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This paper addresses the differences between the Modigliani-Miller [M&M] model (1958, 1963) and the Miles-Ezzell [M&E] model (1980, 1985). The main difference between these two models concerns the stochasticity of the free cash flows. While M&M assumes a strictly stationary process, M&E's process is a martingale. However, this subtle difference has not been fully exposed, and previous literature has produced partly erroneous statements or inconsistent valuation models. Therefore, the main objective of this paper is to illustrate and accentuate the effect of these two mutually exclusive stochastic processes on the timely behavior of cash flows, discount rates, and values of the firm, equity, debt, and tax shield. For this purpose, we perform a numerical experiment that allows the determination of values and discount rates by means of the risk-neutral approach. We show that in the M&E model, all cash flows and values are path-dependent, while they are not in M&M's world. Furthermore, in M&E's model, all discount rates are time-invariant, except for the discount rate applied to tax shields, which depends on the lifetime of the cash flows. Contrarily, in the M&M setup, all discount rates change across time, except for the constant discount rate of the tax shield. This has consequences for the applicability of the well-known presentvalue formula for annuities and for building consistent valuation models for both finite and perpetual cash flows.

Abstract-The dependability of ICT systems is vital for today's society. However, operational systems are not fault free. Providers and customers have to define clear availability requirements and penalties on the delivered services by using SLAs. Fulfilling the stipulated availability may be expensive. The lack of mechanisms that allow a fine control of the SLA risk may lead to overdimension the provided resources. Therefore, a relevant question for ICT service providers is: How to guarantee the SLA availability in a cost efficient way? This paper studies how to combine different fault tolerant techniques with different costs and properties, in order to economically fulfill a given SLA requirement. GEARSHIFT is a mechanism that enables ICT providers to set the fault tolerance technique (gear ratio) needed, depending on the current service conditions and requirements. We illustrate how to use the proposed model in a backbone network scenario, using measurements from a production national network. Finally, we show that the total costs of delivering an ICT service follow a simple convex function, which allows an easy selection of the optimal risk by tuning properly the combination of fault tolerant techniques.

PurposeThe purpose of this paper is to establish the flow-to-equity method, the free cash flow (FCF) method, the adjusted present value method and the relationships between these methods when the FCF appears as an annuity. More specifically, we depart from the two most widely used evaluation settings. The first setting is that of Modigliani and Miller who based their analysis on a stationary FCF. The second setting is that of Miles and Ezzell who worked with an FCF that represents an autoregressive possess of first order.Design/methodology/approachInspired by recent observations in the literature concerning cash flows, discount rates and values in discounted cash flow (DCF) methods, we mathematically derive DCF valuation formulas for annuities.FindingsThe following relationships are established: (a) the correct discount rate of the tax shield when the free cash flow takes the form of a first-order autoregressive annuity, (b) the direct valuation of the tax shield from the free cash flow for a first-order autoregressive annuity, (c) the correct translation from the required return on unlevered equity to the levered equity, when the free cash flow is a stationary annuity and (d) direct calculation of the unlevered and levered firm values and the value of the tax shield for a stationary annuity.Originality/valueUntil now the complete set of formulas for the valuation of stochastic annuities by different DCF methods has not been established in the literature. These formulas are developed here. These formulas are important for practitioners and academics when it comes to the valuation of cash flows of finite lifetime.

PurposeThe primary purpose of this paper is to develop the translation formula between the required return on unlevered and levered equity for the specific case where cash flows have a finite lifetime and the flow to debt is prespecified. The secondary purpose of this paper is to underpin the importance of the type of stochasticity of cash flows for translation formulas. A general derivation of such formulas and the discount rate in the free cash flow approach is shown.Design/methodology/approachThe paper starts with the same assumptions that have been applied by Modigliani and Miller (1963), Miles and Ezzell (1980) and other researchers. Then the paper develops the mathematical foundations to apply a deterministic backward-iterative scheme for valuing cash flows. After stating the valuation formulas for levered and unlevered equity, debt and tax shields, the authors mathematically derive the relationship between the unlevered return and levered return on equity.FindingsConventional translation formulas apply to very special cases. They can generally not be used for projects with nonconstant leverage and a finite lifetime. In general, translation formulas depend on continuing values, cash flows, leverage, taxation, risk-free rate, etc. In this paper, the translation depends on the structure of the debt in addition to the well-known parameters in conventional formulas. This paper formula contains the Modigliani-Miller translation formula as a special case.Originality/valueThe authors develop a novel formula for the translation of the required return on unlevered to levered equity. With this formula, the authors offer a solution for the consistent valuation of cash flows with a limited lifetime and given debt financing.

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Transaction level models of systems-on-chip in SystemC are commonly used in the industry to provide an early simulation environment. The SystemC standard imposes coroutine semantics for the scheduling of simulated processes, to ensure determinism and reproducibility of simulations. However, because of this, sequential implementations have, for a long time, been the only option available, and still now the reference implementation is sequential. With the increasing size and complexity of models, and the multiplication of computation cores on recent machines, the parallelization of SystemC simulations is a major research concern. There have been several proposals for SystemC parallelization, but most of them are limited to cycle-accurate models. In this paper we focus on loosely timed models, which are commonly used in the industry. We present an industrial context and show that, unfortunately, most of the existing approaches for SystemC parallelization can fundamentally not apply in this context. We support this claim with a set of measurements performed on a platform used in production at STMicroelectronics. This paper surveys existing techniques, presents a visualization and profiling tool and identifies unsolved challenges in the parallelization of SystemC models at transaction level.

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