Visa Trade and Non Trade Payables Trend

V -- USA Stock  

USD 137.11  0.77  0.56%

This module enables investors to look at Visa various fundamental indicators over time in order to gain insight into the company future performance. Macroaxis historical fundamental analysis tools allow evaluation of not only typical financial statement drivers such as Consolidated Income of 7 B or Cost of Revenue of 2.6 B, but also many exotic indicators such as Interest Coverage of 21.7192 or Long Term Debt to Equity of 0.5677. This module is a perfect complement to use when analyzing Visa Valuation or Volatility. It can also complement various Visa Technical models. Also please take a look at analysis of Visa Correlation with competitors.
Showing smoothed Trade and Non Trade Payables of Visa with missing and latest data points interpolated. A component of Total Liabilities representing trade and non-trade payables.
Trade and Non Trade Payables10 Years Trend
Increasing
Slightly volatile
 Trade and Non Trade Payables 
      Timeline 

Visa Regression Statistics

Arithmetic Mean 1,364,702,520
Geometric Mean 1,093,389,555
Coefficient Of Variation 61.23
Mean Deviation 708,562,186
Median 1,409,000,000
Standard Deviation 835,545,535
Range 2,482,746,235
R Value 0.93
R Squared 0.86
Significance 0.00001337
Slope 215,084,645

Visa Trade and Non Trade Payables Over Time

2014  907,000,000 
2015  2,287,000,000 
2018  2,690,588,235 

Other Fundumenentals of Visa

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Visa Fundamental Drivers

Visa Fundamental Market Drivers

Short Percent Of Float1.49%
Forward Price Earnings22.75
Shares Short Prior Month28.39M
Average Daily Volume Last 10 Day9.21M
Average Daily Volume In Three Month9.63M
Date Short InterestOctober 31, 2018
Fifty Day Average139.24
Two Hundred Day Average139.61

Visa Upcoming Events

Visa Upcoming Company Events

Upcoming Quarterly ReportOctober 23, 2017
Next Earnings ReportFebruary 1, 2018
Also please take a look at analysis of Visa Correlation with competitors. 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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