Spark Networks Stock Forecast - Polynomial Regression

LOVDelisted Stock  USD 0.28  0.05  21.74%   
The Polynomial Regression forecasted value of Spark Networks SE on the next trading day is expected to be 0.14 with a mean absolute deviation of 0.08 and the sum of the absolute errors of 4.80. Spark Stock Forecast is based on your current time horizon.
  
Spark Networks polinomial regression implements a single variable polynomial regression model using the daily prices as the independent variable. The coefficients of the regression for Spark Networks SE as well as the accuracy indicators are determined from the period prices.

Spark Networks Polynomial Regression Price Forecast For the 25th of January

Given 90 days horizon, the Polynomial Regression forecasted value of Spark Networks SE on the next trading day is expected to be 0.14 with a mean absolute deviation of 0.08, mean absolute percentage error of 0.01, and the sum of the absolute errors of 4.80.
Please note that although there have been many attempts to predict Spark 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 Spark Networks' next future price depends linearly on its previous prices and some stochastic term (i.e., imperfectly predictable multiplier).

Spark Networks Stock Forecast Pattern

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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 Spark Networks stock data series using in forecasting. Note that when a statistical model is used to represent Spark Networks 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 Criteria113.4609
BiasArithmetic mean of the errors None
MADMean absolute deviation0.0787
MAPEMean absolute percentage error0.1566
SAESum of the absolute errors4.8021
A single variable polynomial regression model attempts to put a curve through the Spark Networks 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 Spark Networks

There are currently many different techniques concerning forecasting the market as a whole, as well as predicting future values of individual securities such as Spark Networks SE. 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
0.280.280.28
Details
Intrinsic
Valuation
LowRealHigh
0.270.270.31
Details

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

Spark Networks Market Strength Events

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

Thematic Opportunities

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Other Consideration for investing in Spark Stock

If you are still planning to invest in Spark Networks SE check if it may still be traded through OTC markets such as Pink Sheets or OTC Bulletin Board. You may also purchase it directly from the company, but this is not always possible and may require contacting the company directly. Please note that delisted stocks are often considered to be more risky investments, as they are no longer subject to the same regulatory and reporting requirements as listed stocks. Therefore, it is essential to carefully research the Spark Networks' history and understand the potential risks before investing.
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