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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

Introduction

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.

Personalized Nudges

Behavioral Economics incorporates psychological assumptions into the analyses of economic decision-making. The field of classical economics considers decision-making to be based on cold logic and hard facts – which human decision-making behavior does not always adhere to. Behavioral economics allows us to consider irrational behavior in decision-making and investigates the underlying reasons for these irrational choices.

Personalization in Healthcare: Diagnosis and Treatment

Personalization is disrupting products and services in every sector – be it retail, e-commerce or the quick-service industry. Therefore, it makes good sense to consider personalization in the context of healthcare delivery and services as well. The WHO has promulgated that a 1:1000 doctor-population ratio is desirable. Yet 45% of the 194 member states of the WHO do not meet this criterion; they have less than 1 physician for a population of 1000. With this abysmal doctor-patient ratio, particularly in over-populated and developing third-world countries, healthcare personalization has the potential to bring about real impact and wide-spread health benefits to patients.

Combining AI, NLP and Big Data to Monitor and Control the Spread of Epidemic Diseases

Whether it was Ebola yesterday or the coronavirus today, continuous monitoring and containment of epidemic diseases has been an on-going global challenge. These are moments when the global healthcare organizations and professionals actively collaborate to fight the threat to human life.

Resource Allocation in Epidemic Diffusion

Inventory planning, an important aspect of every industry, is the process of determining the optimal quantity and timing of an organisation’s production and delivery chain in order to ensure that an organization is able to produce and provide its goods and services without resulting in unmet demands due to shortage, or losses due to overstocking. Inventory planning has a different set of challenges in the healthcare and the pharma sector; often characterized by uncertain demand and service times. This problem of resource allocation becomes more complicated due to epidemic diffusion.

A Deep Learning Approach to Drug Discovery: Small-Molecule Design and Optimization

Historically, the field of Artificial Intelligence (AI) has gone through several cycles of initial excitement, intense hype, optimism, and promises of revolution – dubbed as AI summers; only to be followed by periods of disappointments, aptly named AI winters, in which expectations failed to materialise, and government and research funds slowly moved on to other prospects (Chauvet, 2018).

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.