South Africa has been severely impacted by several high-profile corporate scandals, with significant financial manipulation involving both the content of financial statements and the tone set by top executives. Notably, CEOs such as Markus Jooste from Steinhoff have been accused of misleading investors through both earnings management and the use of an authoritative management tone. This study investigates the market's reaction to the interactive effect of top management tone and earnings management, employing a short-window event study methodology. The tones of two distinct management styles—autocratic and pragmatic—are examined by analysing CEO statements using the DICTION textual analysis software. The sample comprises 944 firm-year observations spanning from 2011 to 2018. The results indicate that the market did not respond to earnings management in isolation. However, a significant negative market reaction was observed when earnings management occurred in conjunction with an autocratic tone. This suggests that South African investors are particularly attuned to multiple signals of potential fraud and will adjust their valuations accordingly. The findings underline the importance of considering not only financial disclosures but also the behavioral cues given by top management in assessing firm performance and risk. Investors, regulators, and analysts must therefore remain vigilant to the combined risks posed by earnings manipulation and the tone of management communications. The study contributes to the limited literature on the stock market's response to the interplay of earnings management and management tone, particularly in the context of South Africa, and is the first to explore the combined effects of these two forms of manipulation.

This study investigates the relationship between three prominent global stock market indices, Standard & Poor's 500 (S&P 500), Cotation Assistée en Continu 40 (CAC 40), and Nikkei 225 and the Gross Domestic Product (GDP) of the United States, France, and Japan, respectively, over the period from 2014 to 2023. This research uses empirical analysis where the correlation and regression analysis of these three investment market indices and the GDP of those three nations is done. Based on the results of this research, we can say that the S&P 500 index has a significant impact, explaining approximately 80% of the variation in the GDP of the United States, while the other two indices, Nikkei 225 and CAC 40 have shown a positive statistical relationship with the GDP of Japan and France, accounting for 42% and 51% of GDP variance, respectively. This research also examines some of the roles of factors, such as the power of financial markets as economic indicators, while acknowledging the role of other macroeconomic indicators, including unemployment and inflation. Adjustments for inflation and currency ensure consistency across datasets. These results can be used to create strategies that develop stability and economic growth, improving risk management and the development of investment strategies. This study also reflects the function of fiscal regulations in eliminating the negative influences of unstable markets on financial performance. It is recommended that future researchers use other macroeconomic indicators such as unemployment rates, inflation and research in other countries to obtain a more detailed picture about aspects which can impact economic growth.

The integration of artificial intelligence (AI) and big data analytics has revolutionized audit practices, offering unprecedented advancements in efficiency, transparency, and sustainability. This study critically examines the role of AI-powered auditing in risk detection, anomaly identification, and the development of sustainable audit frameworks. Through an extensive literature review, the adoption of machine learning (ML), natural language processing (NLP), and continuous auditing methodologies is explored, highlighting their impact on audit quality and assurance. It is demonstrated that AI-driven auditing significantly enhances error detection and risk assessment while expediting audit procedures and improving overall accuracy. However, critical challenges remain, including data security risks, algorithmic opacity, and ethical concerns related to decision-making autonomy. Addressing these issues necessitates the establishment of robust governance mechanisms, increased algorithmic transparency, and the implementation of continuous professional training programs to ensure auditors' proficiency in AI-based systems. Furthermore, AI-driven automation enables real-time monitoring and predictive analytics, fostering a proactive approach to auditing that mitigates financial and operational risks. By leveraging AI and data-driven methodologies, audit frameworks can be rendered more adaptive, resilient, and aligned with the evolving digital economy. These findings underscore the necessity for organizations to integrate AI-driven auditing as a strategic imperative while ensuring regulatory compliance and ethical oversight.