Benford's Law, which is the law of digit distributions, is widely applied to study fraud or bias in a data set. In this paper, we applied Benford's Law to examine the first digits of financial statement items of UK listed companies. The evidence shows that the first digits conform to Benford's...

Previous studies show mixed evidence of the role of banking expertise on the board of directors on accounting conservatism. In this paper, we add to this growing literature by providing an innovative way to measure banking expertise based on life-time working history in banks of all individual...

This paper constructs a signal-based composite index, namely ESCORE, which captures the context of earnings management. Specifically, ESCORE aggregates 15 individual signals related to earnings management based on prior relevant literature. Empirical results using UK data shows that when ESCORE...

Previous research suggests that share-financed acquirers inflate their earnings before merger and acquisition announcements. The existing literature also indicates that characteristics of chief executive officers (CEO) could affect earnings management. In this study, we extend prior studies by...

Cryptocurrencies are currently traded worldwide, with hundreds of different currencies in existence and even more on the way. This study implements some statistical and machine learning approaches for cryptocurrency investments. First, we implement GJR-GARCH over the GARCH model to estimate the...

This paper constructs a signal-based composite index, namely ESCORE, which captures the context of earnings management. Specifically, ESCORE aggregates 15 individual signals related to both accrual and real earnings management based on prior relevant literature. After establishing that ESCORE is...

This study examines the behavior of sovereign bonds to COVID-19 related news in 24 countries most affected by the COVID-19 pandemic. The study applies a continuous Hidden Markov Model (HMM) to analyze the regime shifting behavior of sovereign bonds to the news. The results show that the COVID-19...

Time series analysis of daily stock data and building predictive models are complicated. This paper presents a comparative study for stock price prediction using three different methods, namely autoregressive integrated moving average, artificial neural network, and stochastic process-geometric...

Hidden Markov model (HMM) is a powerful machine-learning method for data regime detection, especially time series data. In this paper, we establish a multi-step procedure for using HMM to select stocks from the global stock market. First, the five important factors of a stock are identified and...

The growth of last-mile delivery in developing countries is featuring the important contribution of crowdshippers. Therefore, exploring the factors affecting the intentions to stay in jobs of the crowdshippers is of great significance to maintain and sustainably develop the last-mile delivery...