Analysis of Pre-existing Investment Behavior and Influence of Trading Apps

The last few years have seen a rise in trading apps, and Robinhood is one trading app that has attracted millennials. This paper explores trading apps such as Robinhood and their role in providing financial inclusion and safe trading opportunities. This paper discusses investment behavior in the status quo, explaining overconfidence, sociability, and the disposition effect. Investment behavior can include the behavioral biases and common notions investors utilize for trading. Furthermore, this paper assesses the design and business model of Robinhood. Five expert investors were interviewed (such as a professor and other MBA graduates from Wharton School of Business, financial experts from private equity firms in the US and Mexico, and a JP Morgan investment banking professional), and five casual investors were interviewed to understand their opinions on investment behavior, certain trading apps, common criticisms of stock trading, and solutions to these concerns. The findings led to the conclusion that investment behavior is harmful in the status quo. Results did indicate that Robinhood does promote at least some dangerous behavior through excessive active trading and is one example of a problematic trading app through the 4th Industrial Revolution, but trading apps can only amplify behavioral biases most retail investors already display.

Behavioral Biases and the Law

Empirical and experimental evidence shows that individuals exhibit behavioral biases in their decision-making processes that depart from the full rationality paradigm. This paper discusses the effectiveness of alternative debiasing strategies, designed to induce socially preferable outcomes. Following Jolls, C. and Sunstein, C.R. (2006). Debiasing through law. J. Leg. Stud. 35: 199–242, this paper examines legal strategies that aim at “debiasing through law”, attempting to reduce or eliminate boundedly rational behavior. Alternatively, policymakers can implement “insulating” legal strategies that separate the outcome from the biased behavior, without attempting to eradicate behavioral biases from the decision-making process. This paper compares these strategies in many areas, such as tort law, consumer safety law, and property law.

When Linda meets Preeti: The Validation of Behavioral Biases in India

This paper presents results from an experiment testing 10 of the core biases from the behavioral economics literature amongst two distinct ‘non-WEIRD’ (Western Educated Industrialized Rich and Democratic) population groups: low-income Indians, and university students from an elite Indian university. The study tests for both the existence of the ‘behavioral bias’ for each measure with our ‘non-WEIRD’ sample and tests for heterogeneity across the socioeconomically distinct sub-samples. We find that both sub-samples display significant 'bias' in the majority of tests and across different categories of bias, suggesting that behavioral biases are not peculiar to Western samples. We further find that the patterns of bias are the same for each sub-sample for most measures, but that there are notable exceptions for a small subset of measures. In most of these cases, the student sample, closer to typical samples for this type of research, shows stronger bias than the low-income sample.

Top Ten Behavioral Biases in Project Management: An Overview

Behavioral science has witnessed an explosion in the number of biases identified by behavioral scientists, to more than 200 at present. This article identifies the 10 most important behavioral biases for project management. First, we argue it is a mistake to equate behavioral bias with cognitive bias, as is common. Cognitive bias is half the story; political bias the other half. Second, we list the top 10 behavioral biases in project management: (1) strategic misrepresentation, (2) optimism bias, (3) uniqueness bias, (4) the planning fallacy, (5) overconfidence bias, (6) hindsight bias, (7) availability bias, (8) the base rate fallacy, (9) anchoring, and (10) escalation of commitment. Each bias is defined, and its impacts on project management are explained, with examples. Third, base rate neglect is identified as a primary reason that projects underperform. This is supported by presentation of the most comprehensive set of base rates that exist in project management scholarship, from 2,062 projects. Finally, recent findings of power law outcomes in project performance are identified as a possible first stage in discovering a general theory of project management, with more fundamental and more scientific explanations of project outcomes than found in conventional theory.