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- Liquidity (1)
- Liquidity provider incentives (1)
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- Internalizing the externalities of overfunding on crowdfunding platforms (2019)
- CROWDFUNDING PLATFORMS HAVE BECOME A VALUABLE ALTERNATIVE TO TRADITIONAL SOURCES OF FINANCING. HOWEVER, SOME PHENOMENA ON CROWDFUN DING PLATFORMS CAUSE UNDESIRABLE EXTERNAL EFFECTS THAT CAN ADVERSELY INFLUENCE THE FUNDING SUCCESS OF PROJECTS. ONE SUCH PHENOMENON IS PROJECT OVERFUNDING. IN ORDER TO INTERNALIZE THE EXTERNALITIES OF OVER FUNDING, WE PROPOSE A FUNDING REDISTRIBUTION APPROACH FOR IMPROVING OVERALL FUNDING RESULTS. TO EVALUATE THIS CONCEPT, WE DEVELOP AND DEPLOY AN AGENT-BASED MODEL.
- Enhancing market liquidity through liquidity provider incentives (2018)
- AGAINST THE BACKGROUND OF FRAGMENTED EUROPEAN EQUITIES TRADING, MARKET OPERATORS HAVE EMPLOYED DIFFERENT STRATEGIES TO INCREASE LIQUIDITY ON THEIR MARKET RELATIVE TO OTHER TRADING VENUES. ONE OF THESE STRATEGIES IS TO INCENTIVIZE LIQUIDITY PROVIDERS VIA FEE REBATES. THIS ARTICLE PRESENTS AN EMPIRICAL INVESTIGATION OF THE INTRODUCTION OF THE XETRA LIQUIDITY PROVIDER PROGRAM AT DEUTSCHE BÖRSE AND ITS IMPACT ON LIQUIDITY AND TRADING VOLUME ON THE INTRODUCING MARKET ITSELF AND ON THE CONSOLIDATED EUROPEAN MARKET.
- Liquidity provider incentives in fragmented securities markets (2018)
- We study the introduction of single-market liquidity provider incentives in fragmented securities markets. Specifically, we investigate whether fee rebates for liquidity providers enhance liquidity on the introducing market and thereby increase its competitiveness and market share. Further, we analyze whether single-market liquidity provider incentives increase overall market liquidity available for market participants. Therefore, we measure the specific liquidity contribution of individual markets to the aggregate liquidity in the fragmented market environment. While liquidity and market share of the venue introducing incentives increase, we find no significant effect for turnover and liquidity of the whole market.
- Non-standard errors (2021)
- 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.
