Janashakthi Insurance Stock Forecast - Polynomial Regression

JINSN0000  LKR 44.90  0.80  1.81%   
The Polynomial Regression forecasted value of Janashakthi Insurance on the next trading day is expected to be 45.08 with a mean absolute deviation of 0.89 and the sum of the absolute errors of 54.42. Janashakthi Stock Forecast is based on your current time horizon. Investors can use this forecasting interface to forecast Janashakthi Insurance stock prices and determine the direction of Janashakthi Insurance's future trends based on various well-known forecasting models. We recommend always using this module together with an analysis of Janashakthi Insurance's historical fundamentals, such as revenue growth or operating cash flow patterns.
  
Janashakthi Insurance polinomial regression implements a single variable polynomial regression model using the daily prices as the independent variable. The coefficients of the regression for Janashakthi Insurance as well as the accuracy indicators are determined from the period prices.

Janashakthi Insurance Polynomial Regression Price Forecast For the 27th of November

Given 90 days horizon, the Polynomial Regression forecasted value of Janashakthi Insurance on the next trading day is expected to be 45.08 with a mean absolute deviation of 0.89, mean absolute percentage error of 1.42, and the sum of the absolute errors of 54.42.
Please note that although there have been many attempts to predict Janashakthi 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 Janashakthi Insurance's next future price depends linearly on its previous prices and some stochastic term (i.e., imperfectly predictable multiplier).

Janashakthi Insurance Stock Forecast Pattern

Janashakthi Insurance Forecasted Value

In the context of forecasting Janashakthi Insurance'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. Janashakthi Insurance's downside and upside margins for the forecasting period are 43.08 and 47.09, respectively. We have considered Janashakthi Insurance'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
44.90
45.08
Expected Value
47.09
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 Janashakthi Insurance stock data series using in forecasting. Note that when a statistical model is used to represent Janashakthi Insurance 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 Criteria118.4603
BiasArithmetic mean of the errors None
MADMean absolute deviation0.8921
MAPEMean absolute percentage error0.0214
SAESum of the absolute errors54.4186
A single variable polynomial regression model attempts to put a curve through the Janashakthi Insurance 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 Janashakthi Insurance

There are currently many different techniques concerning forecasting the market as a whole, as well as predicting future values of individual securities such as Janashakthi 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
42.9244.9046.88
Details
Intrinsic
Valuation
LowRealHigh
40.4147.8049.78
Details
Bollinger
Band Projection (param)
LowMiddleHigh
43.6045.7347.86
Details

Other Forecasting Options for Janashakthi Insurance

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

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

Janashakthi 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 Janashakthi Insurance'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 Janashakthi Insurance's current price.

Janashakthi Insurance Market Strength Events

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

Janashakthi Insurance Risk Indicators

The analysis of Janashakthi Insurance'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 Janashakthi Insurance's investment and either accepting that risk or mitigating it. Along with some essential techniques for forecasting janashakthi 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 Janashakthi Stock

Janashakthi Insurance financial ratios help investors to determine whether Janashakthi 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 Janashakthi with respect to the benefits of owning Janashakthi Insurance security.