Spark Networks Stock Forecast - Polynomial Regression

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LOV -- USA Stock  

USD 2.98  0.02  0.67%

Spark Networks Stock Forecast is based on your current time horizon. Investors can use this forecasting interface to forecast Spark Networks historical stock prices and determine the direction of Spark Networks future trends based on various well-known forecasting models. However, solely looking at the historical price movement is usually misleading. Macroaxis recommends to always use this module together with analysis of Spark Networks historical fundamentals such as revenue growth or operating cash flow patterns. Although Spark Networks naive historical forecasting may sometimes provide an important future outlook for the firm we recommend to always cross-verify it against solid analysis of Spark Networks systematic risk associated with finding meaningful patterns of Spark Networks fundamentals over time. Additionally, see Historical Fundamental Analysis of Spark Networks to cross-verify your projections.

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Spark Networks Trade and Non Trade Payables is fairly stable at the moment as compared to the last year. Spark Networks reported Trade and Non Trade Payables of 26.48 Million in 2019. Property Plant and Equipment Net is likely to grow to 3,695 in 2020, whereas Current Assets are likely to drop slightly above 9.4 M in 2020.
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 as well as the accuracy indicators are determined from the period prices.

Spark Networks Polynomial Regression Price Forecast For the 30th of May

Given 30 days horizon, the forecasted value of Spark Networks on the next trading day is expected to be  2.14  with a mean absolute deviation of  0.30 , mean absolute percentage error of  0.13 , and the sum of the absolute errors of  18.40 
 2.14 

Spark Networks Stock Forecast Pattern

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Spark Networks Forecasted Value

Market Value
2.98
29th of May 2020
0.0298
Downside
2.14
Expected Value
8.69
Upside

Model Predictive Factors

AICAkaike Information Criteria116.0684
BiasArithmetic mean of the errors None
MADMean absolute deviation0.3016
MAPEMean absolute percentage error0.1026
SAESum of the absolute errors18.4001
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. 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.
Earnings
Estimates (1)
LowProjected EPSHigh
-0.14-0.14-0.14
Details
Hype
Prediction
LowEstimated ValueHigh
0.122.408.99
Details
Intrinsic
Valuation
LowReal ValueHigh
0.214.1110.70
Details
Bollinger
Band Projection (param)
LowerMiddle BandUpper
2.592.823.05
Details
Analysts
Consensus (1)
LowTarget PriceHigh
7.507.507.50
Details

Other Forecasting Options for Spark Networks

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Current Sentiment - LOV

Spark Networks Investor Sentiment

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Additionally, see Historical Fundamental Analysis of Spark Networks to cross-verify your projections. Please also try Stock Tickers module to use high-impact, comprehensive, and customizable stock tickers that can be easily integrated to any websites.
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