Ford Motor Book Value Per Share Trend

F -- USA Stock  

USD 8.76  0.03  0.34%

This module enables investors to look at Ford Motor 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 Direct Expenses of 135.7 B or Consolidated Income of 6.4 B, but also many exotic indicators such as Interest Coverage of 7.3801 or Long Term Debt to Equity of 3.2322. This module is a perfect complement to use when analyzing Ford Motor Valuation or Volatility. It can also complement various Ford Motor Technical models. Additionally see analysis of Ford Motor Correlation with competitors.
Showing smoothed Book Value per Share of Ford Motor Company with missing and latest data points interpolated. Measures the ratio between Shareholders Equity and Weighted Average Shares.
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9.14 times

          10 Years Trend
Increasing
Very volatile
 Book Value per Share 
      Timeline 

Ford Motor Regression Statistics

Arithmetic Mean 5.77
Coefficient Of Variation 137.78
Mean Deviation 5.07
Median 6.62
Standard Deviation 7.94
Sample Variance 63.12
Range 32.64
R Value 0.11
Mean Square Error 68.55
R Squared 0.01258
Significance 0.73
Slope 0.25
Total Sum of Squares 694.27

Ford Motor Book Value Per Share Over Time

2016-12-31  7.34 
2017-12-31  8.96 
2018-12-31  8.58 

Other Fundumenentals of Ford Motor

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Ford Motor Fundamental Drivers

Ford Motor Company Fundamental Market Drivers

Short Percent Of Float3.02%
Forward Price Earnings6.35
Shares Short Prior Month109.68M
Average Daily Volume Last 10 Day48.37M
Average Daily Volume In Three Month42.43M
Date Short InterestSeptember 14, 2018
Fifty Day Average9.43
Two Hundred Day Average10.63

Ford Motor Upcoming Events

Ford Motor Upcoming Company Events

Upcoming Quarterly ReportJuly 25, 2018
Next Earnings ReportOctober 25, 2018
Additionally see analysis of Ford Motor Correlation with competitors. Please also try Portfolio Rebalancing module to analyze risk-adjusted returns against different time horizons to find asset-allocation targets.
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