Ping An Stock Forecast - Simple Regression

601318 Stock   52.62  0.27  0.51%   
The Simple Regression forecasted value of Ping An Insurance on the next trading day is expected to be 55.16 with a mean absolute deviation of 2.50 and the sum of the absolute errors of 152.73. Ping Stock Forecast is based on your current time horizon. Investors can use this forecasting interface to forecast Ping An stock prices and determine the direction of Ping An Insurance's future trends based on various well-known forecasting models. We recommend always using this module together with an analysis of Ping An's historical fundamentals, such as revenue growth or operating cash flow patterns.
  
At present, Ping An's Net Receivables is projected to increase significantly based on the last few years of reporting. The current year's Other Stockholder Equity is expected to grow to about 17.3 B, whereas Total Assets are forecasted to decline to about 9.1 T.
Simple Regression model is a single variable regression model that attempts to put a straight line through Ping An 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.

Ping An Simple Regression Price Forecast For the 21st of December

Given 90 days horizon, the Simple Regression forecasted value of Ping An Insurance on the next trading day is expected to be 55.16 with a mean absolute deviation of 2.50, mean absolute percentage error of 11.52, and the sum of the absolute errors of 152.73.
Please note that although there have been many attempts to predict Ping 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 Ping An's next future price depends linearly on its previous prices and some stochastic term (i.e., imperfectly predictable multiplier).

Ping An Stock Forecast Pattern

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Ping An Forecasted Value

In the context of forecasting Ping An'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. Ping An's downside and upside margins for the forecasting period are 52.18 and 58.13, respectively. We have considered Ping An'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
52.62
55.16
Expected Value
58.13
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 Ping An stock data series using in forecasting. Note that when a statistical model is used to represent Ping An 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 Criteria120.5542
BiasArithmetic mean of the errors None
MADMean absolute deviation2.5038
MAPEMean absolute percentage error0.0472
SAESum of the absolute errors152.7309
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 Ping An Insurance 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 Ping An

There are currently many different techniques concerning forecasting the market as a whole, as well as predicting future values of individual securities such as Ping An Insurance. 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.
Hype
Prediction
LowEstimatedHigh
50.0953.0656.03
Details
Intrinsic
Valuation
LowRealHigh
47.6062.4565.42
Details
Bollinger
Band Projection (param)
LowMiddleHigh
51.3754.7858.18
Details
Earnings
Estimates (0)
LowProjected EPSHigh
0.000.000.00
Details

Other Forecasting Options for Ping An

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

Ping An 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 Ping An stock to make a market-neutral strategy. Peer analysis of Ping An could also be used in its relative valuation, which is a method of valuing Ping An by comparing valuation metrics with similar companies.
 Risk & Return  Correlation

Ping An Insurance 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 Ping An'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 Ping An's current price.

Ping An Market Strength Events

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

Ping An Risk Indicators

The analysis of Ping An'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 Ping An's investment and either accepting that risk or mitigating it. Along with some essential techniques for forecasting ping 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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Other Information on Investing in Ping Stock

Ping An financial ratios help investors to determine whether Ping Stock is cheap or expensive when compared to a particular measure, such as profits or enterprise value. In other words, they help investors to determine the cost of investment in Ping with respect to the benefits of owning Ping An security.