Long memory and fractality among global equity markets: A multivariate wavelet approach

This paper seeks to understand the long memory behaviour of global equity returns using novel methods from wavelet analysis. We implement the wavelet based multivariate long memory approach, which possibly is the first application of wavelet based multivariate long memory technique in finance and economics. In doing so, long-run correlation structures among global equity returns are captured within the framework of wavelet-multivariate long memory methods, enabling one to analyze the long-run correlation among several markets exhibiting both similar and dissimilar fractal structures

Long Memory and Correlation Structures of Select Stock Returns Using Novel Wavelet and Fractal Connectivity Networks

This study investigates the long range dependence and correlation structures of some select stock markets. Using novel wavelet methods of long range dependence, we show presence of long memory in the stock returns of some emerging economies and the lack of it in developed markets of Europe and the United States. Moreover, we conduct a wavelet based fractal connectivity analysis, which is the first application in economics and financial studies, to segregate markets into fractally similar groups and find that developed markets have similar fractal structures. Similarly stock returns of emerging markets exhibiting long-memory tend to follow similar fractal structures. Furthermore, network analyses of fractal connectivity support our findings on market efficiency which has theoretical roots in both fractal and adaptive market hypothesis

The ChatGPT Artificial Intelligence Chatbot: How Well Does It Answer Accounting Assessment Questions?

ChatGPT, a language-learning model chatbot, has garnered considerable attention for its ability to respond to users’ questions. Using data from 14 countries and 186 institutions, we compare ChatGPT and student performance for 28,085 questions from accounting assessments and textbook test banks. As of January 2023, ChatGPT provides correct answers for 56.5 percent of questions and partially correct answers for an additional 9.4 percent of questions. When considering point values for questions, students significantly outperform ChatGPT with a 76.7 percent average on assessments compared to 47.5 percent for ChatGPT if no partial credit is awarded and 56.5 percent if partial credit is awarded. Still, ChatGPT performs better than the student average for 15.8 percent of assessments when we include partial credit. We provide evidence of how ChatGPT performs on different question types, accounting topics, class levels, open/closed assessments, and test bank questions. We also discuss implications for accounting education and research

Wavelets based multiscale analysis of select global equity returns

This paper examines the relationship between Indian equity prices other developed markets, in the time-scale domain, using wavelets based multiscale analysis and cross wavelet analysis. Stock markets are analyzed at different levels of resolution which makes it possible to perform a scale by scale analysis enabling us to detect the correlation and cross-correlation structures at time periods with high frequency oscillations and also the relatively low frequency structures. There seems to be a weak integration between BSE and other developed markets at almost all levels of time-scale resolution and a strong relationship between French and German Markets. Analyzing the stock returns at different multiscale resolution makes it easier for agents dealing with different trading horizons

Long Memory and Correlation Structures of Select Stock Returns Using Novel Wavelet and Fractal Connectivity Networks

This study investigates the long range dependence and correlation structures of some select stock markets. Using novel wavelet methods of long range dependence, we show presence of long memory in the stock returns of some emerging economies and the lack of it in developed markets of Europe and the United States. Moreover, we conduct a wavelet based fractal connectivity analysis, which is the first application in economics and financial studies, to segregate markets into fractally similar groups and find that developed markets have similar fractal structures. Similarly stock returns of emerging markets exhibiting long-memory tend to follow similar fractal structures. Furthermore, network analyses of fractal connectivity support our findings on market efficiency which has theoretical roots in both fractal and adaptive market hypothesis

CEO political preference and credit ratings

This study investigates whether a CEO\u27s personal political ideology, as captured by his or her political contributions, is associated with a firm\u27s credit ratings. Republican CEOs, we find, are associated with higher credit ratings, especially when their firms are headquartered in conservative areas. In addition, the link between political ideology and credit rating is more pronounced in firms that exhibit high financial distress or weak corporate governance. Changes in political ideology are associated with changes in credit rating. Our results support the behavior consistency, upper echelon, and social identity theories, as well as the risk acceptance hypothesis, and are robust to a number of alternative specifications as well as when alternate approaches and measures of credit risk are introduced. Using Republican CEOs as a proxy for conservative CEOs, our evidence implies that credit rating agencies justifiably view a CEO\u27s political ideology and conservatism as indicative of corporate policies and, therefore, as an important determinant of the firm\u27s credit ratings

CEO political ideologies and auditor-client contracting

We investigate whether CEOs’ political ideology, as captured by their political contributions, is related to audit risk and, consequently, to audit pricing. We find that Republican CEOs are associated with lower inherent risk and control risk, which represent the two components of audit risk related to the firm, while their Democratic counterparts are seen to have higher risks. Consequently, Republican (Democratic) CEOs are associated with lower (higher) audit fees. The results are robust to controlling for religiosity, executive incentives and ability, obtaining alternative measures of inherent and control risk, and to using propensity score matching and entropy balancing. We further show that changes in political ideology are associated with changes in audit risk and fees. In sum, the evidence implies that auditors view the political ideology of CEOs as an important determinant of engagement risk, which may have important implications for disclosure policy

Multivariate long memory structure in the cryptocurrency market: The impact of COVID-19

In this paper, we study the long memory behavior of Bitcoin, Litecoin, Ethereum, Ripple, Monero, and Dash with a focus on the COVID-19 period. Initially, we apply a time-varying Lifting method to estimate the Hurst exponent for each cryptocurrency. Then we test for a change in persistence over time. To model the multivariate connectivity, the wavelet-based multivariate long memory approach proposed by Achard and Gannaz (2016) is implemented. Our results indicate a change in the long-range dependence for the majority of cryptocurrencies, with a noticeable downward trend in persistence after the 2017 bubble and then a dramatic drop after the outbreak of COVID-19. The drop in persistence after COVID-19 is further illustrated by the Fractal connectivity matrix obtained from the Wavelet long-memory model. Our findings provide important implications regarding the evolution of market efficiency in the cryptocurrency market and the associated fractal structure and dynamics of the crypto prices over time. © 2022 Elsevier Inc.Ministerio de Ciencia e Innovación, MICINN: PID2020-114797GB-10