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Aged and insufficient domestic power plants and insecure, unreliable and expensive power imports pose significant challenges for the power sector of Afghanistan. On the other hand, due to the absence of a suitable transmission grid, the internal renewable energy resources are not adequately developed, despite their abundant resource potential throughout the country. This paper proposes a voltage source converter (VSC)-based high-voltage direct current (HVDC) transmission link to connect the Herat province in the west, which has huge solar and wind energy potential, to Kabul, the capital of the country and the main load center. A techno-economic analysis of this HVDC against high-voltage alternating current (HVAC) technology was performed to determine the suitability and effectiveness of the proposed transmission system. The active, reactive and corona losses were calculated as the technical parameters and the discounted cash flow (DCF) method was deployed to economically compare both technologies. The outcomes of the paper disclose that the implementation of this transmission project is techno-economically feasible, and can result in the energy security and economic stability of the country.

Using textual analysis for a large sample of analyst reports on U.S. firms, we find that analysts are more likely to use a discounted cash flow (DCF) model and to discuss more cash flow and discount rate information for firms with more uncertainty, as measured by earnings quality and firm risks. The market reactions to target price changes based on a DCF model are stronger, particularly for firms with greater valuation uncertainty and when the analysts present more cash flow and discount rate discussions. These results indicate that the analyst valuation process reflects investors’ information demand under uncertainty and has a bearing on the informativeness of analyst research.

Technology finance, which has attracted worldwide attention for the successful business development of small-and-medium enterprises (SMEs) or start-ups, has advanced an innovation or stagnation way-out resolution strategy for companies in line with the low-growth economic trends. Although the development of new technologies and the establishment of active R&D and commercialization strategies are essential factors in a company’s management sustainability, the activation of the technology market in practice is still in progress for its golden age. In this study, to promote a technology transfer-based company’s growth and to run technology-based various financial support activities, we develop and propose a new intelligent, deep learning-based technology valuation system that enables technology holders to estimate the economic values of their innovative technologies and further to establish a firm’s commercialization strategy. For the last years, the KIBO Patent Appraisal System (KPAS-II) herein proposed has been advanced by KIBO as a web-based, artificial intelligence (AI) and evaluation data applications valuation system that automatically calculates and estimates a technology’s feasible economic value by utilizing both the intrinsic and extrinsic index information of a patent and the commercialization entity’s business capabilities, and by applying to the discounted cash flow (DCF) method in valuation theory, and finally integrating with deep learning results based on the in-advance previously established patent DB and the financial DB. The KPAS-II proposed in this study can be said to have dramatically overcome the long-term preparation period and high levels of R&D and commercialization costs in terms of the limitations that the existing technology valuation method possesses by enhancing the reliability of approximate economic values from the deep learning results based on financial data and completed valuation data. In addition, it is expected that technology marketing coordinators, researchers, and non-specialty business agents, not limited to valuation experts, can easily estimate the economic values of their patents or technologies, and they can be actively utilized in a technology-based company’s decision-making and technologically dependent financial activities.

PurposeThis research should help determine whether development should focus on individual firms or will raising the national development level act like a rising tide and raise the performance of all corporations.Design/methodology/approachThe comparative data used in this study come from 150 Australian (ASX200 index listed) firms and 150 Sri Lankan (Colombo Stock Exchange listed) firms. The research questions are answered via a quantitative research design that uses primary and secondary data.FindingsThe findings demonstrate that capital budgeting practices are more influenced by contingency features and sophistication in Australia and Sri Lanka. Also, Australian firms tend to use capital budget models with good-to-strong predictive power (except for ROE) and Sri Lankan firms tend to use capital-budget models with fair-to-poor predictive power. Further, the analysis of Australian firms yielded much stronger and more statistically significant results than the analysis of Sri Lankan firms.Practical implicationsIn complex real-world situations, reconciling the outputs of a multifaceted approach to capital budgeting methods is more likely to give the depth and width of input needed to achieve an optimal capital investment plan.Originality/valueThe results of this study can provide rich information for stakeholders about new findings in capital budgeting (CB) practices and their contributions to firm performance in a comparative perspective.

This study aims to assess the potential risks of setting up a hydrogen infrastructure in the Netherlands. An integrated risk assessment framework, capable of analyzing projects, identifying risks and comparing projects, is used to identify and analyze the main risks in the upcoming Dutch hydrogen infrastructure project. A time multiplier is added to the framework to develop parameters. The impact of the different risk categories provided by the integrated framework is calculated using the discounted cash flow (DCF) model. Despite resource risks having the highest impact, scope risks are shown to be the most prominent in the hydrogen infrastructure project. To present the DCF model results, a risk assessment matrix is constructed. Compared to the conventional Risk Assessment Matrix (RAM) used to present project risks, this matrix presents additional information in terms of the internal rate of return and risk specifics.

The paper provides the methodological foundations of a property valuation method based on income approach called cyclical capitalization. The model is proposed for the valuation of income producing properties and was originally introduced by d’Amato (2001) and relies on the assumption of more than a single capitalization rate it integrates direct capitalization with the analysis of an entire market cycle. Such integration has been required in literature (Kazdin,1944; Pyhrr et al.,1990). The procedure has recently and indirectly been recalled by international valuation standards (IVS 2017; IVS 2020; IVS 2022; IVS 105 item 50.21 letter (e)), as it will be discussed in the paper. International Valuation Standards since 2017 required a different approach to the determination of terminal value (going out value, scrap value, exit value, terminal value, exit value) in Discounted Cash Flow Analysis. The models proposed in the paper are the basic ones systematized in the first contributions that took up and reorganized this line of research from its original systematic introduction (d’Amato, 2013; d’Amato, 2015; d’Amato, 2017a; d’Amato, 2017b). The initial applications were based on the office market in London. The first application to the Italian market will be made in this contribution referring to the office real estate market in Milan offered by the then REAG Study Office (currently Kroll). Evidence from the application shows that the proposed model is capable of reaching a prudent value judgments than the income-capitalization estimate normally used. Prudent opinion of value can help in determining the security value and the most likely market value under specific market conditions.

The increased smartness of the built environment is expected to contribute positively to climate change mitigation through energy conservation, efficient renewable energy utilization, and greenhouse gas emission reduction. Accordingly, significant investments are required in smart technologies, which enable the distributed supply of renewables and increased demand-side energy flexibility. The present study set out to understand the cash flows and economic viability of a real-life smart system investment in a building. The data collection process was threefold: First, a case building’s level of (energy) smartness was estimated. Second, the semi-structured interviews were held to understand the building owner’s motives for a smart investment. Third, the investment’s profitability was analyzed. The study found that the progressive smartness investment was technically feasible, and surprisingly also economically profitable. The original EUR 6 million investment provided over 10% return-on-investment and, thus, increased the property value by more than EUR 10 million. Moreover, the commercial partners also emphasized the strategic value gained by renewable energy and environmental performance. The high level of smartness with a good return on investment was accomplished mainly through new income generated from the reserve power markets. However, the results implied that financial profitability alone was not enough to justify the economic viability of a smart building system investment.

The inclusion of photovoltaic energy in the Colombian energy matrix has had several difficulties due to the lack of energy policies and regulations in renewable energy projects. The lack of government support with subsidies that extend the coverage of PV energy projects in residential areas has made the collection of funds more challenging. This paper presents a techno-economic analysis for the implementation of grid-connected photovoltaic projects on the roofs of residential areas, under the net metering policy framework. For the profitability analysis, the discounted cash flow (DCF) method was used. The revenues were obtained from the forecasts of the electrical power production of the PV system, based on the characteristics of the Colombian Caribbean Region. For this purpose, the meteorological data (2013-2017) of this region were used as an input for the calculation of the economic benefits that can be achieved with the implementation of PV systems. Based on the technical sizing and economic assumptions, it was proved that the DCF method allows to accurately determine the optimal debt ratio. After evaluating the three scenarios proposed, it was demonstrated that profitability and self-sustainability, with investment from creditors, is obtained from the implementation of PV systems of at least 3 kWp.

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.

The degree of uncertainty associated with the value of a company plays a relevant role in valuation analysis. We propose an original and robust methodology for company market valuation, which replaces the traditional point estimate of the conventional Discounted Cash Flow model with a probability distribution of fair values that convey information about both the expected value of the company and its intrinsic uncertainty. Our methodology depends on two main ingredients: an econometric model for company revenues and a set of firm-specific balance sheet relations that are estimated using historical data. We explore the effectiveness and scope of our methodology through a series of statistical exercises on publicly traded U.S. companies. At the firm level, we show that the fair value distribution derived with our methodology constitutes a reliable predictor of the company’s future abnormal returns. At the market level, we show that a long-short valuation (LSV) factor, built using buy-sell recommendations based on the fair value distribution, contains information not accessible through the traditional market factors. The LSV factor significantly increases the explanatory and the predictive power of factor models estimated on portfolios and individual stock returns.