Refine
Document Type
- Working Paper (3)
- Part of a Book (2)
Language
- English (5) (remove)
Has Fulltext
- yes (5)
Is part of the Bibliography
- no (5)
Keywords
- Experiment (5) (remove)
Households regularly fail to make optimal financial decisions. But what are the underlying reasons for this? Using two conceptually distinct measures of time inconsistency based on bank account transaction data and behavioral measurement experiments, we show that the excessive use of bank account overdrafts is linked to time inconsistency. By contrast, there is no correlation between a survey-based measure of financial literacy and overdraft usage. Our results indicate that consumer education and information may not suffice to overcome mistakes in households’ financial decision-making. Rather, behaviorally motivated interventions targeting specific biases in decision-making should also be considered as effective policy tools.
We investigate the effect of the tone of news on investor stock price expectations and beliefs. In an experimental study we ask subjects to estimate a future stock price for twelve real listed companies. As additional information we provide them with historical stock prices and extracts from real newspaper articles. We propose a way to manipulate the tone of news extracts without distorting its content. Subjects in different treatment groups read news items that are written either in positive or negative tone for each stock. We find that subjects tend to predict a significantly higher (lower) return for stocks after reading positive (negative) tone news. The effect is especially pronounced for stocks with poor past performance. Subjects are more likely to be optimistic (pessimistic) about the economy and to buy (sell) stocks after reading positive (negative) than negative (positive) tone news. Our results show that the news media might affect not only how investors perceive information, but also what they do in response to it.
This paper reports the results of a corpus investigation on case conflicts in German argument free relative constructions. We investigate how corpus frequencies reflect the relative markedness of free relative and correlative constructions, the relative markedness of different case conflict configurations, and the relative markedness of different conflict resolution strategies. Section 1 introduces the conception of markedness as used in Optimality Theory. Section 2 introduces the facts about German free relative clauses, and section 3 presents the results of the corpus study. By and large, markedness and frequency go hand in hand. However, configurations at the highest end of the markedness scale rarely show up in corpus data, and for the configuration at the lowest end we found an unexpected outcome: the more marked structure is preferred.
With Big Data, decisions made by machine learning algorithms depend on training data generated by many individuals. In an experiment, we identify the effect of varying individual responsibility for the moral choices of an artificially intelligent algorithm. Across treatments, we manipulated the sources of training data and thus the impact of each individual’s decisions on the algorithm. Diffusing such individual pivotality for algorithmic choices increased the share of selfish decisions and weakened revealed prosocial preferences. This does not result from a change in the structure of incentives. Rather, our results show that Big Data offers an excuse for selfish behavior through lower responsibility for one’s and others’ fate.