Hang Seng Pink Sheet Forecast - Naive Prediction

HSNGY Stock  USD 12.17  0.41  3.49%   
The Naive Prediction forecasted value of Hang Seng Bank on the next trading day is expected to be 11.73 with a mean absolute deviation of 0.21 and the sum of the absolute errors of 12.56. Hang Pink Sheet Forecast is based on your current time horizon.
  
A naive forecasting model for Hang Seng is a special case of the moving average forecasting where the number of periods used for smoothing is one. Therefore, the forecast of Hang Seng Bank value for a given trading day is simply the observed value for the previous period. Due to the simplistic nature of the naive forecasting model, it can only be used to forecast up to one period.

Hang Seng Naive Prediction Price Forecast For the 27th of November

Given 90 days horizon, the Naive Prediction forecasted value of Hang Seng Bank on the next trading day is expected to be 11.73 with a mean absolute deviation of 0.21, mean absolute percentage error of 0.06, and the sum of the absolute errors of 12.56.
Please note that although there have been many attempts to predict Hang Pink Sheet 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 Hang Seng's next future price depends linearly on its previous prices and some stochastic term (i.e., imperfectly predictable multiplier).

Hang Seng Pink Sheet Forecast Pattern

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Hang Seng Forecasted Value

In the context of forecasting Hang Seng's Pink Sheet 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. Hang Seng's downside and upside margins for the forecasting period are 9.76 and 13.70, respectively. We have considered Hang Seng'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
12.17
11.73
Expected Value
13.70
Upside

Model Predictive Factors

The below table displays some essential indicators generated by the model showing the Naive Prediction forecasting method's relative quality and the estimations of the prediction error of Hang Seng pink sheet data series using in forecasting. Note that when a statistical model is used to represent Hang Seng pink sheet, 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 Criteria115.3157
BiasArithmetic mean of the errors None
MADMean absolute deviation0.2059
MAPEMean absolute percentage error0.0169
SAESum of the absolute errors12.5591
This model is not at all useful as a medium-long range forecasting tool of Hang Seng Bank. This model is simplistic and is included partly for completeness and partly because of its simplicity. It is unlikely that you'll want to use this model directly to predict Hang Seng. Instead, consider using either the moving average model or the more general weighted moving average model with a higher (i.e., greater than 1) number of periods, and possibly a different set of weights.

Predictive Modules for Hang Seng

There are currently many different techniques concerning forecasting the market as a whole, as well as predicting future values of individual securities such as Hang Seng Bank. Regardless of method or technology, however, to accurately forecast the pink sheet market is more a matter of luck rather than a particular technique. Nevertheless, trying to predict the pink sheet 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
10.2012.1714.14
Details
Intrinsic
Valuation
LowRealHigh
10.2112.1814.15
Details
Please note, it is not enough to conduct a financial or market analysis of a single entity such as Hang Seng. Your research has to be compared to or analyzed against Hang Seng's peers to derive any actionable benefits. When done correctly, Hang Seng's competitive analysis will give you plenty of quantitative and qualitative data to validate your investment decisions or develop an entirely new strategy toward taking a position in Hang Seng Bank.

Other Forecasting Options for Hang Seng

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

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Hang Seng Bank Technical and Predictive Analytics

The pink sheet market is financially volatile. Despite the volatility, there exist limitless possibilities of gaining profits and building passive income portfolios. With the complexity of Hang Seng'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 Hang Seng's current price.

Hang Seng Market Strength Events

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

Hang Seng Risk Indicators

The analysis of Hang Seng'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 Hang Seng's investment and either accepting that risk or mitigating it. Along with some essential techniques for forecasting hang pink sheet 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.

Additional Tools for Hang Pink Sheet Analysis

When running Hang Seng's price analysis, check to measure Hang Seng's 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 Hang Seng is operating at the current time. Most of Hang Seng's value examination focuses on studying past and present price action to predict the probability of Hang Seng's future price movements. You can analyze the entity against its peers and the financial market as a whole to determine factors that move Hang Seng's price. Additionally, you may evaluate how the addition of Hang Seng to your portfolios can decrease your overall portfolio volatility.