In this paper, we compare the predictions on the market liquidity in crypto and fiat currencies between two traditional time series methods, the autoregressive moving average (ARMA) and the generalized autoregressive conditional heteroskedasticity (GARCH), and the machine learning algorithm called the k-nearest neighbor (KNN) approach. We measure market liquidity as the log rates of bid-ask spreads in a sample of three cryptocurrencies (Bitcoin, Ethereum, and Ripple) and 16 major fiat currencies from 9 February 2018 to 8 February 2019. We find that the KNN approach is better suited for capturing the market liquidity in a cryptocurrency in the short-term than the ARMA and GARCH models maybe due to the complexity of the microstructure of the market. Considering traditional time series models, we find that ARMA models perform well when estimating the liquidity of fiat currencies in developed markets, whereas GARCH models do the same for fiat currencies in emerging markets. Nevertheless, our results show that the KNN approach can better predict the log rates of the bid-ask spreads of crypto and fiat currencies than ARMA and GARCH models.

Purpose: Our findings indicate that workers with more financial education were more prepared to face the negative effects on their finances from COVID. This ability reduces the probability of becoming financially fragile and experiencing financial stress. Design/methodology/approach: The authors applied a survey questionnaire to 856 Colombian adults and used principal component analysis to build an index for each factor. Then, the authors used a linear regression model with the indexes to test our hypotheses and verify our results using a structural equation model. Findings: Our findings indicate that workers who have more financial education are more prepared to face the negative effects on their finances, which reduces the probability of becoming financially fragile and having financial stress. Research limitations/implications: The authors found that there is no significant relationship between financial literacy and financial fragility, neither between financial literacy and financial stress, so a better financial education will not lower financial fragility and stress unless it is being applied by households through better financial preparedness. Practical implications: It is important to highlight that the pandemic not only taught us to improve biosecurity measures but also that financial strength, ability to work remotely and income diversification were key factors in facing this adverse shock, the authors show that high levels of financial education have a positively relationship with the ability of individuals to manage their resources, so private and public institutions have to promote better financial education. Originality/value: This is the first study that applies the four different indexes to an emerging country (i.e. Colombia), and the first one to create and use a financial stress index.

Purpose: This paper aims to identify what are the moderating factors affecting the relationship between firms’ adoption of international financial and reporting standards (IFRS) and the firm’s opacity. Design/methodology/approach: This study uses the meta-analysis methodology from Hunter et al. (1982) to find if the mere IFRS adoption reduces firm’s opacity and a meta-regression from Stanley and Jarrell (1989) to identify the moderating factors that may influence this relationship. Findings: Contrary to previous studies, this study finds a low, negative and nonsignificant correlation between IFRS adoption and firms’ opacity, but this relationship depends on the geographical region. Using 34 results from 28 studies from different continents published between 2005 and 2018 this study finds that IFRS adoption reduces opacity in countries with common law (COML) and with more authorities’ oversight and power to enforce the rules. Originality/value: This study finds two institutional commonalities between different previous studies that intend to assess the impact of the IFRS adoption upon firms’ opacity: the legal system and the authorities’ oversight power.