Non-Standard Errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in 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 (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants

Asset returns and financial intermediary leverage : an emprical analysis

In this paper the result of Adrian, Etula, and Muir (2014) is reexamined. They propose a model with nancial intermediary leverage that is able to price a set of portfolios remarkably well. In this paper the model is estimated with di erent portfolios as test assets. This is done to account for recent critiques of the use of size and book-to-market sorted portfolios as test assets. This paper uses two new sets of portfolios, industry portfolios and portfolios sorted on size and pre-formation leverage beta. The proposed model with nancial intermediaries is not able to explain the variation of cross-sectional average returns on the two new sets of portfolios.nhhma

Informational frictions in financial markets

This thesis consists of three chapters on informational frictions in financial markets. The chapters analyze problems related to markets' ability to guide real investment, and what drives liquidity. Both problems are important to ensure efficient resource allocation in the economy. The first chapter studies the interaction between financial markets and real investments. I develop a model that simultaneously study the equilibrium in financial markets, the choice of investors to produce information, and real decisions by the firm. The chapter provides a new method to overcome non-linearities in the security price, and the equilibrium is surprisingly simple. The results provide insights into when real investments have a substantial impact on market efficiency and when we can analyze equilibrium market efficiency separately. Equilibrium behavior may hide some inefficiencies from standard empirical tests. Some changes in financial markets may increase or have little effect on market efficiency, but reduce real efficiency by increasing the cost of information production. The second chapter analyzes time-variation in liquidity. I develop a tractable model where conditions among traders vary over time. The resulting equilibrium offers several new predictions on what drives liquidity variation. For example, there may be significant reductions in liquidity from even tiny changes among the traders' conditions. Strategic behavior drives the results, and the model explains how liquidity may suddenly evaporate without a clear cause. Empirical results are in line with the predictions of the model. Surprisingly, everyone may benefit from sometimes restricting some traders from the market. Doing so can reallocate liquidity to periods with more significant liquidity needs. The third chapter studies the choice of anonymity among traders. All traders end up revealing their identity unless doing so is costly, or the order flow is noisy. The intuition is that there is always at least one trader who prefers to reveal his or her identity. If the order flow is noisy, then there is a threshold type, and more patient traders stay anonymous. The results suggest that a fully anonymous market is most efficient, but the gains from anonymity are distributed unevenly. This result explains why different markets vary significantly in choices related to anonymity

Non-Standard Errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in 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 (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants

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