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Special memorandum account (SMA) [1] is a margin credit account used for calculating US Regulation T requirements on brokerage accounts. In addition to Initial Margin and Maintenance Margin requirements, the SMA ledger is used to lock in unrealized gains that augment the client's buying power. According to Regulation T, Section 220.5: [2]
The VDM formula is derived from the net present value formula and can be used to calculate the value of maintenance. The VDM formula is: PV maintenance = Σ {F SHE,t x (CF AU,t + CF CC,t + CF RA,t + CF SHE,t) / (1+r) t} where: PV maintenance = present value potential of maintenance F SHE,t = SHE factor in year t
Randomly varying channel gains such as fading are taken into account by adding some margin depending on the anticipated severity of its effects. The amount of margin required can be reduced by the use of mitigating techniques such as antenna diversity or multiple-input and multiple-output (MIMO). A simple link budget equation looks like this:
Therefore, this input to the calculation of free cash flow may be subject to manipulation, or require estimation. Since it may be a large number, maintenance capex's uncertainty is the basis for some people's dismissal of 'free cash flow'. A second problem with the maintenance capex measurement is its intrinsic 'lumpiness'.
The function f(x) (in blue) is approximated by a linear function (in red). In calculus, the trapezoidal rule (also known as the trapezoid rule or trapezium rule) [a] is a technique for numerical integration, i.e., approximating the definite integral: ().
Ultimately, the $54 markup price is the shop's margin of profit. Cost-plus pricing is common and there are many examples where the margin is transparent to buyers. [ 4 ] Costco reportedly created rules to limit product markups to 15% with an average markup of 11% across all products sold. [ 5 ]
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The plot shows that the Hinge loss penalizes predictions y < 1, corresponding to the notion of a margin in a support vector machine. In machine learning, the hinge loss is a loss function used for training classifiers. The hinge loss is used for "maximum-margin" classification, most notably for support vector machines (SVMs). [1]