C1 - Econometric and Statistical Methods and Methodology: GeneralReturn

Globally, the financial industry in the recent times has witnessed various forms of fraudulent activities in the financial markets creating dilemma for the professionals, and the auditors who owns responsibility of ensuring accuracy and transparency. This article aims at finding the emergence of Big Data technology to fraud and forensic accounting by practitioner accountants in India. A research model and hypotheses has been developed to examine the relationship between the awareness level of forensic accounting, Big Data and intentions to use it for fraud detection using structural equation modeling. Results indicate that awareness of forensic accounting has a positive influence on practitioners’ intentions to its use for fraud detection. Big data technologies mediate the relationship between awareness and intentions to use for fraud detection. The results of the study are useful in implementation of Big Data technologies into the forensic accounting domain that can facilitate combating fraud.

The paper deals with the impact of the economic agent sentiment on the return for Apple and Microsoft stocks. We employed text mining procedures to analyze Twitter messages with either negative or positive sentiment towards the chosen stock titles. Those sentiments were identified by developed algorithms which are capable of identifying sentiment towards companies and also counting the numbers of tweets in the same group. This resulted in counts of tweets with positive and negative sentiment. Then we ran analysis in order to find causality between sentiment levels and the stock price of companies. To identify causal effects we applied Granger causality tests. We found bilateral causality between the risk premium and the amount of news distributed by Twitter messages.