Research Paper: Who Is the Next "Wolf of Wall Street"? Detection of Financial Intermediary Misconduct
Our study will be published in the Journal of the Association for Information Systems (JAIS).
The pre-final version of our paper can be downloaded here.
Financial intermediaries are essential for investors’ participation in financial markets. Due to their position within the financial system, intermediaries committing misconduct not only harm investors but also undermine trust in the financial system, which ultimately has a significant negative impact on the economy as a whole. Building upon information manipulation theory as well as warranting theory and making use of self-disclosed data with varying levels of external verification, we propose different classifiers that automatically detect financial intermediaries committing misconduct. Therefore, we focus on self-disclosed information by financial intermediaries on the business network LinkedIn. We match user profiles with regulatory-disclosed information and use this data for classifier training and evaluation. We find that self-disclosed information provides valuable input to detect financial intermediary misconduct. Regarding external verification, our classifiers achieve the best predictive performance when additionally taking regulatory-confirmed information into account. These results are supported by an economic evaluation. Our findings are highly relevant for both investors and regulators in order to identify financial intermediaries committing misconduct and thus contribute to the societal challenge of building and ensuring trust in the financial system.