Contributions in Merlin

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This paper introduces a new proprietary measure of a firm's negative impact on biodiversity, the corporate biodiversity footprint, and studies whether it is priced in the cross-section of stock returns. Using an international sample of firms, we find no evidence that the biodiversity footprint explains these returns, on average. However, event-study evidence shows that, following the UN Biodiversity Conference (COP15), which raised awareness of biodiversity issues, however, firms with larger corporate biodiversity footprints lost value. This response is consistent with investors revising their valuation of these firms downward upon the prospect that regulations to preserve biodiversity will become more stringent.

I employ data gathered by an account aggregator app covering the universe of user transactions to study the effects of three waves of stimulus checks on U.S. households’ investments, consumption, and savings. I analyze within-person variation in consumption sensitivity showing that higher liquidity and debt levels lead to smaller consumption responses. Using relative timing of stimulus and tax refunds, I illustrate the role of liquidity. Lastly, I estimate marginal propensities to invest and show a strong effect on market participation. There is a significant gender gap in the use of funds for investment.

Using unique account-level data from a high inflation period in Estonia in 2005-11 and interactive fixed effect estimation, we find individual consumption to depend on personal beyond national headline inflation. Foreign shocks to selected goods’ inflation affect disproportionately households with greater consumption basket weights on these goods and make them increase consumption by an extra 1.3% per percentage point of higher inflation exposure, financed with more net borrowing. Indebted households respond stronger, consistent with a debt depreciation effect. Resulting extra demand for goods with higher inflation can reinforce inflation, letting future inflation depend on its current distribution.

We regard accounting regulation as a politico-economic institution and analyze its evolution in the presence of changing investor sentiment. When the market sentiment is moderate, if most of the business projects in the economy are successful, the economy will enter a stable high-disclosure regime. If most of the projects are unsuccessful, the economy may enter a stable low-disclosure regime or experience a regime change from low to high disclosure. The regime change can take place if the economy coordinates on a future high disclosure quality, which will induce a Pareto improvement and is thus supported by all interest groups in the economy. In contrast, when the market sentiment is extremely hyped up, low disclosure regime will always emerge. While an initial majority of unsuccessful projects will lead the economy into a stable low-disclosure regime, an initial majority of successful projects will also result in a low disclosure regime. This regime change from high to low disclosure occurs because even successful projects benefit from low disclosure due to the significant price boost. These results are generally consistent with the observed development of accounting regulation.

We examine the inclusion of companies into so-called ‘dark green’ funds categorized as Article 9 under the European Sustainable Finance Disclosure Regulation (SFDR). While these funds should conceptually go beyond screening and ESG integration approaches and invest only in ‘sustainable investments’, our findings raise questions on fund managers’ definition of ‘sustainable investments’. Our dataset consists of 290 public equity SFDR Article 9 funds and a resulting set of 4’463 global stocks. We develop a metric of a company’s implied ‘greenness’ based on the frequency it is included in our fund sample and analyze the factors driving this greenness score. Our results provide three main findings. Firstly, our results show that the greenness score is significantly driven by efforts to improve the corporate sustainability profile, such as science-based net zero targets or human rights policies, as well as higher ESG ratings, while violations of the UN Global Compact principles or OECD guidelines for multinational enterprises have no statistically significant effect on the greenness score. Secondly, we find differences between global and regional SFDR Article 9 funds regarding corporate sustainability coefficients and sector exposure, suggesting that regional funds’ consideration of double materiality might be more limited and the share of ‘sustainable investments’ lower. Regional SFDR Article 9 funds could therefore be more likely to be declassified. Thirdly, our analysis of declassified SFDR Article 9 funds supports this prediction and highlights that the greenness score is less driven by corporate sustainability efforts when excluding declassified funds. We claim that the implementation of SFDR Article 9 poses greenwashing risks, leaving observers with the criticism of "tout ça pour ça?".

Is conventional monetary policy transmitted through the demand for and supply of intermediate goods in an economy? Analyzing unique US data on corporate linkages, we document that downstream and upstream corporate financial health are instrumental for the transmission of monetary policy. Our estimates suggest that contractionary changes in monetary conditions lead to reductions in both the demand and the supply of all financially constrained business partners, thereby creating bottlenecks, which induce the linked firms themselves to curtail their own activities ("ripple effects"). Overall, our estimates suggest that changes in monetary conditions may have a quantitatively larger impact on firms' operations through the changes in demand and supply induced by constrained business partners than through the firms' own financial conditions.

This article presents eight neural cyberattacks affecting spontaneous neural activity, inspired by well-known cyberattacks from the computer science domain: Neural Flooding, Neural Jamming, Neural Scanning, Neural Selective Forwarding, Neural Spoofing, Neural Sybil, Neural Sinkhole and Neural Nonce. These cyberattacks are based on the exploitation of vulnerabilities existing in the new generation of Brain-Computer Interfaces. After presenting their formal definitions, the cyberattacks have been implemented over a neuronal simulation. To evaluate the impact of each cyberattack, they have been implemented in a Convolutional Neural Network (CNN) simulating a portion of a mouse's visual cortex. This implementation is based on existing literature indicating the similarities that CNNs have with neuronal structures from the visual cortex. Some conclusions are also provided, indicating that Neural Nonce and Neural Jamming are the most impactful cyberattacks for short-term effects, while Neural Scanning and Neural Nonce are the most damaging for long-term effects.

This paper aims to determine why and how companies that did not start out with the intention to deceive can, nonetheless, enter a vicious circle of moral decline that may culminate in reaching a point of no return. Drawing on a qualitative analysis of the Volkswagen emissions scandal, we offer a stage-of-escalation framework that portrays the dynamics of cheating as a multistage process, whereby the function of cheating is substituted from one stage to the next. Building on behavioral ethics, social psychology, and organizational research, our analysis further explores what happens at the key points at which the company advanced to the next stage of moral decline and why those functional shifts in cheating occurred. The paper concludes by deriving (testable) propositions about factors that increase the probability of initiating, reinforcing and breaking a downward spiral.

The CCC-GARCH model, and its dynamic correlation extensions, form the most important model class for multivariate asset returns. For multivariate density and portfolio risk forecasting, a drawback of these models is the underlying assumption of Gaussianity. This paper considers the so-called COMFORT model class, which is the CCC-GARCH model but endowed with multivariate generalized hyperbolic innovations. The novelty of the model is that parameter estimation is conducted by joint maximum likelihood, of all model parameters, using an EM algorithm, and so is feasible for hundreds of assets. This paper demonstrates that (i) the new model is blatantly superior to its Gaussian counterpart in terms of forecasting ability, and (ii) also outperforms ad-hoc three step procedures common in the literature to augment the CCC and DCC models with a fat-tailed distribution. An extensive empirical study confirms the COMFORT model’s superiority in terms of multivariate density and Value-at-Risk forecasting.