Twitter Stock Forecast - 8 Period Moving Average

<div class='circular--portrait' style='background:#689CFD;color: #ffffff;font-size:3em;padding-top: 38px;;'>TWT</div>
TWTR -- USA Stock  

 Earnings Call  In Two Weeks

Twitter Stock Forecast is based on your current time horizon. Investors can use this forecasting interface to forecast Twitter historical stock prices and determine the direction of Twitter's 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 Twitter historical fundamentals such as revenue growth or operating cash flow patterns. Although Twitter naive historical forecasting may sometimes provide an important future outlook for the firm we recommend to always cross-verify it against solid analysis of Twitter systematic risk associated with finding meaningful patterns of Twitter fundamentals over time. Additionally, take a look at Historical Fundamental Analysis of Twitter to cross-verify your projections.

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Twitter Accounts Payable Turnover is relatively stable at the moment as compared to the past year. Twitter reported last year Accounts Payable Turnover of 22.59. As of 07/03/2020, Accrued Expenses Turnover is likely to grow to 9.60, while Receivables Turnover is likely to drop 3.90. . As of 07/03/2020, Issuance Purchase of Equity Shares is likely to grow to about 29.5 M, while Weighted Average Shares is likely to drop slightly above 612.1 M.
An 8-period moving average forecast model for Twitter is based on an artificially constructed time series of Twitter daily prices in which the value for a trading day is replaced by the mean of that value and the values for 8 of preceding and succeeding time periods. This model is best suited for price series data that changes over time.

Twitter 8 Period Moving Average Price Forecast For the 4th of July

Given 30 days horizon, the forecasted value of Twitter on the next trading day is expected to be  30.50  with a mean absolute deviation of  1.34 , mean absolute percentage error of  2.98 , and the sum of the absolute errors of  71.08 
 30.50 

Twitter Stock Forecast Pattern

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Twitter Forecasted Value

Market Value
30.89
3rd of July 2020
30.50
Expected Value
34.22
Upside

Model Predictive Factors

AICAkaike Information Criteria104.4982
BiasArithmetic mean of the errors -0.2833
MADMean absolute deviation1.3412
MAPEMean absolute percentage error0.0426
SAESum of the absolute errors71.0825
The eieght-period moving average method has an advantage over other forecasting models in that it does smooth out peaks and valleys in a set of daily observations. Twitter 8-period moving average forecast can only be used reliably to predict one or two periods into the future.

Predictive Modules for Twitter

There are currently many different techniques concerning forecasting the market as a whole as well as predicting future values of individual securities such as Twitter. 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 (7)
LowProjected EPSHigh
-0.32-0.10.08
Details
Hype
Prediction
LowEstimated ValueHigh
27.1330.8534.57
Details
Intrinsic
Valuation
LowReal ValueHigh
27.9831.7035.42
Details
Bollinger
Band Projection (param)
LowerMiddle BandUpper
28.9132.9536.99
Details
24 Analysts
Consensus
LowTarget PriceHigh
24.0032.3847.00
Details

Other Forecasting Options for Twitter

View Currently Related Equities

Twitter Technical and Predictive Analytics

Volatility Measures

Twitter Risk Indicators

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Additionally, take a look at Historical Fundamental Analysis of Twitter to cross-verify your projections. Please also try Watchlist Optimization module to optimize watchlists to build efficient portfolio or rebalance existing positions based on mean-variance optimization algorithm.
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