Understanding Behavioural Finance: A Machine Learning perspective

People in standard finance are rational. People in behavioural finance are normal.

Meir Statman, Ph.D. Machine Learning, Santa Clara University


The revolutionary work of psychologists Daniel Kahneman and Amos Tversky in the 1970s-1980s, and their research conducted over the last three decades have revealed striking insights into the intricate ways the human mind operates. This research identifies prevalent, deep-seeded, subconscious biases and heuristics present in the human decision-making process, and reveals an entirely new perspective on why we behave the way we do. This body of work, and subsequent work by other researchers, represents an entirely new field of endeavour, referred to as behavioral finance and economics. In this series of blogs, I aim to shed light on the various investor biases in behavioral finance and explain how Machine Learning techniques can help to deal with them, effectively.

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Effective Pharmacovigilance Using NLP

Pharmacovigilance (PV) is the “science and activities relating to the detection, assessment, understanding, and prevention of adverse effects or any other possible drug-related problems” (WHO, 2015). PV practices for most cases depend on analysing clinical trials, biomedical writing, observational examinations, Electronic Health Records (EHRs), social media and Spontaneous Reporting (SR). Pharmacovigilance plays a vital role in monitoring the Adverse Drug Reaction (ADR) caused due to single drug intake, combined dose as well as prolonged administration. ADR has led to an increase in the mortality rate by 1.8% throughout the world.

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