Leonardo H. S. Fernandes

This research employs Martins, Fernandes, and Nascimento (MFN), a statistical confidence interval estimation method for information theory quantifiers, Permutation entropy (𝐻 𝑠), and Fisher information measure (𝐹 𝑠), encompassing financial assets' daily closing price time series. Specifically, we examine the five more relevant cryptocurrencies, considering market cap and volume: Bitcoin (BTC), Ethereum (ETH), BNB, Solana (SOL), and XRP. At the same time, we investigate two stock market indexes, the S&P 500 and NYSA Composite (NYA), one commodity (Gold), and one exchange rate (EUR/USD). Based on the values of 𝐻 𝑠 and 𝐹 𝑠 , we construct the Shannon-Fisher Causality Plane (SFCP), which allows us to quantify disorder and evaluate randomness in the daily closing prices of various financial assets. Also, we provide novel insights related to the SFCP with density contours. Our findings reveal that XRP, BNB, and BTC are positioned close to the random ideal position (𝐻 𝑠 = 1, 𝐹 𝑠 = 0) on the SFCP, indicating a higher level of disorder, a low level of predictability, a high level of informational efficiency, a low levels of informational asymmetry and speculative activity. Otherwise, S&P 500, NYA and Gold are further from this random ideal position, suggesting a higher susceptibility to market inefficiencies and speculation. Moreover, cryptocurrencies are characterized by less dense density contours with high 𝐻 𝑠 and low 𝐹 𝑠. In contrast, classical financial assets are marked by denser density contours with low 𝐻 𝑠 and high 𝐹 𝑠. Specifically, XRP, BNB and BTC showed less dense contours than the other financial assets. Gold, NYA and S&P 500 showed the densest density contours. The Principal Component Analysis (PCA) technique validated our findings, verifying that cryptocurrencies, S&P 500, NYSA and Gold are safe haven financial assets. Our results underscore the potential of these cryptocurrencies to provide more reliable signals for investment decisions, thereby reducing risks associated with information asymmetry and speculative trading.

This paper sheds light on the changes suffered in cryptocurrencies due to the COVID-19 shock through a nonlinear cross-correlations and similarity perspective. We have collected daily price and volume data for the seven largest cryptocurrencies considering trade volume and market capitalization. For both attributes (price and volume), we calculate their volatility and compute the Multifractal Detrended Cross-Correlations (MF-DCCA) to estimate the complexity parameters that describe the degree of multifractality of the underlying process. We detect (before and during COVID-19) a standard multifractal behavior for these volatility time series pairs and an overall persistent long-term correlation. However, multifractality for price volatility time series pairs displays more persistent behavior than the volume volatility time series pairs. From a financial perspective, it reveals that the volatility time series pairs for the price are marked by an increase in the nonlinear cross-correlations excluding the pair Bitcoin versus Dogecoin [ ]. At the same time, all volatility time series pairs considering the volume attribute are marked by a decrease in the nonlinear cross-correlations. The K-means technique indicates that these volatility time series for the price attribute were resilient to the shock of COVID-19. While for these volatility time series for the volume attribute, we find that the COVID-19 shock drove changes in cryptocurrency groups.

This paper represents a pioneering effort to investigate multifractal dynamics that exclusively encompass the return time series of USA Education Group Stocks concerning two non-overlapping periods (before, during, and after COVID-19). Given this, we employ the Multifractal Detrended Fluctuations Analysis (MF-DFA). In this sense, we investigate the generalized Hurst exponent [ ] and the Rényi exponent [ ] for each asset and quantify their statistical properties, which allowed us to observe separately the contributing small scale (primarily via the negative moments [ ]) and the large scale (via the positive moments [ ]). We perform a fourth-degree polynomial regression fit to estimate the complexity parameters that describe the degree of multifractality of the underlying process. Also, we shall apply the inefficiency multifractal index to assess the COVID-19 shock for both periods. Our findings show that for both periods, the majority of these assets are marked by multifractal dynamics associated with persistent behavior [ ], a higher degree of multifractality and the dominance of large fluctuations. At the same time, most of these assets show asymmetry parameter [ ] for both periods, indicating that large fluctuations contributed more to multifractality in the time series of returns.

In this paper, we explore the price dynamics of 16 representative records of USA Education Group stocks, encompassing two non-overlapping periods (before, during, and after COVID-19). Based on information theory and cluster analysis techniques, our study provides insights into disorder, predictability, efficiency, similarity, and resilience/weakness, considering the most diverse financial stakeholders. Our findings contribute significantly to understanding price dynamics in the education sector, encompassing non-crisis and crisis periods, and provide valuable information for financial risk management, highlighting the importance of considering financial assets’ efficiency and resilience/weakness during economic turbulence, such as the COVID-19 crisis.

We explore the synergic interplay between entropy (disorder), predictability, and informational efficiency of the daily closing price time series of 13 sectoral economics components of the Shanghai index letter considering three non-overlapping periods (before and during COVID-19 and Russia-Ukraine war). Our findings reveal that the telecom services, financials, and consumer discretionary sectors are marked by higher informational efficiency. Otherwise, the industrials, utilities, and transportation sectors exhibit lower informational efficiency. These insights are relevant for financial agents to make informed decisions, manage risk, and seek opportunities in an ever-changing market environment.

We have examined the nonlinear cross-correlation between the São Paulo time series of the weekly price of Bioethanol and the other 14 Brazilian capitals’ time series of the weekly price of the same biofuel using the Multifractal Detrended Cross-Correlation Analysis (MFDCCA). We provide evidence of multifractal correlation properties across these prices. We implement our method using both prices and returns and show some divergence between results. We recommend using price time series when using MF-DCCA.