Search Results (11)

This paper examines whether standard price-discovery measures can reliably identify directional predictive precedence in a highly correlated commodity-equity system. Using 21 years of daily data for Petrobras and the Ibovespa (2005–2026), the study separates a measurement problem in forecast error variance decomposition from the reduced-form question of directional predictability in the conditional mean. The empirical strategy combines Monte Carlo simulation, generalized and Cholesky forecast error variance decompositions, full-sample and rolling-window Granger causality tests, a continuous Granger Leadership Index, Gaussian mixture regime classification, robustness checks, and out-of-sample forecasting validation. The results show that Cholesky-based FEVDs can be systematically misleading in high-correlation settings: at the observed contemporaneous correlation, generalized FEVD symmetry is mechanically induced by row normalization, while Cholesky attribution changes sharply under alternative orderings. By contrast, first-moment predictability reveals a directional asymmetry from Petrobras to the Ibovespa, interpreted as conditional-mean predictive precedence rather than structural informed trading or definitive price discovery. This asymmetry survives alternative lag structures, weekly aggregation, univariate GARCH filtering, within-dataset proxy controls, and a stylized equal-weight ex-Petrobras benchmark. Rolling evidence further identifies five persistent predictive regimes that alternate between firm-led, neutral, and macro-dominant states, indicating that firm-index predictive relations are regime dependent rather than static. Out-of-sample forecasting shows that the identified predictive precedence does not generate exploitable one-step-ahead gains (RMSE ratio = 1.002, OOS-R² = −0.003, DM p = 0.451), thereby delimiting the economic scope of the findings. Overall, the results support a reduced-form interpretation of Petrobras–Ibovespa predictive dynamics and highlight the need to distinguish variance connectedness from conditional-mean predictive content when contemporaneous correlation is high. Full article

This study explores how the volatility spillover mechanism and dynamic dependence among the founding BRICS equity markets, namely IBOVESPA, MICEX, Nifty 50, SSE, and JSE, have evolved over time using a multivariate DCC-GARCH model. The analysis is conducted across three distinct regimes: the pre-COVID-19 period (1 January 2010 to 10 March 2020), the COVID-19 crisis (11 March 2020 to 23 February 2022), and the Russia–Ukraine war and sanction period (24 February 2022 to 31 March 2024). The findings indicate that, prior to the COVID-19 pandemic, the BRICS equity markets experienced significant short-term volatility spillovers and significant volatility persistence, indicative of slow financial integration, as opposed to rapid contagion. In comparison, the COVID-19 pandemic resulted in significant structural shifts in the form of increased shock transmission, greater co-movement, and evident financial contagion among the markets. During the post-COVID-19 conflict period, while there was considerable persistence in volatility, the primary drivers of volatility spillovers were geopolitical. Across the three sub-periods, the volatility spillover network shows pronounced structural changes. Before COVID-19, IBOVESPA, MICEX, and SSE act as net transmitters, while Nifty 50 and JSE are net receivers. During the COVID-19 crisis, SSE and JSE become the main shock transmitters, whereas IBOVESPA, MICEX, and Nifty 50 shift to receiver roles. In the post-COVID-19 Russia–Ukraine war period, the network becomes more asymmetric, with JSE and Nifty 50 again emerging as net transmitters, while MICEX and SSE function primarily as net receivers. Overall, this study demonstrates that BRICS equity market interdependence is regime-specific and greatly dependent on exogenous global events. Full article

This study examines quantitative investment strategies for Brazilian equity funds, integrating traditional financial performance indicators with machine learning techniques to enhance fund selection. The main objective was to construct and validate predictive models for fund selection. The methodology involved collecting daily data from 2019 to 2025, computing a range of return and risk measures, and trained models to classify 1- and 3-month shifted windows. The 3-month models achieved the strongest predictive accuracy, exceeding 91%, with the Sharpe Ratio emerging as the most influential feature. A 12-month backtest (October/2024–September/2025) showed that ML-constructed portfolios delivered cumulative returns between 14.65% and 91.86%, depending on the selection criterion, substantially outperforming Brazil’s CDI risk-free benchmark (12.70%) and the Ibovespa (11.46%). These findings highlight the practical potential of ML-based fund selection, though successful implementation requires careful risk management and ongoing model validation. Full article

This study applies the Log-Periodic Power-Law (LPPL) framework to three major equity markets—Mexico (IPC), Brazil (IBOVESPA), and the United States (NYSE Composite)—using daily closes from 8 November 1991–30 January 2025 for IPC and NYSE, and 3 May 1993–30 January 2025 for IBOVESPA. Multi-window calibrations (L$ϵ$ 180, 240, 300, 360, 420) are estimated in raw and log space to evaluate bubble signatures and the stability of the critical time $t_c$. Across all indices, log-space fits consistently outperform raw fits in terms of RMSE and $R^2$, and longer windows reduce parameter variability, yielding coherent clusters of $t_c$. Under full-sample conditions, the LPPL structure points to March–April 2025 for NYSE, mid-October 2025 for IBOVESPA, and October–December 2025 for IPC, while shorter windows pull $t_c$ forward. A rolling early-warning ensemble translates these estimates into lead-based risk bands, with numerical reporting used when median leads fall just outside the 60-trading-day decision horizon. The early-2025 weakening in the U.S. market is consistent with the NYSE cluster, whereas Brazil and Mexico remain within their projected windows as of September 2025. The analysis highlights the strengths of LPPL—behavioral interpretability and hazard-based framing—while noting limitations such as window sensitivity and parameter sloppiness, reinforcing the need for conservative communication and the use of longer-window weighting in practical applications. Full article

The global economy frequently experiences cycles of rapid growth followed by abrupt crashes, challenging economists and analysts in forecasting and risk management. Crashes like the dot-com bubble crash and the 2008 global financial crisis caused huge disruptions to the world economy. These crashes have been found to display somewhat similar characteristics, like rapid price inflation and speculation, followed by collapse. In search of these underlying patterns, the Log-Periodic Power-Law (LPPL) model has emerged as a promising framework, capable of capturing self-reinforcing dynamics and log-periodic oscillations. However, while log-periodic structures have been tested in developed and stable markets, they lack validation in volatile and developing markets. This study investigates the applicability of the LPPL framework for modeling financial crashes in the Brazilian stock market, which serves as a representative case of a volatile market, particularly through the Bovespa Index (IBOVESPA). In this study, daily data spanning 1993 to 2025 is analyzed to model pre-crash oscillations and speculative bubbles for five major market crashes. In addition to the traditional LPPL model, autoregressive residual analysis is incorporated to account for market noise and improve predictive accuracy. The results demonstrate that the enhanced LPPL model effectively captures pre-crash oscillations and critical transitions, with low error metrics. Eigenstructure analysis of the Hessian matrices highlights stiff and sloppy parameters, emphasizing the pivotal role of critical time and frequency parameters. Overall, these findings validate LPPL-based nonlinear modeling as an effective approach for anticipating speculative bubbles and crash dynamics in complex financial systems. Full article

This research seeks to evaluate the effects of the preceding cyclical indicators and macroeconomic variables on the performance of the Brazilian stock market from January 2011 to December 2022. The objective is to identify how these factors influence the behavior of the main index representing this market. In this way, it was analyzed how shocks in the composite leading indicator of the economy (IACE) as well as the basic interest rate of the economy (SELIC), the broad national consumer price index (IPCA), the nominal exchange rate (in reals per dollar—BRL/USD) and the central bank economic activity index (IBC-Br) impact the performance of Brazilian stock market index (IBOVESPA). Using the vector autoregression (VAR) model with vector error correction (VEC), positive shocks were simulated in the IACE and the aforementioned macroeconomic variables to identify and compare their impacts on the index. The results obtained, through generalized impulse response functions, indicated that the shocks to the IACE, the exchange rate, and the inflation variables influenced the IBOVESPA in different and statistically significant ways. However, shocks to the economic activity index and the interest rate did not exert a statistically significant influence on the index, partially confirming the hypothesis, which was initially raised, that these factors influence the stock index in different ways. Full article

This study aims to assess the disclosure extent and quality, as well as the percentage of audited reports, of the Brazilian companies listed on the IBOVESPA stock exchange index and explore some factors that influence disclosure quality. A content analysis of 71 annual sustainability (or similar) and integrated reports was conducted, focused on the social dimension. Multiple linear regression was used to assess the relationship between the disclosure quality index and being audited by a Big Four company, the number of members on the board of directors, the use of the Global Reporting Initiative (GRI) standards in the preparation of the reports, and the type of industry. The results suggest that although the disclosure extent is reasonable, its quality is poor. In addition, considering its voluntary nature, the disclosure-auditing index is deemed satisfactory. The results also suggest that the disclosure quality of Brazilian companies is positively and significantly influenced by being audited by a Big Four company, by adopting the GRI standards, by the number of members composing the board of directors, and by belonging to the “Energy and utility” industry. This study contributes to the extant literature by assessing the disclosure extent and quality and the percentage of audited reports of companies in an emerging economy setting—Brazil—and exploring some factors which influence the disclosure quality in emerging countries’ companies, such as auditing by a Big Four company, which has thus far been unexplored. It also contributes to increasing the awareness of the theme among managers. Full article

Investing in stocks and shares is a common strategy to pursue potential gains while considering future financial needs, such as retirement and children’s education. Effectively managing investment risk requires thoroughly analyzing stock market returns and making informed predictions. Traditional models often utilize normal variance distributions to describe these returns. However, stock market returns often deviate from normality, exhibiting skewness, higher kurtosis, heavier tails, and a more pronounced center. This paper investigates the Laplace distribution and its generalized forms, including asymmetric Laplace, skewed Laplace, and the Kumaraswamy Laplace distribution, for modeling stock market returns. Our analysis involves a comparative study with the widely-used Variance-Gamma distribution, assessing their fit with the weekly returns of the S&P 500 Index and its eleven business sectors, drawing parallel inferences from international stock market indices like IBOVESPA and KOSPI for emerging and developed economies, as well as the 20+ Years Treasury Bond ETFs and individual stocks across varied time horizons. The empirical findings indicate the superior performance of the Kumaraswamy Laplace distribution, which establishes it as a robust alternative for precise return predictions and efficient risk mitigation in investments. Full article

Since financial assets on stock exchanges were created, investors have sought to predict their future values. Currently, cryptocurrencies are also seen as assets. Machine learning is increasingly adopted to assist and automate investments. The main objective of this paper is to make daily predictions about the movement direction of financial time series through classification models, financial time series preprocessing methods, and feature selection with genetic algorithms. The target time series are Bitcoin, Ibovespa, and Vale. The methodology of this paper includes the following steps: collecting time series of financial assets; data preprocessing; feature selection with genetic algorithms; and the training and testing of machine learning models. The results were obtained by evaluating the models with the area under the ROC curve metric. For the best prediction models for Bitcoin, Ibovespa, and Vale, values of 0.61, 0.62, and 0.58 were obtained, respectively. In conclusion, the feature selection allowed the improvement of performance in most models, and the input series in the form of percentage variation obtained a good performance, although it was composed of fewer attributes in relation to the other sets tested. Full article

This paper aims to present aspects related to corporate governance, organisational resilience and sustainable practices developed by Brazilian companies during the COVID-19 pandemic. The methodological strategy included documentary research to identify sustainability reports published by companies listed on the Ibovespa index, which were examined through the content analysis technique. The results allowed light to be shed on opportunities for improvement concerning sustainability and corporate governance. Regarding organisational resilience, risk management is central for the development of sustainability practices by organisations. The support to society related to the COVID-19 pandemic was evidenced mainly through the donation of financial resources, equipment and hospital supplies. Practices related to reducing energy consumption and minimising waste generation were those most associated with environmental aspects. The actions identified were mapped according to their relations with the SDGs. Managers can benefit from the information presented in this study to enhance organisational sustainability and resilience, while researchers can use it as a starting point for future studies. Full article

This article aims to perform a critical analysis of wage gaps according to gender from information provided by sustainability reports that were disseminated by Brazilian companies listed in the Ibovespa index. To conduct this analysis, bibliographic research was performed, followed by a deductive content analysis of sustainability reports from Brazilian companies listed in the Ibovespa index, considering item 405-2 of the Global Reporting Initiative standard. From this analysis, it was possible to show that only some companies disseminate detailed information related to the gender wage ratio. Many companies do not present this data or present it superficially. The findings of this research present important insights that may be used to motivate debates on the topic. Full article