Using a cross-country dataset covering 9265 observations on 1785 firms representing 53 countries over the period 2004–2019, this study investigates the relation between carbon emissions reduction and corporate financial performance (CFP). We perform OLS regressions with fixed effects. We found that carbon emissions reduction increases the return on assets, the return on equity, and the return on sales, whereas it has no effect on the Tobin’s Q and the current ratio. The positive relationship with the return on assets is stronger for firms with a higher responsibility score. We study country characteristics by modeling GDP growth, overall emissions within a country, and the presence of carbon emissions legislation. Our results indicate that the overall carbon emissions of a country and the presence of carbon emissions legislation are related to both corporate carbon emissions reduction and CFP. Moderating effects of the country’s overall emissions and the presence of carbon emissions legislation do not affect the relationship between carbon emissions reduction and CFP. Despite the further understanding gained, the issue of whether it “pays to be green” can still not be resolved well.

The article comprises analysis of theoretical and practical aspects of measurement of the city's intellectual capital. This article includes analysis of the intellectual capital concept and possibilities of its application at the city level, generalization of the organization intellectual capital models and principles, which, considering the macro-level context and overview of tendencies of economic growth, are applied for measurement of intellectual capital of the city. The newly created city's intellectual capital balance index is presented. The empirical application of the method proved that it is an appropriate tool for the measurement of the city's intellectual capital within a country, which corresponds to the second level NUTS of the European Union. This article is one of the ambitions to promote the methodological background for urban governance and improvement of intellectual capital as well as competitiveness of Lithuanian cities. The city's intellectual capital balance index can be used as the tool for assessment of efficiency and timeliness of the urban and national development strategies, also as the tool for publicity for innovation, creativity and “smartness” of the city or the whole country.

Economic (sometimes called "business") cycle research is one of the most popular topics of scientific literature discussions over the last years encompassing global economy long-term grow and recession starting from 2007. Such cycles can also be observed in banking activities-decreasing crediting volumes can be noticed in the majority of countries. However, the interaction between financial and business cycles is not fully revealed and differs in different countries. In the case of Lithuania credit volume and business activeness cyclic interaction can be also named as specific, reflected in gross domestic product (GDP) and credit volume fluctuations. Based on various economic cycle stages identification methodologies, not every single fluctuations can be assigned as an economic cycle stage: some of them are not identified as significant and are not recognised as economic cycles, the others match an economic cycle stage and time criterions and can be recognized as economic cycles. In the current situation, when global economy and the situation in Lithuania show recovery signs from recession to growth, credit market reacts respectively. Though the question of business and credit volume cycles is very actual, because knowing credit market dynamics indications and synchronization level between credit and economic cycles different financial stability implementation politics measures can be developed. The importance of business and credit volume cycles interaction research is also evident from the number of theoretic studies, however, to investigate interaction between an economic cycle and crediting activities in Lithuania banks there was adopted methodology developed by Kress (2004) and Avouyi-Dovi et al. (2006), including these main stages: 1) identification of turning points according to Avouyi-Dovi et al. (2006) adopted Harding and Pagan methodology, identifying peaks and troughs when two different economic cycle indicators are selected (Industrial production (Lt) and GDP (Lt)) with three credit cycle indicators (Total loans (Lt); Household loans (Lt); Loans to non-financial corporations (Lt)); 2) Calculation of concordance index between economic cycle and banks provided credit volume using business and credit cycle indicators; 3) Valuation of dependence between economic indicators and banks credit activities ratios using correlation function; 4) Conclusions delivering. Research methodology consists of separate independent stages, what makes possible to compare the results in separate stages and deliver more comprehensive conclusions. Obtained results revealed that peak in Total loans indicator converge with the peak in economic cycle indicators, but the peak in Household loans is accessed earlier than the peak in economic cycle indicators. These tendencies were also approved by the correlation analysis and calculation of a conformity indicator. The results allow to deliver significant conclusions about government monetary political decisions influencing the country's economic cycle fluctuations and determining fi...

Over the years, technological progress has accelerated highly, and the speed, flexibility, human error reduction, and the ability to manage the process in real time have become more critical and required production companies to adapt production and business models according to the needs. The demand for real-time decision support systems adapted to these raising business needs is continuously growing. Nevertheless, businesses usually face challenges in identifying new indicators, data sources, and appropriate financial modeling methods to analyze them. This paper aims to define and summarize the main financial/economic forecasting methods for production companies in the context of Industry 4.0. Main findings show forecasting accuracy of up to 96% when combining economic and demand information, optimal forecasting period from 10 months to five years, more frequent use of soft indicators in forecasting, the relationship between company’s size and production planning. Four groups of indicators used in financial modeling, such as (I) production-related, (II) customers’ and demand-oriented, (III) industry-specific, and (IV) media information indicators, were separated. The analysis forms a suggestion for decision-makers to pay more attention to the forecasting object identification, indicators’ selection peculiarities, data collection possibilities, and the choice of appropriate methods of financial modeling. AcknowledgmentThis work was partly supported by Project No. 0121U100470 “Sustainable development and resource security: from disruptive technologies to digital transformation of Ukrainian economy”.

The curiosity of how startups become unicorns is increasing. Only one-fifth of unicorns operating in the world trade their shares publicly. Financial data from the balance sheets and profit (loss) statements of 97 unicorns, which had IPOs between 2009-2018, was collected with the aim to analyse what specific characteristics of financial ratios over a particular IPO related period can be identified for unicorns operating in different regions and sectors. ANOVA was used to analyse the financial efficiency from different perspectives: (I) the financial profile of a unicorn, (II) the financial efficiency of a unicorn based on the business sector (Software; Products and Services; Technology; Internet and Healthcare sectors), and (III) the financial efficiency of a unicorn based on the region of origin (US+, Europe and Asia). Research showed that unicorns are mostly financed by investors, but remain unprofitable. Positive profitability was found in Europe, and the highest liquidity - in Healthcare sector.

After the analysis of scientific studies and evaluation of available research results, which all of them confirm the investors' financial management efficiency based on well timed management of investment decisions, main problem is identified -investor's choice of investment instruments under influence of different economic cycle periods. The purpose of article defined -to select the appropriate financial investment timing strategies according countries economical cycle stages, based on the case of Lithuania economical cycle. After the financial investment timing strategies analysis, equity shares and funds were assigned for economic growth period; meanwhile commodities, bonds and deposits in the banks were assigned for recession period. The results of Hodrick-Prescott filter application showed, that Lithuanian economical cycle continued from 2004 Q2 till 2010 Q1. These periods can be viewed as the effective timescales of financial investment and strategies defined can be used to reflect investment decisions according country's economical cycle. Research methods used are based on systematic literature analysis, mathematical statistics methods, logical comparative and generalization analysis.

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The latest research confirms the necessity of investment timing strategies based on generated returns which keeps savings work more efficient. Earlier studies involved country's economical cycle and well timed investment decisions according to cycle period. But the main constraint was defined-the decision to purchase a security can be difficult since there are many attributes to consider and can include the necessary examination of these attributes, it also can be thought of as a multi-criteria decision-making problem. The purpose of this article is to demonstrate a created personal investment decisions model, which allows private investor to make effective investment decisions during different countries economical cycle periods. Research methods used are based on systematic literature analysis, mathematical statistics methods, logical comparative and generalization analysis. After the systemized literature studies, a model of evaluating efficiency of private investor's decisions was created and it consists of five stages: (1) investor behaviour analysis (survey), (2) the determination of the economic cycle stages (Hodrick-Prescott filter method), (3) private investor's behavioural performance assessment, (4) investment instruments selection (Data Envelopment Analysis Method-multi-criteria decision-making technique) and allocation of savings analysis, (5) the investor's behaviour in performance assessment. This model allows evaluating of efficiency of private investor's investment decisions during different country's economic cycle phases. Created model application is performed during different stages of Lithuania's economic cycle. Also there is a summary of adaptive model results, where financial return of investment was compared to effectiveness of average statistical Lithuanian's residents financial decisions (18 investment portfolios are summarized). It was found that created model could be relatively simply adapted to practice and also could empower private investor to make effective personal finance decisions on the influence of country's economic cycle. It should be noted, that studies show the amount of average Lithuanian's revenue loss on the impact of inefficient personal finance management decisions.