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c = cost of capital, or the weighted average cost of capital (WACC). NOPAT is profits derived from a company's operations after cash taxes but before financing costs and non-cash bookkeeping entries. It is the total pool of profits available to provide a cash return to those who provide capital to the firm.
An exponential moving average (EMA), also known as an exponentially weighted moving average (EWMA), [5] is a first-order infinite impulse response filter that applies weighting factors which decrease exponentially. The weighting for each older datum decreases exponentially, never reaching zero. This formulation is according to Hunter (1986). [6]
The rate of return on a portfolio can be calculated indirectly as the weighted average rate of return on the various assets within the portfolio. [3] The weights are proportional to the value of the assets within the portfolio, to take into account what portion of the portfolio each individual return represents in calculating the contribution of that asset to the return on the portfolio.
Exponential smoothing or exponential moving average (EMA) is a rule of thumb technique for smoothing time series data using the exponential window function. Whereas in the simple moving average the past observations are weighted equally, exponential functions are used to assign exponentially decreasing weights over time. It is an easily learned ...
The weighted average return on assets, or WARA, is the collective rates of return on the various types of tangible and intangible assets of a company.. The presumption of a WARA is that each class of a company's asset base (such as manufacturing equipment, contracts, software, brand names, etc.) carries its own rate of return, each unique to the asset's underlying operational risk as well as ...
The weighted average cost of capital (WACC) is the rate that a company is expected to pay on average to all its security holders to finance its assets. The WACC is commonly referred to as the firm's cost of capital. Importantly, it is dictated by the external market and not by management.
Kernel average smoother example. The idea of the kernel average smoother is the following. For each data point X 0, choose a constant distance size λ (kernel radius, or window width for p = 1 dimension), and compute a weighted average for all data points that are closer than to X 0 (the closer to X 0 points get higher weights).
We can see that the slope (tangent of angle) of the regression line is the weighted average of (¯) (¯) that is the slope (tangent of angle) of the line that connects the i-th point to the average of all points, weighted by (¯) because the further the point is the more "important" it is, since small errors in its position will affect the ...