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Improving financial conditions of individuals requires an understanding of the mechanisms through which bad financial decision-making leads to worse financial outcomes. From a theoretical point of view, a key candidate inducing mistakes in financial decision-making are so called present-biased preferences, which are one of the cornerstones of behavioral economics. According to theory, present-biased households should behave systematically different when it comes to consumption and saving decisions, as they should be more prone to spending too much and saving too little.
In this policy letter we show how high frequency financial transaction data available in digitized form allows to precisely categorize individual financial-decision making to be present-biased or not. Using this categorization, we find that one out of five individuals in our sample exhibits present-bias and that this present-biased behavior is associated with a stronger use of overdrafts. As overdrafts represent a particularly expensive way of short-term borrowing, their systematic use can be interpreted as a measure of suboptimal financial-decision making. Overall, our results indicate that the combination of economic theory and Big Data is able to generate valuable insights with applications for policy makers and businesses alike.
We propose a new framework for modelling time dependence in duration processes on financial markets. The well known autoregressive conditional duration (ACD) approach introduced by Engle and Russell (1998) will be extended in a way that allows the conditional expectation of the duration process to depend on an unobservable stochastic process, which is modelled via a Markov chain. The Markov switching ACD model (MSACD) is a very flexible tool for description and forecasting of financial duration processes. In addition the introduction of an unobservable, discrete valued regime variable can be justified in the light of recent market microstructure theories. In an empirical application we show, that the MSACD approach is able to capture several specific characteristics of inter trade durations while alternative ACD models fail. Furthermore, we use the MSACD to test implications of a sequential trade model.