Home Product Stock Forecast - Polynomial Regression
| HMPRO Stock | THB 9.90 0.10 1.02% |
The Polynomial Regression forecasted value of Home Product Center on the next trading day is expected to be 10.13 with a mean absolute deviation of 0.21 and the sum of the absolute errors of 12.69. Home Stock Forecast is based on your current time horizon. Investors can use this forecasting interface to forecast Home Product stock prices and determine the direction of Home Product Center's future trends based on various well-known forecasting models. We recommend always using this module together with an analysis of Home Product's historical fundamentals, such as revenue growth or operating cash flow patterns.
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Home Product Polynomial Regression Price Forecast For the 27th of November
Given 90 days horizon, the Polynomial Regression forecasted value of Home Product Center on the next trading day is expected to be 10.13 with a mean absolute deviation of 0.21, mean absolute percentage error of 0.06, and the sum of the absolute errors of 12.69.Please note that although there have been many attempts to predict Home 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 Home Product's next future price depends linearly on its previous prices and some stochastic term (i.e., imperfectly predictable multiplier).
Home Product Stock Forecast Pattern
| Backtest Home Product | Home Product Price Prediction | Buy or Sell Advice |
Home Product Forecasted Value
In the context of forecasting Home Product'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. Home Product's downside and upside margins for the forecasting period are 8.04 and 12.22, respectively. We have considered Home Product'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.
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 Home Product stock data series using in forecasting. Note that when a statistical model is used to represent Home Product 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 | 115.3338 |
| Bias | Arithmetic mean of the errors | None |
| MAD | Mean absolute deviation | 0.208 |
| MAPE | Mean absolute percentage error | 0.0206 |
| SAE | Sum of the absolute errors | 12.6883 |
Predictive Modules for Home Product
There are currently many different techniques concerning forecasting the market as a whole, as well as predicting future values of individual securities such as Home Product Center. 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.Other Forecasting Options for Home Product
For every potential investor in Home, whether a beginner or expert, Home Product's price movement is the inherent factor that sparks whether it is viable to invest in it or hold it better. Home Stock price charts are filled with many 'noises.' These noises can hugely alter the decision one can make regarding investing in Home. Basic forecasting techniques help filter out the noise by identifying Home Product's price trends.Home Product 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 Home Product stock to make a market-neutral strategy. Peer analysis of Home Product could also be used in its relative valuation, which is a method of valuing Home Product by comparing valuation metrics with similar companies.
| Risk & Return | Correlation |
Home Product Center 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 Home Product'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 Home Product's current price.| Cycle Indicators | ||
| Math Operators | ||
| Math Transform | ||
| Momentum Indicators | ||
| Overlap Studies | ||
| Pattern Recognition | ||
| Price Transform | ||
| Statistic Functions | ||
| Volatility Indicators | ||
| Volume Indicators |
Home Product Market Strength Events
Market strength indicators help investors to evaluate how Home Product stock reacts to ongoing and evolving market conditions. The investors can use it to make informed decisions about market timing, and determine when trading Home Product shares will generate the highest return on investment. By undertsting and applying Home Product stock market strength indicators, traders can identify Home Product Center entry and exit signals to maximize returns.
Home Product Risk Indicators
The analysis of Home Product'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 Home Product's investment and either accepting that risk or mitigating it. Along with some essential techniques for forecasting home 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 | 1.7 | |||
| Semi Deviation | 1.47 | |||
| Standard Deviation | 2.41 | |||
| Variance | 5.8 | |||
| Downside Variance | 3.63 | |||
| Semi Variance | 2.16 | |||
| Expected Short fall | (2.53) |
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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Try AI Portfolio ArchitectOther Information on Investing in Home Stock
Home Product financial ratios help investors to determine whether Home 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 Home with respect to the benefits of owning Home Product security.