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This paper aims to examine the valuation precision of composite models in each of six key industries in South Africa. The objective is to ascertain whether equity-based composite multiples models produce more accurate equity valuations than optimal equity-based, single-factor multiples models.

This study applied principal component regression and various mathematical optimisation methods to test the valuation precision of equity-based composite multiples models vis-à-vis equity-based, single-factor multiples models.

The findings confirmed that equity-based composite multiples models consistently produced valuations that were substantially more accurate than those of single-factor multiples models for the period between 2001 and 2010. The research results indicated that composite models produced up to 67 per cent more accurate valuations than single-factor multiples models for the period between 2001 and 2010, which represents a substantial gain in valuation precision.

The evidence, therefore, suggests that equity-based composite modelling may offer substantial gains in valuation precision over single-factor multiples modelling.

In light of the fact that analysts’ reports typically contain various different multiples, it seems prudent to consider the inclusion of composite models as a more accurate alternative.

This study adds to the existing body of knowledge on the multiples-based approach to equity valuations by presenting composite modelling as a more accurate alternative to the conventional single-factor, multiples-based modelling approach.

From the buying club’s perspective, the transfer of a player can be interpreted as an investment from which the club expects uncertain future benefits. This paper aims to develop a decision-oriented approach for the valuation of football players that could theoretically help clubs determine the subjective value of investing in a player to assess its potential economic advantage.

We build on a semi-investment-theoretical risk-value model and elaborate an approach that can be applied in imperfect markets under uncertainty. Furthermore, we illustrate the valuation process with a numerical example based on fictitious data. Due to this explicitly intended decision support, our approach differs fundamentally from a large part of the literature, which is empirically based and attempts to explain observable figures through various influencing factors.

We propose a semi-investment-theoretical valuation approach that is based on a two-step model, namely, a first valuation at the club level and a final calculation to determine the decision value for an individual player. In contrast to the previous literature, we do not rely on an econometric framework that attempts to explain observable past variables but rather present a general, forward-looking decision model that can support managers in their investment decisions.

This approach is the first to show managers how to make an economically rational investment decision by determining the maximum payable price. Nevertheless, there is no normative requirement for the decision-maker. The club will obviously have to supplement the calculus with nonfinancial objectives. Overall, our paper can constitute a first step toward decision-oriented player valuation and for theoretical comparison with practical investment decisions in football clubs, which obviously take into account other specific sports team decisions.

Increased economic activity in sub-Saharan Africa (SSA) has given rise to increased demand for port development. Given the often scarce availability of national public funding, port institutional reform programmes have been implemented to pave the way for the inclusion of external port investors. Notwithstanding this fact, some sub-Saharan African Governments remain institutionally locked into the notion that state-owned enterprises remain an appropriate vehicle for port terminal operations. This, despite the fact that terminal operational concessions globally and within the continent of Africa are increasingly being managed by global terminal operators. Given this context, this study aims to evaluate different port valuation and funding strategies. Two research questions form the core of this research: what is the financial value of a concession? What is the most cost advantageous funding strategy? The methodology is applied to the development of a two-berth container terminal in SSA.

After reviewing a range of financial valuation and funding techniques, the study presents valuation and funding model applicability-fit tests. Thereafter, a suitable valuation technique is selected and applied to the case study providing a concession valuation. Different funding strategies are applied to the valuation model to determine the cost implications of each funding instrument given the local context and institutional constraints applicable to SSA. Finally, the study discusses the significance of the results to potential SSA port investors by highlighting the impact of each funding approach on key financial metrics.

The study presents a range of financial investment appraisal results for the case study concession in consideration of four specific funding strategies. The highest concession valuation could be attributed to a higher debt ratio as a principal funding strategy. In addition, this funding approach (100% debt) realised the shortest payback period and the highest internal rate of return values. The authors, however, maintain that the optimal funding strategy for a concession depends ultimately on the financial goals of the investor.

This research makes a contribution to the existing literature on port finance and development by presenting a structured approach to the evaluation of the valuation and funding techniques, which can be used in terminal development subject to the specific local context and institutional constraints (in this case applicable to SSA). The study provides practical insight into the potential cost of the considered terminal concession for private or public sector participants and a view of the most cost advantageous funding strategy available for interested investors.

This paper aims to investigate the relevance of two groups of valuations models as follows: the accounting models based on the residual income (RIM) and the standard market model, on equity price, return and volatility relevance.

The models are tested on companies traded on Palestine exchange from 2009 to 2018, using panel regression analysis. Two-price and two-return models derived from RIM to compare with the market model and four volatility models.

The standard RIM outperformed other models in equity price modeling. The dividend discount model (DDM) outperformed the rest of the models in terms of return estimation. However, the authors find that the market model can explain equity variance better than RIM and DDM models.

For investors, market beta does not necessarily capture all relevant factors of value and traditional financial statements are still important in providing relevant information and different models are used for different values perspectives (price, return and volatility).

Previous studies focus on comparing the price and return relevance of accounting-based models (RIM and cash flow models). Three aspects differentiate this paper and contribute to its originality, namely, the uniqueness of the context, incorporating the market model into the picture along with the accounting-based models and adding Volatility dimensions of relevance.

Although Public Research Institutions (PRIs) are large technology producers, they lack automated information tools that follow technical and scientific criteria for assessing and valuing patents. The assessment and valuation processes are stages of technology transfer (TT) that make it possible to obtain productive arrangements and guide the efforts of those involved in the development, maintenance and negotiation. This study aims to analyze the hybrid model of assessment and valuation of technologies by Soares (2018), applying the ‘Valorativo' software. In addition to patent value and indicator scores, the methods allow an understanding of the technology portfolio and its management.

This research is quali-quantitative, following an approach of applied nature and descriptive objectives. The research has bibliographical, documental and case study features based on the software development methodologies described in the study and the theoretical framework.

The Valorativo software assisted in the analysis of ten patents on PRIs. With the data collection and patent analysis, PAT1 scored highest among engineering patents, PAT3 scored highest among pharmaceutical patents and PAT10 scored highest among biotechnology patents. Five of the assessed patents resulted in a surplus of net present value (NPV), final net present value (NPVF) and royalties; revenue expectations outpaced investments.

The authors based the developed software on Soares’s (2018) methodology, with additional calculations and graphs. The Web software and the spreadsheet with Visual Basic for Application (VBA) were developed to deal with the patents assessment and valuation, helping in the analysis of their Legal Value, Technological Value and Market Conditions in the assessment process, and the Discounted Cash Flow and NPV in the valuation process.

The software helps with patent analysis and can generate indicators for traders, technology holders and researchers. Thus, it was necessary to understand and develop a theoretical-applied framework to outline and replicate the methodology clearly and easily.

In the literature there are numerous tests that compare the accuracy of automated valuation models (AVMs). These models first train themselves with price data and property characteristics, then they are tested by measuring their ability to predict prices. Most of them compare the effectiveness of traditional econometric models against the use of machine learning algorithms. Although the latter seem to offer better performance, there is not yet a complete survey of the literature to confirm the hypothesis.

All tests comparing regression analysis and AVMs machine learning on the same data set have been identified. The scores obtained in terms of accuracy were then compared with each other.

Machine learning models are more accurate than traditional regression analysis in their ability to predict value. Nevertheless, many authors point out as their limit their black box nature and their poor inferential abilities.

AVMs machine learning offers a huge advantage for all real estate operators who know and can use them. Their use in public policy or litigation can be critical.

According to the author, this is the first systematic review that collects all the articles produced on the subject done comparing the results obtained.

State ownership has been common especially in industries with restricted competition. In Russia, state-controlled firms represent around 41 percent of the market value of all listed firms (Deloitte, 2015). Yet, there is a significant gap in the literature regarding the effects of various forms of government control in listed firms. The purpose of this paper is to fill this gap by exploring the impact of the complexity of state ownership and competition on the performance of Russian listed firms.

The sample consists of data for 72 firms (360 firm-years) in the Russian MOEX broad market index during 2011–2015. The complexity of state ownership is captured by studying forms of state control including majority/minority, direct/indirect, federal/regional, mixed structures and golden shares.

The authors find significant differences in performance relating to different forms of state ownership. State control is negatively related to firm valuation and the sales/employees ratio. Performance is weakest when state ownership takes the form minority, regional or direct ownership. State control through golden shares typically outperforms other state-controlled firms. The authors find indications of employment prioritization beyond the economical optimum. In addition, the relation between state ownership and profitability becomes positive in sectors where state firms appear to enjoy lower competition.

While the effects of state ownership have been studied on many markets, there is a lack of studies on the effects of different forms, or the complexity, of state ownership beyond direct and indirect ownership. The authors contribute to the literature on the performance effects of state ownership by studying a multitude of forms of governmental ownership as well as the role of competition in Russia. Especially the profitability of state-controlled firms is significantly affected by industry characteristics. Implications of the results are discussed both from firm and policy maker perspectives.

This paper shows a new methodology for evaluating the value and sensitivity of autocall knock-in type equity-linked securities. While the existing evaluation methods, Monte Carlo simulation and finite difference method, have limitations in underestimating the knock-in effect, which is one of the important characteristics of this type, this paper presents a precise joint probability formula for multiple autocall chances and knock-in events. Based on this, the calculation results obtained by utilizing numerical and Monte Carlo integration are presented and compared with those of existing models. The results of the proposed model show notable improvements in terms of accuracy and calculation time.

The world over is becoming urbanized, and people are migrating to cities in large numbers in search of opportunities. The increased urbanization has posed challenges such as congestion, rising crime, and growing urban poverty. The governments respond by providing amenities such as schools, hospitals, and housing to meet to increase in demand for these facilities. However, there is a need for the provision of facilities that meets the expectations of the people, particularly on the proximity of amenities and bundles of utility-bearing housing characteristics. In an attempt to address the challenge mentioned, the study estimated the hedonic characteristics influencing the willingness to accept and willingness to pay for housing facilities in the Eastern Cape Province, South Africa. Using a multiple linear regression model and artificial neural network, the study found out that properties with a bathroom, garage and large floor size have a higher value compared to properties without these facilities.When making decisions to acquire a property, buyers consider the availability of discounts and the prevailing property price. Overall, willingness to pay and accept decisions are mainly determined by location and the price at which homogeneous neighborhood properties were sold. Therefore, the study recommends that urban town planners and other housing authorities prioritize the construction of properties with larger floor areas, parking bays, and bathrooms using a cost-effective mechanism that makes the properties affordable to residents.