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Focusing on managerial problems related to the measurement of intangibles, this paper develops and validates a hedonic-pricing methodology for the evaluation of the intangible resources of companies obtaining their shadow prices.

The paper adapts a hedonic-pricing methodology developed primarily for markets in real estate and secondhand cars to define how much intangibles may contribute to companies' market value. A certain calibration of the original tool has been developed to make this methodology appropriate for interpretation and practical use. The main advantage of this approach is that it allows for an evaluation of the shadow prices of intangible resources. These prices can be interpreted as the market value of the intangible resources which are not reflected on the balance sheet.

The results of this study demonstrate that hedonic pricing with a self-selection correction generates robust estimates. As one can see, the positive contribution of a high endowment of intangibles for all shadow prices is confirmed through estimations using two different techniques. Meanwhile, the negative effect of a low endowment is even more evident for the baseline model. This model shows consistent negative shadow prices for the majority of underinvested intangibles. Brands have the highest shadow prices in the introduced models; human capital, as measured by the qualification of top management and investments in employees, has likewise demonstrated high prices. However, most structural resources seem to be not reflected to a large degree in companies' market value.

This paper brings new opportunities to obtain the monetary value of intangible resources based on estimated market prices of a corporation's resource portfolio. These prices may be used for several purposes – for example, benchmarking for performance management, capital budgeting or knowledge-management practices. Moreover, by having methodological value, this study opens ways to evaluate any other intangibles which are not explicitly discussed in the empirical test of this particular study.

This study primarily contributes to the methodological advancement of evaluation of corporate intangible resources. It departs from the conventional hedonic-pricing mechanism to identify cogent estimates to intangibles in monetary terms. Importantly, this mechanism implies individual shadow prices for specific intangible resources which makes the contribution of this study unique for the existing literature, both within resource-based and value-based views.

The research aims to examine the impact of economic policy uncertainty (EPU) on a company's behaviour concerning its human capital. Additionally, the difference in effect for companies with specific human capital is analysed.

The hypotheses are tested on a multi-industry sample of large public companies from five European countries, using panel data modelling. The index of Baker et al. (2016) is used to measure EPU.

In the case of increasing EPU on one standard deviation, companies tend to reduce their human capital by approximately 1.7%. Moreover, despite theoretical assumptions, the effect on companies with more specific human capital is twice stronger. The heterogeneity of effect across countries and industries is also present.

Regulators and governments should consciously introduce changes in relation to regulations and decrease the uncertainty of economic policy to stimulate corporate investments in human capital.

This is the first study that considers the mechanism of EPU and its influence on corporate human capital. The results suggest that concerns regarding economic policy cause companies to reduce human capital.

This paper aims to explore whether individual intellectual capital of a fund manager allows mutual fund to outperform market.

The sample includes 85 Russian equity funds for the period of 2013. First, Jensen’s alpha for each fund has been calculated, and then cross-sectional regression analysis has been used. While only a part of fund managers publish biographic sketches, the authors use the Heckman procedure to control for self-selection issues.

The results support the idea that the individual characteristics indicate the possibility to earn abnormal alpha. Managers with economic education and with Moscow education perform better than others. Relationship between both fund performance measures and manager’s experience has inverted U-shape. Jensen’s alpha reaches its highest level at the point of 9 years, whereas beta – at 10 years of manager’s experience.

Investigation can be improved by including more variables that influence the disclosure of managers’ personal information, for example, by conducting surveys. Additionally, cross-sectional data restrict the analysis.

The discovered characteristics of managers’ intellectual capital can be used as additional screening tool for the investor who is deciding on mutual fund choice in Russia. While individual intellectual capital is observable and more persistent in time in comparison with the past fund performance, such tool allows better decision-making.

This is the first paper that explores which characteristics of Russian fund managers are connected with higher abnormal return (measured by Jensen’s alpha) and risk (beta) of mutual funds.

The purpose of this paper is to present a framework that helps to analyze the dependence between personal welfare and individual (personal) intellectual capital (IIC). The authors also introduce the system of proxy indicator for personal intellectual capital (IC) of football coaches.

This paper employs the idea that personal welfare depends on personal IC, particularly, talent. That is why initially the literature analysis of welfare phenomenon was provided. Then the system of available proxy indicators of football coaches’ IC was designed. To achieve the purpose a linear function is estimated with the help of ordinary least squares method.

The chosen set of IC proxy indicators explain the significant part of coaches’ salary. Such proxies as improvement in the championship table and coach's image in media have a significant and positive influence on coach's salary. Whereas, lowering the position of the club does not considerably affect the coach's wealth. A clubs’ financial capacities and budget also influences coaches’ salaries.

Traditional limitation of proxy indicators-based studies is connected with their eligibility and complexity.

The possibility to codify IC of a person enables to analyze core competencies necessary in a particular activity or profession for success achievement. Moreover a policy of inequality reduction should take into account that intangible assets are at the base of those persons wealth.

This is the first paper that employs IC concept to people wealth while previous literature is dedicated to companies’ or countries’ IC.

Investments in intellectual capital (IC) are often linked to competitive advantages that improve economic profit and increase the value of a company. However, this effect is reciprocal: companies that generate higher cash flow can invest more in IC. The purpose of this paper is to analyze a dynamic relationship between IC components and economic profit, with a special emphasis on industry specific effects in pharmaceutical, retail, steel, telecommunications, and service sectors.

Panel vector autoregression was used to deal with the mutual influence of IC components, the lag effect, and heterogeneity. The data were taken from Compustat database and covers the period from 2001 to 2010.

The paper proves that there is interaction between investments in the IC components and company performance. However, there are sectoral differences: there is a positive impact of economic profit on human capital in retail; in the steel industry a mutual influence is revealed. Moreover, interaction between different IC components is detected in telecommunication and steel industries.

This is the first study to present clear evidence of the effects of performance on IC investment decisions. The time lag in the effects of IC investments was estimated and compared for different industries. On the methodological side, the paper presents a rather simple method capable of yielding results consistent with other studies and yet rich enough to be applied to an analysis of sectoral differences in dynamic IC investment decisions.

Company intellectual capital (IC) is nowadays considered as a key resource that can transform a company’s value. For this reason, the efficiency of IC is crucial for all stakeholders. Evaluating efficiency is difficult, because IC is partly unobservable and its efficiency varies across time. The aim of this study is to suggest a methodology for estimating the dynamic efficiency of a company’s intellectual resources.

The panel data model suggested by Kneip et al. (2012) is used to estimate dynamic efficiency. The main feature of this model is that the unobservable component has a multi-dimensional factor structure. Taking advantage of the ability of this model to control for unobserved complex heterogeneity, the authors use the results in further stochastic frontier analysis. A data set containing information about Russian companies for the period from 2001 to 2010 is used.

In this paper, the dynamic efficiency of Russian companies is estimated. It is shown that, using the traditional efficiency estimate, companies can be overestimated.

The main limitation of the suggested methodology is that it is necessary to have a long panel data structure.

Taking advantage of time-varying efficiency, one can estimate the efficiency growth rate as a measure of performance, standard deviation as a measure of risk and autocorrelation as a measure of stability.

This is the first study to present clear evidence of the time-varying nature of IC efficiency. On the methodological side, the paper presents a fairly simple method capable of estimating various indicators of a company’s efficiency.

The purpose of this paper is to present a tool to categorize companies as potentially profitable on the basis of an intellectual capital (IC) analysis.

The paper distinguishes two crucial attributions for picking shares: IC and capitalization of IC-based growth potential. Using these two attributions, the author creates a portfolio from a sample of European companies and annually rebalances it. To test its attractiveness, the author then compares the portfolio with benchmarks and random portfolios during the period from 2006 to 2013 using a Sharpe coefficient.

The comparison of the constructed portfolio with the benchmarks demonstrates the importance of IC for market investors and the validity of the proposed tool. The Sharpe ratio of the portfolio is significantly higher than the mean and median Sharpe ratios of random portfolios. In addition, the importance of IC for choosing proper investment goal increases in crisis.

This investigation can be improved by analysing other IC such as the qualification of CEOs, participation of the company in business alliances, and a company’s innovation activity. In addition, the paper considers only European companies.

The proposed tool provides a method to construct investment-attractive portfolios on the basis of IC.

The paper contributes to the literature by identifying the underestimated shares on the basis of a company’s IC and by developing an algorithm to create an IC-based investment portfolio.