Purpose- The purpose of this study is to compare the success of alternative data imputation techniques with missing data. The study distinguishes itself from the rest of the literature by proposing an appropriate technique for mixed data on financial performance and environmental, social and governance (ESG) metrics of companies. In addition to simple imputation techniques, we also use machine learning techniques that allow working with more complex data. Methodology- We first employ ad-hoc methods such as mean, median, mode, constant, most frequent and regression imputation. In what follows, we handle multivariate imputation techniques such as multiple imputation by chained equations (MICE). Finally, we run imputation methods with machine learning (ML) classification such as K-nearest Neighbor (KNN), Ridge and Random Forest. To consider the assumptions of missing data, we first check the normality of the variables with Kolmogorov-Smirnov test and employ Rubin’s classification technique that defines the relationship among variables with the probability of missing data. The success of imputation techniques applied to missing data changes when the missing data are classified with Rubin’s technique according to randomness. Consequently, we apply listwise deletion at various levels and alternative data imputation techniques. We then compare their performances. The raw data contain parametric as well as categorical variables (binary and others). Among these are time-series (yearly) financial series such as sales and total assets obtained from financial statements, ESG scores as well as float ratios for firms from several countries and industries. Imputation is done randomly on a sample varying from 5% to 30% of the dataset and results are compared to true data based on accuracy or other measures such as root mean square errors (RMSE) or mean absolute percentage error (MAPE). Several robustness checks have been performed to supplement the analysis. Findings- Results show that ML methods such as KNN have a superior performance than others. Moreover, when multidimensional nature of the data is taken into account, the prediction performance improves. Hence, an optimality can be reached based on parameters. Conclusion- Based upon the analysis, we conclude that the selected imputation technique and how it is employed matter to attain a higher accuracy and a better prediction of the missing values on selected mixed panel data in finance. Keywords: Imputation techniques, Panel data, Machine learning, Financial performance, ESG JEL Codes: C55, C81, M14, Q51

This paper explores the impact of sustainability practices (ESG and greenwashing) on profitability, with a special focus on crises, in particular COVID-19. We analyze the post-crisis profitability of sustainable firms, distinguishing between country groups (developed/developing and high voice and accountability/low voice and accountability). We run fixed effects difference-in-differences regressions on a dataset comprising 3832 to 14,652 firm-year observations from 47 stock exchanges. Evidence suggests that firms with a higher ESG during COVID-19 achieved higher profitability afterwards. Environmental score (E) contributed the most to profitability. Although greenwashing improved post-crisis operational performance, its effect on market value is mixed. Countries exhibited different dynamics regarding the sustainability–profitability nexus: firms in developed countries and countries with greater voice and accountability achieved higher post-crisis profitability. In the post-crisis recovery period, this pattern reversed (for ESG) or disappeared (for greenwashing). Overall, companies investing in ESG (E, S, G) during COVID-19 gained substantial benefits, but those engaging in greenwashing were not penalized much, especially those in developed countries and countries with greater voice and accountability. These findings show how institutional and stakeholder-driven pressures can play a critical role in the sustainability–profitability relationship, engendering important implications for managers and policymakers.

Purpose: This article mainly investigates the direct and indirect effect (through its relation with advertising) of greenwashing on market value of firms with a special emphasis on consumer awareness and technological innovation. Design/methodology/approach: We analyze data comprising 52,861 firm-year observations for 5,280 firms from 47 countries from 2002 to 2022 and use panel regression models having greenwashing, advertising and their interaction as explanatory variables along with control variables. We also run 2SLS estimation to account for potential endogeneity. Findings: Both advertising and greenwashing are positively associated with market value in the short run. In the long run, the impact of greenwashing reveals negative. The combined effect of advertising and greenwashing remains insignificant over both the short and long horizons. However, in industries with high awareness (high technological innovation), greenwashing increases (decreases) the positive impact of advertising. In other words, consumers and other stakeholders tend to penalize (not penalize) greenwashers more in businesses with high technology (awareness) combined with intensive advertising. Practical implications: Managers, policymakers, business leaders, investors and marketing experts should consider the outcomes of advertising and green communication strategies. In particular, companies in high-tech businesses should be more cautious about implementing greenwashing. Originality/value: We contribute to the literature at the intersection of marketing (advertising expense and customer awareness), finance (firm value and asset pricing) and sustainability (corporate social responsibility and greenwashing) by offering the first empirical insights into the moderator role of greenwashing on market value.