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Surprisingly little is known of the various methods of security analysis used by financial analysts with industry-specific knowledge. Financial analysts’ industry knowledge is a favored and appreciated attribute by fund managers and institutional investors. Understanding analysts’ use of industry-specific valuation models, which are the main value drivers within different industries, will enhance our understanding of important aspects of value creation in these industries. This paper contributes to the broader understanding of how financial analysts in various industries approach valuation, offering insights that can be beneficial to a wide range of stakeholders in the financial market.

This paper systematically reviews existing research to consolidate the current understanding of analysts’ use of valuation models and factors. It aims to demystify what can often be seen as a “black box”, shedding light on the valuation tools employed by financial analysts across diverse industries.

The use of industry-specific valuation models and factors by analysts is a subject of considerable interest to both academics and investors. The predominant model in several industries is P/E, with some exceptions. Notably, EV/EBITDA is favored in the telecom, energy and materials sectors, while the capital goods industry primarily relies on P/CF. In the REITs sector, P/AFFO is the most commonly employed model. In specific sectors like pharmaceuticals, energy and telecom, DCF is utilized. However, theoretical models like RIM and AEG find limited use among analysts.

This is the first paper systematically reviewing the research on analyst’s use of industry-specific stock valuation methods. It serves as a foundation for future research in this field and is likely to be of interest to academics, analysts, fund managers and investors.

This 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.

The 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.

The 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.

In 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.

The 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.

In the process of building the “Belt and Road” and “Bright Road” community of interests between China and Kazakhstan, this paper proposes the construction of an inland nuclear power plant in Kazakhstan. Considering the uncertainty of investment in nuclear power generation, the authors propose the MGT (Monte-Carlo and Gaussian Radial Basis with Tensor factorization) utility evaluation model to evaluate the risk of investment in nuclear power in Kazakhstan and provide a relevant reference for decision making on inland nuclear investment in Kazakhstan.

Based on real options portfolio combined with a weighted utility function, this study takes into account the uncertainties associated with nuclear power investments through a minimum variance Monte Carlo approach, proposes a noise-enhancing process combined with geometric Brownian motion in solving complex conditions, and incorporates a measure of investment flexibility and strategic value in the investment, and then uses a deep noise reduction encoder to learn the initial values for potential features of cost and investment effectiveness. A Gaussian radial basis function used to construct a weighted utility function for each uncertainty, generate a minimization of the objective function for the tensor decomposition, and then optimize the objective loss function for the tensor decomposition, find the corresponding weights, and perform noise reduction to generalize the nonlinear problem to evaluate the effectiveness of nuclear power investment. Finally, the two dimensions of cost and risk (estimation of investment value and measurement of investment risk) are applied and simulated through actual data in Kazakhstan.

The authors assess the core indicators of Kazakhstan's nuclear power plants throughout their construction and operating cycles, based on data relating to a cluster of nuclear power plants of 10 different technologies. The authors compared it with several popular methods for evaluating the benefits of nuclear power generation and conducted subsequent sensitivity analyses of key indicators. Experimental results on the dataset show that the MGT method outperforms the other four methods and that changes in nuclear investment returns are more sensitive to changes in costs while operating cash flows from nuclear power are certainly an effective way to drive investment reform in inland nuclear power generation in Kazakhstan at current levels of investment costs.

Future research could consider exploring other excellent methods to improve the accuracy of the investment prediction further using sparseness and noise interference. Also consider collecting some expert advice and providing more appropriate specific suggestions, which will facilitate the application in practice.

The Novel Coronavirus epidemic has plunged the global economy into a deep recession, the tension between China and the US has made the energy cooperation road unusually tortuous, Kazakhstan in Central Asia has natural geographical and resource advantages, so China–Kazakhstan energy cooperation as a new era of opportunity, providing a strong guarantee for China's political and economic stability. The basic idea of building large-scale nuclear power plants in Balkhash and Aktau is put forward, considering the development strategy of building Kazakhstan into a regional international energy base. This work will be a good inspiration for the investment of nuclear generation.

This study solves the problem of increasing noise by combining Monte Carlo simulation with geometric Brownian motion under complex conditions, adds the measure of investment flexibility and strategic value, constructs the utility function of noise reduction weight based on Gaussian radial basis function and extends the nonlinear problem to the evaluation of nuclear power investment benefit.

The Public–Private Partnership (PPP) modality plays an important role in the procurement of global infrastructure projects. Regarding PPP's complex transaction structure, pricing of a PPP project is critical to both parties where the government pursues a high value for money (VFM) and the investor strives to maximize its financial gains. Despite the straightforward win–win principle, a formidable compromise is often the case to end up with a fairly acceptable price, subject to many determinants such as the risk profile, expected return, technological innovation and capacities of both parties. Among them, this study chooses to examine the “managing flexibility” (MF) capacity of investors in pricing of a PPP project, in light of the widely recognized importance of a real-option perspective toward the long term, complex and uncertain PPP arrangement. This study addresses two major questions: (1) how is MF in PPP projects to be valued and (2) how are PPP projects to be priced when considering a project's MF value.

A binomial tree model is used to evaluate the MF value in PPP projects. Based on the developed MF pricing model, net present value (NPV) and adjusted VFM value are then calculated. Finally, a multi-objective decision-making method (MODM) was adopted to determine the optimal level of returns based on invested capital (ROIC), return on operation maintenance (ROOM) and concession period.

The applicability and functionality of the proposed model is investigated using a real project case. For a given return, extended NPV and adjusted VFM value were calculated and analyzed using sensitivity analysis. Factor influence is shown by the model to be dependent on factor impact on cash flow. Subsequently, a multi-objective decision-making (MODM) model was adopted to determine the optimal level of returns, where the solution approximates the real-world bidding price. Results confirm that the pricing model provides a reliable and practical PPP proposal pricing tool.

This study proposes an integrated framework for valuing MF in PPP projects and thus more accurately determine optimal pricing of PPP projects than revealed in extant research. The model offers a practical tool to aid in the valuation of PPP projects.

The purpose of this study is to examine the application of sustainable building design and operation within a university setting to determine its economic efficacy and potential for further university investment.

This study incorporated a life cycle cost analysis (LCCA), simple payback period and discounted payback period calculations to determine the return on investment, including a sensitivity analysis when comparing the energy use and financial benefits of the sustainable design of a multi-use facility at Toronto Metropolitan University with buildings of similar size and use-type.

It was found that there is a positive business argument for Canadian Universities to consider the use of sustainable design to reduce energy use and greenhouse gas (GHG) emissions. A reasonable payback period and net present value within an institutional context were determined using a life-cycle cost assessment approach.

This study was limited to the measure of only a single location. Certain assumptions regarding energy pricing and interest rates and the related sensitivities were anchored on a single year of time, and the results of this study may be subject to change should those prices or rates become significantly different over time. Considerations for future research include a longitudinal approach combined with a more detailed analysis of the effect of use-type on the variables discussed.

For university administrators, the results of this study may encourage institutions such as universities to approach new building projects through the lens of energy efficiency and environmental sustainability.

GHG emissions are a well-proven contributor to global climate change, and buildings remain a significant source of GHG emissions in Canada due to their winter heating and summer cooling loads. As a result, sustainable building design on university campuses can mitigate this impact by optimizing and reducing energy consumption.

Research related to the economic evaluation of sustainable building design on university campuses is generally limited, and this study represents the first of its kind in regard to an LCCA of a sustainably designed building on a Canadian University campus.

Organizational technologies can be classified according to the roles they play as either commodity or strategic. Commodity technologies support common operations, while strategic technologies address perceived threats to competitiveness, often identified by strategic foresight. These must go through an adoption process before playing an effective role in strategy execution. The adoption process includes known activities, ranging from sourcing (itself from in-house development to turn-key acquisition) to operational integration. This paper aims to reveal strategic technology adoption risks that arise during strategy execution.

A gradually developed causal loop diagram model, supported by general literature, introduces three general classes of technology adoption risks: mismatched requirements, supplier dependence and unmanaged life cycles.

Rather than managed, these risks are incurred or avoided depending on decisions made during the adoption process.

Despite the scarce literature coverage for the approach, examples revealing the presence of adoption risks are nevertheless available in the well-documented history of enterprise resource planning (ERP).

Although ERP is presented as a general-purpose strategic technology, the unique business features of maritime container terminals pose serious challenges to its adoption, which provides additional support to the discussion and reinforces the conclusions.

The approach to identifying risks in strategic technology adoption departs from the current risk paradigm in two significant ways. First, it emphasizes policy decision-making rather than external events. Second, it views risks as systemic rather than occurring independently.

Access to financing is a key success factor for start-ups. High failure rates, long payback periodse and asymmetries lead to conservative pricing and valuation discounts. The authors examine financial marketing and contingent factors, as enablers of a “patent premium” by private equity (PE) investors targeting start-ups in their growth and expansion stages.

Drawing from the contingency, innovation and signaling theories, the authors collect patent records for Italian start-ups in which a higher than 30% stake was acquired by PE investors during the period 2014–2020. The authors apply a generalized linear model with a logit link and robust clustered error to test the key relationships and control for endogeneity with a Heckman two-stage selection model.

Findings indicate start-ups’ access to financing is significantly impacted by marketing constructs adopted in the operation. Innovation alone does not suffice to determine a valuation premium, unless contingent on the promotion of its product, the placement -investors targeted-of the equity, brand equity levers of previous ownership and marketing competence backing the deal.

The authors provide new insights in the marketing-finance interface, highlighting levers that reassure investors and enable monetizing innovation in start-ups that are still privately held. The authors bridge a gap in literature that has mainly focused on venture capital and innovation financing in the open market, as well as a significant gap regarding the marketing design of private equity placements.