Co Diagnostics Stock Forecast - Polynomial Regression

CODX Stock  USD 1.01  0.05  4.72%   
The Polynomial Regression forecasted value of Co Diagnostics on the next trading day is expected to be 0.95 with a mean absolute deviation of 0.03 and the sum of the absolute errors of 1.62. CODX Stock Forecast is based on your current time horizon.
  
Receivables Turnover is likely to rise to 21.62 in 2024, whereas Inventory Turnover is likely to drop 1.43 in 2024. . Common Stock Shares Outstanding is likely to drop to about 22.2 M in 2024. Net Loss is likely to rise to about (15.6 M) in 2024.
Co Diagnostics polinomial regression implements a single variable polynomial regression model using the daily prices as the independent variable. The coefficients of the regression for Co Diagnostics as well as the accuracy indicators are determined from the period prices.

Co Diagnostics Polynomial Regression Price Forecast For the 26th of November

Given 90 days horizon, the Polynomial Regression forecasted value of Co Diagnostics on the next trading day is expected to be 0.95 with a mean absolute deviation of 0.03, mean absolute percentage error of 0, and the sum of the absolute errors of 1.62.
Please note that although there have been many attempts to predict CODX 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 Co Diagnostics' next future price depends linearly on its previous prices and some stochastic term (i.e., imperfectly predictable multiplier).

Co Diagnostics Stock Forecast Pattern

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Co Diagnostics Forecasted Value

In the context of forecasting Co Diagnostics' 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. Co Diagnostics' downside and upside margins for the forecasting period are 0.01 and 4.19, respectively. We have considered Co Diagnostics' 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
1.01
0.95
Expected Value
4.19
Upside

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 Co Diagnostics stock data series using in forecasting. Note that when a statistical model is used to represent Co Diagnostics 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 Criteria111.3663
BiasArithmetic mean of the errors None
MADMean absolute deviation0.0266
MAPEMean absolute percentage error0.0216
SAESum of the absolute errors1.6215
A single variable polynomial regression model attempts to put a curve through the Co Diagnostics historical price points. Mathematically, assuming the independent variable is X and the dependent variable is Y, this line can be indicated as: Y = a0 + a1*X + a2*X2 + a3*X3 + ... + am*Xm

Predictive Modules for Co Diagnostics

There are currently many different techniques concerning forecasting the market as a whole, as well as predicting future values of individual securities such as Co Diagnostics. 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 Co Diagnostics' 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
0.051.024.26
Details
Intrinsic
Valuation
LowRealHigh
0.061.254.49
Details
1 Analysts
Consensus
LowTargetHigh
1.822.002.22
Details

Other Forecasting Options for Co Diagnostics

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

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 Risk & Return  Correlation

Co Diagnostics 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 Co Diagnostics' 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 Co Diagnostics' current price.

Co Diagnostics Market Strength Events

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

Co Diagnostics Risk Indicators

The analysis of Co Diagnostics' 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 Co Diagnostics' investment and either accepting that risk or mitigating it. Along with some essential techniques for forecasting codx 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.

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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 CODX Stock Analysis

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