Zoned Properties OTC Stock Forecast - Simple Regression
| ZDPY Stock | USD 0.54 0.04 6.90% |
The Simple Regression forecasted value of Zoned Properties on the next trading day is expected to be 0.55 with a mean absolute deviation of 0.02 and the sum of the absolute errors of 1.45. Zoned OTC Stock Forecast is based on your current time horizon. Investors can use this forecasting interface to forecast Zoned Properties stock prices and determine the direction of Zoned Properties's future trends based on various well-known forecasting models. We recommend always using this module together with an analysis of Zoned Properties' historical fundamentals, such as revenue growth or operating cash flow patterns.
Check out Historical Fundamental Analysis of Zoned Properties to cross-verify your projections. Zoned |
Most investors in Zoned Properties cannot accurately predict what will happen the next trading day because, historically, stock markets tend to be unpredictable and even illogical. Modeling turbulent structures requires applying different statistical methods, techniques, and algorithms to find hidden data structures or patterns within the Zoned Properties' time series price data and predict how it will affect future prices. One of these methodologies is forecasting, which interprets Zoned Properties' price structures and extracts relationships that further increase the generated results' accuracy.
Simple Regression model is a single variable regression model that attempts to put a straight line through Zoned Properties 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. Zoned Properties Simple Regression Price Forecast For the 25th of April
Given 90 days horizon, the Simple Regression forecasted value of Zoned Properties on the next trading day is expected to be 0.55 with a mean absolute deviation of 0.02, mean absolute percentage error of 0.0009, and the sum of the absolute errors of 1.45.Please note that although there have been many attempts to predict Zoned OTC 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 Zoned Properties' next future price depends linearly on its previous prices and some stochastic term (i.e., imperfectly predictable multiplier).
Zoned Properties OTC Stock Forecast Pattern
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Zoned Properties Forecasted Value
In the context of forecasting Zoned Properties' OTC 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. Zoned Properties' downside and upside margins for the forecasting period are 0.01 and 6.30, respectively. We have considered Zoned Properties' 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 Regression forecasting method's relative quality and the estimations of the prediction error of Zoned Properties otc stock data series using in forecasting. Note that when a statistical model is used to represent Zoned Properties otc 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 | 111.0786 |
| Bias | Arithmetic mean of the errors | None |
| MAD | Mean absolute deviation | 0.0238 |
| MAPE | Mean absolute percentage error | 0.0477 |
| SAE | Sum of the absolute errors | 1.4508 |
Predictive Modules for Zoned Properties
There are currently many different techniques concerning forecasting the market as a whole, as well as predicting future values of individual securities such as Zoned Properties. Regardless of method or technology, however, to accurately forecast the otc stock market is more a matter of luck rather than a particular technique. Nevertheless, trying to predict the otc 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 Zoned Properties' 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.
Other Forecasting Options for Zoned Properties
For every potential investor in Zoned, whether a beginner or expert, Zoned Properties' price movement is the inherent factor that sparks whether it is viable to invest in it or hold it better. Zoned OTC Stock price charts are filled with many 'noises.' These noises can hugely alter the decision one can make regarding investing in Zoned. Basic forecasting techniques help filter out the noise by identifying Zoned Properties' price trends.Zoned Properties 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 Zoned Properties otc stock to make a market-neutral strategy. Peer analysis of Zoned Properties could also be used in its relative valuation, which is a method of valuing Zoned Properties by comparing valuation metrics with similar companies.
| Risk & Return | Correlation |
Zoned Properties Technical and Predictive Analytics
The otc stock market is financially volatile. Despite the volatility, there exist limitless possibilities of gaining profits and building passive income portfolios. With the complexity of Zoned Properties' 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 Zoned Properties' current price.| Cycle Indicators | ||
| Math Operators | ||
| Math Transform | ||
| Momentum Indicators | ||
| Overlap Studies | ||
| Pattern Recognition | ||
| Price Transform | ||
| Statistic Functions | ||
| Volatility Indicators | ||
| Volume Indicators |
Zoned Properties Market Strength Events
Market strength indicators help investors to evaluate how Zoned Properties otc stock reacts to ongoing and evolving market conditions. The investors can use it to make informed decisions about market timing, and determine when trading Zoned Properties shares will generate the highest return on investment. By undertsting and applying Zoned Properties otc stock market strength indicators, traders can identify Zoned Properties entry and exit signals to maximize returns.
Zoned Properties Risk Indicators
The analysis of Zoned Properties' 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 Zoned Properties' investment and either accepting that risk or mitigating it. Along with some essential techniques for forecasting zoned otc 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 | 3.6 | |||
| Semi Deviation | 3.87 | |||
| Standard Deviation | 5.74 | |||
| Variance | 32.99 | |||
| Downside Variance | 38.27 | |||
| Semi Variance | 14.96 | |||
| Expected Short fall | (7.11) |
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.
Also Currently Popular
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.Check out Historical Fundamental Analysis of Zoned Properties to cross-verify your projections. You can also try the Sync Your Broker module to sync your existing holdings, watchlists, positions or portfolios from thousands of online brokerage services, banks, investment account aggregators and robo-advisors..
Complementary Tools for Zoned OTC Stock analysis
When running Zoned Properties' price analysis, check to measure Zoned Properties' 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 Zoned Properties is operating at the current time. Most of Zoned Properties' value examination focuses on studying past and present price action to predict the probability of Zoned Properties' future price movements. You can analyze the entity against its peers and the financial market as a whole to determine factors that move Zoned Properties' price. Additionally, you may evaluate how the addition of Zoned Properties to your portfolios can decrease your overall portfolio volatility.
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