

ELCR Stock  USD 0.0001  0.00  0.00%   
The Naive Prediction forecasted value of Electric Car on the next trading day is expected to be 0.0001 with a mean absolute deviation of 0 and the sum of the absolute errors of 0. Electric Stock Forecast is based on your current time horizon. Although Electric Car's naive historical forecasting may sometimes provide an important future outlook for the firm, we recommend always cross-verifying it against solid analysis of Electric Car's systematic risk associated with finding meaningful patterns of Electric Car fundamentals over time.
  
As of 12/11/2024, Inventory Turnover is likely to grow to 5.56, while Payables Turnover is likely to drop 0.38. . As of 12/11/2024, Net Loss is likely to grow to about (1.6 M).

Electric Car Cash Forecast

Forecasting financial indicators like cash flow involves analysts applying various statistical methods, techniques, and algorithms. These tools reveal hidden trends within the Electric Car's financial statements to estimate their effects on upcoming price movements.
 
Cash  
First Reported
2010-12-31
Previous Quarter
52.5 K
Current Value
55.2 K
Quarterly Volatility
12.4 K
 
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Yuan Drop
 
Covid
A naive forecasting model for Electric Car is a special case of the moving average forecasting where the number of periods used for smoothing is one. Therefore, the forecast of Electric Car value for a given trading day is simply the observed value for the previous period. Due to the simplistic nature of the naive forecasting model, it can only be used to forecast up to one period.

Electric Car Naive Prediction Price Forecast For the 12th of December 2024

Given 90 days horizon, the Naive Prediction forecasted value of Electric Car on the next trading day is expected to be 0.0001 with a mean absolute deviation of 0, mean absolute percentage error of 0, and the sum of the absolute errors of 0.
Please note that although there have been many attempts to predict Electric 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 Electric Car's next future price depends linearly on its previous prices and some stochastic term (i.e., imperfectly predictable multiplier).

Electric Car Stock Forecast Pattern

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Electric Car Forecasted Value

In the context of forecasting Electric Car'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. Electric Car's downside and upside margins for the forecasting period are 0.0001 and 0.0001, respectively. We have considered Electric Car'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
0.0001
0.0001
Downside
0.0001
Expected Value
0.0001
Upside

Model Predictive Factors

The below table displays some essential indicators generated by the model showing the Naive Prediction forecasting method's relative quality and the estimations of the prediction error of Electric Car stock data series using in forecasting. Note that when a statistical model is used to represent Electric Car 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.
AICAkaike Information Criteria30.385
BiasArithmetic mean of the errors None
MADMean absolute deviation0.0
MAPEMean absolute percentage error0.0
SAESum of the absolute errors0.0
This model is not at all useful as a medium-long range forecasting tool of Electric Car. This model is simplistic and is included partly for completeness and partly because of its simplicity. It is unlikely that you'll want to use this model directly to predict Electric Car. Instead, consider using either the moving average model or the more general weighted moving average model with a higher (i.e., greater than 1) number of periods, and possibly a different set of weights.

Predictive Modules for Electric Car

There are currently many different techniques concerning forecasting the market as a whole, as well as predicting future values of individual securities such as Electric Car. 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.
Sophisticated investors, who have witnessed many market ups and downs, anticipate that the market will even out over time. This tendency of Electric Car'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.
Hype
Prediction
LowEstimatedHigh
0.000.00010.00
Details
Intrinsic
Valuation
LowRealHigh
0.000.0000840.00
Details
Bollinger
Band Projection (param)
LowMiddleHigh
0.00010.00010.0001
Details

Other Forecasting Options for Electric Car

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

View Electric Car Related Equities

 Risk & Return  Correlation

Electric Car 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 Electric Car'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 Electric Car's current price.

Electric Car Market Strength Events

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

Pair Trading with Electric Car

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 Electric Car 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 Electric Car will appreciate offsetting losses from the drop in the long position's value.
The ability to find closely correlated positions to Electric Car could be a great tool in your tax-loss harvesting strategies, allowing investors a quick way to find a similar-enough asset to replace Electric Car 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 Electric Car - 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 Electric Car to buy it.
The correlation of Electric Car 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 Electric Car moves, either up or down, the other security will move in the same direction. Alternatively, perfect negative correlation means that if Electric Car 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 Electric Car 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.
Pair CorrelationCorrelation Matching

Additional Tools for Electric Stock Analysis

When running Electric Car's price analysis, check to measure Electric Car'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 Electric Car is operating at the current time. Most of Electric Car's value examination focuses on studying past and present price action to predict the probability of Electric Car's future price movements. You can analyze the entity against its peers and the financial market as a whole to determine factors that move Electric Car's price. Additionally, you may evaluate how the addition of Electric Car to your portfolios can decrease your overall portfolio volatility.