Transparency and information asymmetry in the financial market: Strategic dependencies between sustainability disclosure, SDG achievement and financial and information efficiency

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In today’s financial world, the pursuit of sustainable development has evolved from an ethical imperative to a strategic necessity. It has spurred corporations to enhance transparency regarding their non-financial and responsible or ESG practices. This paper aims to formalize the strategic dependencies between sustainability disclosure, SDG achievement, and the financial and information efficiency of the financial market. The research methods are normality tests, canonical correlation analysis, and multivariate multiple and univariate regression analysis. The object of the study is 137 countries. The time period is 2022. The results confirmed that a positive strong correlation was found between sustainability disclosure and the achievement of the SDGs on the one hand and financial and information efficiency of the financial market on the other. Identifying the direction of the relationship also confirmed two-way positive dependencies between the indicators, in particular, the SDG Index will have the most significant impact on the growth of GDP per capita, the change in the Economic Sustainability Competitiveness Index on the growth of the United Nations Global Compact participants. The specified connection can be used as the basis for the formation of the concept of ensuring transparency and leveling information asymmetry in the activities of enterprises.

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    • Figure 1. Relationships between sustainability disclosure, SDG achievement and financial and information efficiency of the financial market within the CCA
    • Table 1. Input data characteristics
    • Table 2. Descriptive statistics for the input dataset
    • Table 3. Evaluation of canonical functions
    • Table 4. Estimation of standardized canonical coefficients
    • Table 5. Evaluation of canonical correlation coefficients
    • Table 6. Results of multivariate multiple and univariate regression
    • Table A1. Geography of the sample
    • Conceptualization
      Inna Makarenko, Viktoriia Gryn, Nelia Proskurina
    • Data curation
      Inna Makarenko, Nelia Proskurina
    • Methodology
      Inna Makarenko, Viktoriia Gryn, Nelia Proskurina
    • Resources
      Inna Makarenko, lryna Pushkar
    • Software
      Inna Makarenko, Nelia Proskurina
    • Supervision
      Inna Makarenko, Nelia Proskurina
    • Writing – original draft
      Inna Makarenko, Viktoriia Gryn, Nelia Proskurina, lryna Pushkar
    • Writing – review & editing
      Inna Makarenko, Viktoriia Gryn, Nelia Proskurina
    • Formal Analysis
      Viktoriia Gryn, lryna Pushkar, Valentina Goncharova
    • Project administration
      Viktoriia Gryn, Nelia Proskurina
    • Validation
      Nelia Proskurina, lryna Pushkar, Valentina Goncharova
    • Investigation
      lryna Pushkar, Valentina Goncharova
    • Visualization
      lryna Pushkar, Valentina Goncharova