Abstract The following research paper is devoted to the complex topic of modeling stochastic financial markets using the example of auction markets. The presented model for market makers’ behavior on stochastic auction markets contributes practically to the field of studying portfolio optimization, risk management, market participants’ balance processes, and prediction problems via cutting-edge machine learning and statistics approaches. The reliability of the given model is proved practically with the help of modern machine learning methods of validation, namely, combinatorial splits. A client-server model for remote simulation was implemented, as well as interpreted language in C++. XGBoost, Catboost, LSTM, NN Ensemble, and H 2 O Auto-ML models were considered in the course of building the decision model. Hyperparameters were obtained via Optuna. Besides that, the developed model was backtested on historical data of different financial assets, starting with stocks and ending with commodity prices and foreign exchange rates. Within all models, positive Sharpe ratios have been obtained, which indicates the robustness of the model. The paper offers a valuable framework for market maker decision-making stochastic modeling, examining its pricing mechanisms and financial risk management as crucial for exchanges, funds, and other financial institutions, which makes it relevant in the context of the current dynamics of the development of financial markets and the increase in trading volumes.

Abstract This work aims to analyze the efficiency of Brazilian financial institutions until the COVID-19 pandemic period, from production and profitability perspectives. To accomplish this, the data envelopment analysis (DEA) techniques, specifically the CCR and BCC models, are applied to 213 Brazilian financial institutions in four methodological stages. The first step involved conducting a literature review of similar studies. The second step consisted of gathering financial information for each bank through the website of the Central Bank of Brazil. The third step involved selecting the variables to be used in the models. The fourth step was outlier detection using the jackstrap method. Subsequently, the mentioned efficiency models were applied, and the most efficient banks were identified based on each perspective. The results identified heterogeneous groups of efficient banks based on different market segments, with a focus on the efficiency of large banks and public banks when considering the production-oriented perspective. It is also observed that new digital banks are among the banks considered efficient. These findings are valuable for the scientific literature investigating the sustainability of financial institutions, as well as for decision-makers seeking to make more efficient investment allocations and for banking supervisory authorities in formulating risk regulatory policies.

Abstract This study evaluated the relationship between firm returns and stock price volatility of listed commercial banks on the Dar es Salaam Stock Exchange (DSE). A quantitative research design was utilized in the examination. The research utilized company-year observations spanning from 2011 to 2020, sourced from Seven (7) banks. The secondary data came from listed commercial banks’ annual reports. In this investigation, panel data regression was employed. Based on the results of the panel regressions. The study’s results also showed that the volatility of commercial banks’ share prices was somewhat impacted negatively by corporate returns. Additionally, the study suggests that commercial banks increase their earnings per share in order to stabilize the price volatility of commercial banks listed on the DSE.

Abstract Green innovation helps companies achieve high-quality sustainable development, and environmental, social responsibility and corporate governance (ESG) performance impacts enterprises’ green innovation capability. Taking the data from 2011 to 2021 of Chinese A-share listed companies as the research sample, this paper empirically tests the impact of corporate ESG performance on green innovation and explores the impact mechanism. Measuring firms’ ESG performance through ESG score given by a third-party rating agency, this paper finds that better ESG performance enhances firms’ green innovation capability. Based on the double externality of green innovation, we find that better ESG performance of enterprises can enhance their green innovation capability by incentivizing firms in the same industry to innovate, strengthening external supervision, and alleviating financing constraints. As an important informal system in China, Confucianism has a certain inhibitory effect on firms’ green innovation capability. This paper provides a decision-making reference for the effectiveness of ESG in the Chinese market and corporate green sustainable development by investigating the impact mechanism of ESG performance on corporate green innovation capability.

Abstract The middle-income trap has been in developmental discussions for the better part of the last decade since the inability of most Latin American countries to break out of it. There is renewed interest in the concept since the realisation that one of the biggest economies in the world, i.e., China could be caught in it. While there is abundant literature on the part that social infrastructure and human capital development plays, the effect of financial systems is relatively ignored. This paper seeks to fill this gap by understanding the role of financial system variables in escaping the middle-income trap. By taking a sample of thirteen countries and using two classification techniques—Naive Bayes and random forest, it is concluded that both bank-based measures and market-based measures have an impact on income levels. These results have strong implications on understanding how to break out of the trap for policymakers.