We examine the effect of asset redeployability on corporate misconduct and find a significant positive relationship.Utilizing a large sample of public US firms for the period of 2001 to 2015, we find that a one standard deviation (SD) increase in the proportion of redeployable assets leads to a 7.2% increase in corporate fines. We also find that the positive association between asset redeployability and corporate misconduct varies across types of misconduct and industrial heterogeneity. In our channel analysis, we find that managerial risk-taking is a potential mechanism through which asset redeployability is associated with misconduct.Additional tests reveal that corporate misconduct associated with asset redeployability leads to lower firm value. Our results remained robust in a series of sensitivity tests and continue to hold after accounting for potential endogeneity concerns. Our paper contributes to the ongoing discourse on the costs and benefits of asset redeployability.

Purpose: This research attempts to investigate the functionality of each of the five Internal control components, effectiveness of the control system and its relationship with financial performance. Methodology: Sample size for this research is 210 respondents comprising of employees from 6 Banks in Hyderabad: NBP and Sindh bank from the public sector, MCB and HBL from the private sector, Meezan bank and Bank Alfalah as Islamic banks. In this study, internal control is measured by the five components whereas financial performance is measured through three profitability ratios. Data is collected through primary as well as secondary sources. The primary source used is questionnaire taken from a combination of instruments developed by Baker, Castro, Labrena & Meyer (2005). Secondary source used are the financial statements of the sample banks for a period of four years. Return on Asset (ROA), Return on Equity (ROE), Profit expense ratio (PER) are the profitability ratios used to measure the financial performance. Data was analyzed using the Statistical Package for Social Scientists (SPSS). The statistical methods of correlation and one-way ANOVA were used for the testing of the research hypotheses. Findings: Results showed that Internal control effectiveness is strongest in private banks, followed by public banks and weakest in islamic banks, although the difference is not statistically large, but slight variation exists. Moreover, private banks had a high level of financial performance, public banks had moderate level of financial performance whereas islamic banks were found to have low financial performance. Hence it was concluded that Internal control effectiveness has a positive relationship with the Financial performance of the banks. Practical Implications: The detailed evaluation and understanding of the Internal control system effectiveness and its components provides valuable insights to managers and employees of how they can manage or improve their control systems in order to achieve greater operational as well as financial performance.

Purpose The purpose of this study is to compare five data-driven-based ML techniques to predict the time series data of Bitcoin returns, namely, alternating model tree, random forest (RF), multiple linear regression, multi-layer perceptron regression and M5 Tree algorithms. Design/methodology/approach The data used to forecast time series data of Bitcoin returns ranges from 8 July 2010 to 30 Aug 2020. This study used several predictors to predict bitcoin returns including economic policy uncertainty, equity market volatility index, S&P returns, USD/EURO exchange rates, oil and gold prices, volatilities and returns. Five statistical indexes, namely, correlation coefficient, mean absolute error, root mean square error, relative absolute error and root relative squared error are determined. The results of these metrices are used to develop colour intensity ranking. Findings Among the machine learning (ML) techniques used in this study, RF models has shown superior predictive ability for estimating the Bitcoin returns. Originality/value This study is first of its kind to use and compare ML models in the prediction of Bitcoins. More studies can be carried out by using further cryptocurrencies and other ML data-driven models in future.

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