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 Tax revenue is an important public asset that contributes significantly to a nation’s economic development. The primary function of every government is to make provision in terms of infrastructure facilities, development, and better living conditions for its people. Due to the government’s limited resources, it is necessary to impose taxes on all residents and businesses to strengthen its financial situation because it is impossible for it to carry out this large task in an effective manner. Governments have always passed numerous tax laws, and they have been amended to withstand the test of time. Therefore, this study’s goal is to determine whether taxes have an impact on economic development in the Cape Coast Metropolis in Ghana. The study used the quantitative approach, survey technique, and questionnaire for the data collection process. A sample size of 115 respondents comprising the staff of the domestic tax revenue division’s workforce and owners of registered SMEs were engaged in the study. The data was analyzed using the SPSS version 26. The results indicated that the majority of the staff of Ghana Revenue Authority (GRA) considered payment of tax as beneficial to the state while few of the staff did not consider payment of tax as beneficial to the state. In addition, the study outcomes show that respondents are aware of the extent to which tax revenue can be used by the government for economic development. Furthermore, the researchers recommended that the head of GRA create a strong regulatory framework to monitor how tax money is used for economic growth. They should also make an effort to ensure that the tax revenue generated will be wisely used for the growth of the metropolis. This will make taxpayers appreciate the need to pay their taxes because they know they will benefit from it in the end in the form of social and economic developments.

Abstract This paper analyses selected sub-indices listed on the Warsaw Stock Exchange (WSE) covering seven sectors: construction, IT, media, real estate, fuel, food, and telecommunications, from 3 January 2006 to 29 May 2020. We use daily, weekly, monthly, and quarterly data, resulting in 3600 daily, 751 weekly, 172 monthly, and 56 quarterly observations. The WIG index quotations were used to approximate the market portfolio and the Poland 10Y government bond yields for the risk-free rate. We have estimated the parameter β in CAPM regression using three different stochastic assumptions for the error term. The basic stochastic framework of the model utilises the generalised asymmetric student- t distribution (GAST). We have also estimated the parameter β based on the symmetric version of the GAST distribution and on the Gaussian one. These models can be treated as a special case of the basic framework. The estimates of the β parameter are influenced by the assumptions made about the error term. The data indicates that except for WIG-Paliwa, the Gaussian error term leads to larger β estimates than other non-Gaussian specifications. The inference about the shape parameters is not very certain, and the data does not strongly support the two-piece mechanism that enforces the asymmetry of the error term distribution. Furthermore, the estimates of the β parameter depend strongly on the frequency of the analysed series.

Abstract This study evaluated the relationship between firm return and stock price volatility of listed commercial banks in 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 return. 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.