The Polynomial Regression forecasted value of VN Index on the next trading day is expected to be 1,263 with a mean absolute deviation of 15.69 and the sum of the absolute errors of 972.94. Investors can use prediction functions to forecast VN Index's index prices and determine the direction of VN Index's future trends based on various well-known forecasting models. However, exclusively looking at the historical price movement is usually misleading.
VN Index polinomial regression implements a single variable polynomial regression model using the daily prices as the independent variable. The coefficients of the regression for VN Index as well as the accuracy indicators are determined from the period prices.
VN Index Polynomial Regression Price Forecast For the 20th of January
Given 90 days horizon, the Polynomial Regression forecasted value of VN Index on the next trading day is expected to be 1,266 with a mean absolute deviation of 8.90, mean absolute percentage error of 126.99, and the sum of the absolute errors of 542.88.
Please note that although there have been many attempts to predict VNI Index 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 VN Index's next future price depends linearly on its previous prices and some stochastic term (i.e., imperfectly predictable multiplier).
VN Index Index Forecast Pattern
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 VN Index index data series using in forecasting. Note that when a statistical model is used to represent VN Index index, 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
122.9546
Bias
Arithmetic mean of the errors
None
MAD
Mean absolute deviation
8.8997
MAPE
Mean absolute percentage error
0.0071
SAE
Sum of the absolute errors
542.8801
A single variable polynomial regression model attempts to put a curve through the VN Index 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 VN Index
There are currently many different techniques concerning forecasting the market as a whole, as well as predicting future values of individual securities such as VN Index. Regardless of method or technology, however, to accurately forecast the index market is more a matter of luck rather than a particular technique. Nevertheless, trying to predict the index 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.
VN Index 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 VN Index index to make a market-neutral strategy. Peer analysis of VN Index could also be used in its relative valuation, which is a method of valuing VN Index by comparing valuation metrics with similar companies.
Market strength indicators help investors to evaluate how VN Index index reacts to ongoing and evolving market conditions. The investors can use it to make informed decisions about market timing, and determine when trading VN Index shares will generate the highest return on investment. By undertsting and applying VN Index index market strength indicators, traders can identify VN Index entry and exit signals to maximize returns.
The analysis of VN Index'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 VN Index's investment and either accepting that risk or mitigating it. Along with some essential techniques for forecasting vni index 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.