Abstract The limits testing method known as ARDL (Autoregressive Distributed Lag) examines Nigerian capital market improvement and monetary development. Relevant indicators about the capital market are taken into account during analysis while considering influencing factors as well. This study aims to explore the relationship between the capital market and financial development in Nigeria. The study’s theoretical framework, which is based on the ARDL framework, includes both short- and long-term dynamics. The data used in this analysis was collected from the Central Bank of Nigeria (CBN), the World Bank database, and the Nigerian Stock Exchange (NSE). In this study, the Nigerian All-Share Index, foreign direct investment, currency exchange rates, and inflation rates are the free factors, while the increase in GDP is the dependent factor. Through cointegration analysis using the ARDL framework, it was discovered that in Nigeria there’s a lengthy equilibrium correlation between economic expansion and financial development. The coefficients determined allow for a deeper understanding of how capital market factors affect economic growth over time. Furthermore, utilizing an error correction model derived from the ARDL analysis offers insight into both brief dynamics and modified speed toward reaching a lasting state of balance. By utilizing the ARDL approach, this study adds to the continuing discourse surrounding how capital markets impact growth in Nigeria. Its empirical evidence provides valuable knowledge for policymakers and stakeholders looking to utilize capital markets effectively toward achieving long-lasting economic development within the country.

Abstract Institutional and executive shares account for the majority of ownership and have a considerable impact on the firm’s future financing, investment, earnings, and other corporate decision-making activities. This study aims to investigate the statistical links of institutional ownership and executive ownership on financial earnings and the corporate cash level of non-financial firms using a balanced dataset of 200 non-financial listed firms in PSX (Pakistan Stock Exchange) during the period 2013 to 2018. Among many advanced econometric methods, fixed effect models with pooled ordinary least square (OLS) estimation were found more appropriate to our investigation. Our main findings are twofold. The outcome of our analysis indicates that institutional ownership and executive ownership are significantly related to financial earnings. Further, our results suggest that there is a significant relationship between executive ownership and corporate cash level, as well as a positive and significant relationship between institutional ownership and finance. The cash level of firms can only be identified and predicted through executive ownership in a developing economy like Pakistan. This study provides insightful information for non-financial industry shareholders and policymakers in Pakistan.

Abstract Motivated by recent studies that show that ownership characteristics have an effect on auditor opinion and auditor change, this research examines the effect of ownership concentration on the auditor switching with modified audit opinion as a mediation variable. The explanatory variable is ownership concentration, and the explained variable is auditor change and modified audit opinion as mediating variables. The method used for analysis is called logistic regression. The data is related to manufacturing companies listed on the Tehran Stock Exchange from 2013 to 2022. Research findings show that ownership concentration has a positive and significant effect on auditor change. Ownership concentration has a significant negative effect on the modified audit opinion. Modified audit opinion mediates between ownership concentration and auditor change. Empirical findings show that high ownership concentration may increase the probability of auditor change and decrease the probability of modified audit opinions.

Abstract This study performs a comparative analysis of autoregressive moving average models for non-negative time series within a financial context, aiming to identify the model that offers the best fit and forecasting accuracy. The analysis is applied to two financial datasets: the stock trading volume of Banco Bradesco and the insurance volume of Porto Seguro. Four models are fitted in the process: Autoregressive Moving Average (ARMA), Rayleigh Autoregressive Moving Average (RARMA), Generalized Autoregressive Moving Average (GARMA), and Generalized Linear Autoregressive Moving Average (GLARMA). Model performance is evaluated through fit comparison metrics and forecasting accuracy measures to determine the most effective model.

Abstract In this paper we propose a multi-target linear shrinkage estimator of the precision matrix by shrinking the inverse of the sample covariance matrix directly, which is a generalization of the single-target linear shrinkage estimator. The explicit expression of the weights of multi-target linear shrinkage estimator is derived when the ratio of the dimension p and the sample size n tends to a positive constant c ∈ (0, 1). The numerical simulation and an empirical analysis of financial market data are provided to compare the multi-target linear shrinkage estimator with some other estimators of the precision matrix proposed in the literature. The computation results show the improvement of the multi-target linear shrinkage estimator.

Abstract In this paper, we propose a novel application of classical directional statistics to model the cross-correlation of asset volatility in financial networks. The proposed novel Circular Volatility Model (CVM) provides a framework for studying the interdependencies of financial assets whose returns exhibit periodic behaviors. By extending traditional volatility models into a circular framework, we establish new pathways for understanding the cyclicity inherent in market dynamics. Our model is rigorously grounded in classical \& directional statistics, utilizing von Mises distributions for parameter estimation and novel circular covariance structures. We offer formal derivations, maximum likelihood estimates, and a novel goodness-of-fit testing framework for this circular model. We establish our methodologies using simulation studies.

Abstract At present, digital transformation has become a vital way for companies to achieve sustainable growth. This paper reviews the literature on the correlation between digital transformation and corporate governance paradigm, analyzes the specific impact mechanism of AI technology and big data technology in the field of corporate governance, and explores the influence effect of the most popular ChatGPT technology on corporate governance from the perspective of business practice. It is found that digital transformation has an important impact on stakeholder management, information disclosure, green governance and other aspects of corporate governance. The purpose of the study is to provide a new reference for the construction of corporate governance paradigm and help companies achieve long-term development in the digital wave.