Behavioural Science with Mass Transaction Data

February 1, 2018 4:00PM

Speaker: Professor Neil Stewart, Professor of Behavioural Economics, The University of Warwick

Venue: ESRI, Whitaker Square, Sir John Rogerson’s Quay, Dublin 2


Seminar Topic

Professor Stewart will discuss three papers on behavioural science with mass transaction data.

Paper 1: Gathergood, J., Mahoney, N., Stewart, N., & Weber, J. (2017). How do individuals repay their debt? The balance-matching heuristic (available at SSRN: https://ssrn.com/abstract=3000526)

We study how individuals repay their debt using linked data on multiple credit cards from five major issuers. We find that individuals do not allocate repayments to the higher interest rate card, which would minimize the cost of borrowing. Instead, individuals allocate repayments using a balance-matching heuristic under which the share of repayments on each card is matched to the share of balances on each card. We show that balance matching captures more than half of the predictable variation in repayments, performs substantially better than other models, and is highly persistent within individuals over time. Consistent with these findings, we show that machine learning algorithms attribute the greatest variable importance to balances and the least variable importance to interest rates in predicting repayment behavior.

Paper 2: Quispe-Torreblanca, E., Stewart, N., Gathergood, J., & Loewenstein, G. (2017). The red, the black, and the plastic: Paying down credit card debt for hotels not sofas (available at SSRN: https://ssrn.com/abstract=3037416)

Using transaction data from a sample of 1.8 million credit card accounts, we provide the first field test of a major prediction of Prelec and Loewenstein’s (1998) theory of mental accounting. The prediction is that consumers will pay off expenditure on transient forms of consumption more quickly than expenditure on durables. According to the theory, this is because the pain of paying can be offset by the future anticipated pleasure of consumption only when money is spent on consumption that endures over time. Consistent with the prediction, we found that repayment of debt incurred for non-durable goods is an absolute 9% more likely than repayment of debt incurred for durable goods. The size of this effect is comparable to an increment in 15 percentage points in the credit card APR.

Paper 3: Sakaguchi, H., Stewart, N., & Walasek, L. (2017). Selling winners or losers: Two-stage decision making and the disposition effect in stock trading (available at SSRN: https://ssrn.com/abstract=3053331)

Current methods for estimating the disposition effect implicitly assume that all stocks are evaluated simultaneously in a single decision stage. Here we propose a two-stage model where investors first decide whether to sell a stock in the domain of gains or losses, and only then choose a stock to sell from within their chosen domain. As evidence, we show that the probability of individual gains being sold is inversely proportional to the number of gains in the portfolio, but is not associated with the number of losses. Similarly, the probability of individual losses being sold is inversely proportional to the number of losses in the portfolio, but is not associated with the number of gains. There are two consequences for the disposition effect: First, sell decisions are about the domain of gains versus losses, not just about individual stocks. Second, current regression methods must be refined to avoid substantial bias.

Speaker Bio

Professor Neil Stewart is the Professor of Behavioural Science at Warwick Business School in the University of Warwick. He works in the field of behavioural and economic science, and applies this research to problems in the real world. He is currently working on consumer decision making using credit card transaction data, on criminal and other bad behaviour using crime and incident records, and on a mathematical model of consumer decision making called decision by sampling. He uses a mixture of laboratory experiments, field experiments, and data science techniques applied to large data sets.

About the ESRI Seminar Series

The ESRI organises a public seminar series, inviting researchers from both the ESRI and other institutions to present new research on a variety of public policy issues. The seminar series provides access to specialised knowledge and new research methodologies, with the objective of promoting research excellence and facilitating productive dialogue across the policy and research fields.

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