Purpose: The problem of identifying the most important determinants of the market value of football players is quite well described in the literature. There are many works that try to identify these factors. Some of them are focused on variables to make a set complete and others are on models and methods. The aim of this article is to present the variables influencing the valuation and to build an econometric model valuing footballers playing on the forward position, taking into consideration the assumptions of the econometric modelling. Such an approach indicates managers as other sources for obtaining information. Methodology: Econometric models were used to verify the hypothesis formulated in this research. The database was created on the basis of variables presented on the website www.transfermarkt. de that presents the values of the most valuable football players in the world playing on the forward position. The Gretl program was used in the research. Findings: The literature review showed that there are many independent variables having an impact on the value of the player. There are also many different models used to valuate footballers’ performance rights. The results of estimation of models in the research indicated that such factors as Canadian classification points adjusted the market value of the team and dummy variables describing “goodwill” (only for the best players) had an impact on the market value of footballers’ performance rights. Limitations/implications: Information about different factors having an impact on football players’ market value could support the investment decision process of football managers. Originality/value: The results were part of a study concerning economics of sport, particularly processes of management of football clubs and valuation of intangible assets

The purpose of this study is to shed light on the nexus between electricity supply and economic growth in South Asian countries during 1990–2018. The study employs Pedroni’s panel cointegration test as well as Dumitrescu and Hurlin’s (DH) causality test for panel data. The empirical results confirm a long-term relationship between electricity supply and economic growth. We fail to reject the non-causal relationship between electricity supply and economic growth for the panel, thereby affirming the neutrality hypothesis. Single country causality analysis reveals the growth hypothesis in the case of Pakistan. These results have a number of policy implications. For example, governments can introduce measures to improve energy efficiency in Bangladesh, India and Sri Lanka without fear of harming economic growth. The results for Pakistan may also imply that fostering green energy generation would lead to a positive effect on economic growth via improved electricity production. The government may use various policy tools to stimulate adoption of renewable energy, such as fiscal incentives, low interest loans, or grants for rural populations to speed up the green energy transformation.

Recent researches on behavioral finance have tested for, among others, evidence for the relations between weather, investors’ mood, and investment decisions. Many of the researches related to the influence of some weather factors, such as sunshine duration on stock exchange returns, but there is no complex research taking into account a wide group of weather factors determining investors’ mood. The main goal of the article is to verify the influence of weather factors on basic market parameters of energy sector companies quoted on the Warsaw Stock Exchange. Rates of return, trading volumes, and values of trading volume are taken into account during the research. All analyses are based on econometric models assuming the existence of typical problems of estimation such as: autocorrelation of residuals, heteroskedasticity, or abnormality of residuals. The best approximation of models was obtained for GARCH (Generalized Autoregressive Conditional Heteroskedasticity) type models.

This work aims to design a proposed decision support system (DSS) for helping investors in making investment decision by using rule text-mining based algorithm to analyze news headline and implement analyzing pro-gram based on a manual analyzed headlines. The news analysis program (NAP) was used as an important stage in making investment decision on sample of the Gulf Cooperation Council (GCC) stock markets using Alarabia.net and Reuters.com which treated as a source of media noise that has an influence on the value of stock quoted stock market. The second kind of data that proposed to use in this system is the financial data of GCC stock market. The resulted data can be used in further steps to make better understanding of stock market companies behavior such as the statistical, data mining calculation for choosing the best period of time that give the best reaction of stock market ratios to the news indicators and using the vector measure construction method (VMCM) for classifying companies according to their response to the news.

Traditional finance explains all human activity on the ground of rationality and suggests all decisions are rational because all current information is reflected in the prices of goods. Unfortunately, the development of information technology and a growth of demand for new, attractive possibilities of investment caused the process of searching new, unique signals supporting investment decisions. Such a situation is similar to risk-taking, so it must elicit the emotional reactions of individual traders.The paper aims to verify the question that the market risk may be the determinant of traders’ emotions, and if volatility is a useful tool during the investment process as the measure of traders’ optimism, similarly to Majewski’s work (2019). Likewise, various econometric types of models of estimation of the risk parameter were used in the research: classical linear using OLS, general linear using FGLS, and GARCH(p, q) models using maximum likelihood method. Hypotheses were verified using the data collected from the most popular world stock exchanges: New York, Frankfurt, Tokyo, and London. Data concerned stock exchange indexes such as SP500, DAX, Nikkei, and UK100.