CarPartsCom Stock Forecast - Simple Regression

PRTS Stock  USD 1.11  0.10  9.90%   
The Simple Regression forecasted value of CarPartsCom on the next trading day is expected to be 0.93 with a mean absolute deviation of 0.06 and the sum of the absolute errors of 4.01. CarPartsCom Stock Forecast is based on your current time horizon.
  
At this time, CarPartsCom's Inventory Turnover is comparatively stable compared to the past year. Payables Turnover is likely to gain to 8.60 in 2024, whereas Receivables Turnover is likely to drop 58.06 in 2024. . Common Stock Shares Outstanding is likely to drop to about 34 M in 2024. Net Loss is likely to drop to about (898.7 K) in 2024.
Simple Regression model is a single variable regression model that attempts to put a straight line through CarPartsCom price points. This line is defined by its gradient or slope, and the point at which it intercepts the x-axis. Mathematically, assuming the independent variable is X and the dependent variable is Y, then this line can be represented as: Y = intercept + slope * X.

CarPartsCom Simple Regression Price Forecast For the 23rd of November

Given 90 days horizon, the Simple Regression forecasted value of CarPartsCom on the next trading day is expected to be 0.93 with a mean absolute deviation of 0.06, mean absolute percentage error of 0.01, and the sum of the absolute errors of 4.01.
Please note that although there have been many attempts to predict CarPartsCom 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 CarPartsCom's next future price depends linearly on its previous prices and some stochastic term (i.e., imperfectly predictable multiplier).

CarPartsCom Stock Forecast Pattern

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CarPartsCom Forecasted Value

In the context of forecasting CarPartsCom'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. CarPartsCom's downside and upside margins for the forecasting period are 0.01 and 5.24, respectively. We have considered CarPartsCom'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
1.11
0.93
Expected Value
5.24
Upside

Model Predictive Factors

The below table displays some essential indicators generated by the model showing the Simple Regression forecasting method's relative quality and the estimations of the prediction error of CarPartsCom stock data series using in forecasting. Note that when a statistical model is used to represent CarPartsCom 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 Criteria114.9504
BiasArithmetic mean of the errors None
MADMean absolute deviation0.0647
MAPEMean absolute percentage error0.0761
SAESum of the absolute errors4.0111
In general, regression methods applied to historical equity returns or prices series is an area of active research. In recent decades, new methods have been developed for robust regression of price series such as CarPartsCom historical returns. These new methods are regression involving correlated responses such as growth curves and different regression methods accommodating various types of missing data.

Predictive Modules for CarPartsCom

There are currently many different techniques concerning forecasting the market as a whole, as well as predicting future values of individual securities such as CarPartsCom. 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 CarPartsCom'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
0.061.155.46
Details
Intrinsic
Valuation
LowRealHigh
0.173.347.65
Details
2 Analysts
Consensus
LowTargetHigh
9.3710.3011.43
Details

Other Forecasting Options for CarPartsCom

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

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 Risk & Return  Correlation

CarPartsCom 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 CarPartsCom'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 CarPartsCom's current price.

CarPartsCom Market Strength Events

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

CarPartsCom Risk Indicators

The analysis of CarPartsCom'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 CarPartsCom's investment and either accepting that risk or mitigating it. Along with some essential techniques for forecasting carpartscom 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.
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.

Thematic Opportunities

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Additional Tools for CarPartsCom Stock Analysis

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