Football Championships and Jersey Sponsors' Stock Prices: An Empirical Investigation

Corporate sports sponsorship is an important part of many companies? corporate communication strategy. We take the example of major football tournaments to show that sponsorship indeed affects the sponsor?s (stock) market value. We find a statistically significant impact of football results (at an individual game level) of the seven most important football nations at European and World Championships on the stock prices of jersey sponsors. In general, the more important a match and the less expected its result, the higher its impact. In addition, we find a form of ?mere exposure?-effect which contradicts the efficient markets hypothesis.Sports sponsorship, Advertising, Stock market efficiency

Corporate campaign contributions and abnormal stock returns after presidential elections

In the U.S. campaign contributions by companies play a major role in financing election campaigns. We analyze contributions by companies before an election and stock market performance after the election for the presidential elections from 1992 until 2004. We find that (i) the percentage of contributions given to the winner in a presidential election and (ii) the total contribution (divided by market capitalization) have a significant positive impact on a company's stock market performance after an election, with the second factor being more important. Furthermore, we find that hypothetical portfolios of the 30 highest contributors according to (i) would have earned significant abnormal returns of up to 0.54% per month (6.6% p.a.) during the first year after an election. Investing in a portfolio formed according to (ii) would have yielded abnormal returns of up to 1.21% per month (15.5% p.a.) for the same observation period.Presidential Election, Corporate Campaign Contributions, Abnormal Returns

Thar she bursts - Reducing confusion reduces bubbles

To explore why bubbles frequently emerge in the experimental asset market model of Smith, Suchanek and Williams (1988), we vary the fundamental value process (constant or declining) and the cash-to-asset value-ratio (constant or increasing). We observe high mispricing in treatments with a declining fundamental value, while overvaluation emerges when coupled with an increasing C/A-ratio. A questionnaire reveals that the declining fundamental value process confuses subjects, as they expect the fundamental value to stay constant.Running the experiment with a different context (“stocks of a depletable gold mine” instead of “stocks”) significantly reduces mispricing and overvaluation as it reduces confusion.Experimental economics, asset market, bubble, market efficiency, confusion

It is hard to beat the Monkeys - On the Value of Asymmetric Fundamental Information in Asset Markets

In this paper we present results from experimental asset markets and simulations with traders who receive asymmetric information about the fundamental value of an asset. In the experimental markets with repetition insiders outperform the market and uninformed computerized random traders (monkeys) perform equally well compared to average informed traders. This is in line with the results of the equilibrium simulation output in which traders choose between a random strategy and their fundamental strategy. We further find that pattern of average informed not being able to beat the uninformed is not due to their overconfidence but due to the asymmetric information structure of the market.Information economics, experimental economics, agent-based model, overconfidence, value of information

The value of information in a multi-agent market model

We present an experimental and simulated model of a multi-agent stock market driven by a double auction order matching mechanism. Studying the effect of cumulative information on the performance of traders, we find a non monotonic relationship of net returns of traders as a function of information levels, both in the experiments and in the simulations. Particularly, averagely informed traders perform worse than the non informed and only traders with high levels of information (insiders) are able to beat the market. The simulations and the experiments reproduce many stylized facts of stock markets, such as fast decay of autocorrelation of returns, volatility clustering and fat-tailed distribution of returns. These results have an important message for everyday life. They can give a possible explanation why, on average, professional fund managers perform worse than the market index.Comment: 11 pages, 5 figures, published in EPJ

The value of information in a multi-agent market model

We present an experimental and simulated model of a multi-agent stock market driven by a double auction order matching mechanism. Studying the effect of cumulative information on the performance of traders, we find a non monotonic relationship of net returns of traders as a function of information levels, both in the experiments and in the simulations. Particularly, averagely informed traders perform worse than the non informed and only traders with high levels of information (insiders) are able to beat the market. The simulations and the experiments reproduce many stylized facts of stock markets, such as fast decay of autocorrelation of returns, volatility clustering and fat-tailed distribution of returns. These results have an important message for everyday life. They can give a possible explanation why, on average, professional fund managers perform worse than the market index.Economics; econophysics; financial markets; business and management; information theory and communication theory

What drives risk perception? A global survey with financial professionals and laypeople

Contains fulltext : 209823.pdf (preprint version ) (Open Access)01 juli 202026 p

Non-Standard Errors

In statistics, samples are drawn from a population in a data generating process (DGP). Standard errors measure the uncertainty in sample estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence generating process (EGP). We claim that EGP variation across researchers adds uncertainty: non-standard errors. To study them, we let 164 teams test six hypotheses on the same sample. We find that non-standard errors are sizeable, on par with standard errors. Their size (i) co-varies only weakly with team merits, reproducibility, or peer rating, (ii) declines significantly after peer-feedback, and (iii) is underestimated by participants.Online appendix available at https://bit.ly/3DIQKrB.Please note a full list of authors is available in the working paper

Predicting the replicability of social science lab experiments

We measure how accurately replication of experimental results can be predicted by black-box statistical models. With data from four large-scale replication projects in experimental psychology and economics, and techniques from machine learning, we train predictive models and study which variables drive predictable replication. The models predicts binary replication with a cross-validated accuracy rate of 70% (AUC of 0.77) and estimates of relative effect sizes with a Spearman rho of 0.38. The accuracy level is similar to market-aggregated beliefs of peer scientists [1, 2]. The predictive power is validated in a pre-registered out of sample test of the outcome of [3], where 71% (AUC of 0.73) of replications are predicted correctly and effect size correlations amount to rho = 0.25. Basic features such as the sample and effect sizes in original papers, and whether reported effects are single-variable main effects or two-variable interactions, are predictive of successful replication. The models presented in this paper are simple tools to produce cheap, prognostic replicability metrics. These models could be useful in institutionalizing the process of evaluation of new findings and guiding resources to those direct replications that are likely to be most informative