Contributions in Merlin

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Fossil fuel investments over the past few years are at the centre of political debates about climate change policy. This article explores the role market- and bank-based debt play in the climate transition process. It presents evidence that fossil fuel firms increasingly substitute bonds for syndicated bank loans, when banks price the risk of stranded assets less than the bond market.

Many applied settings in empirical economics require estimation of a large number of individual effects, like teacher effects or location effects; in health economics, prominent examples include patient effects, doctor effects or hospital effects. Increasingly, these effects are the object of interest of the estimation, and predicted effects are often used for further descriptive and regression analyses. To avoid imposing distributional assumptions on these effects, they are typically estimated via fixed effects methods. In short panels, the conventional maximum likelihood estimator for fixed effects binary response models provides poor estimates of these individual effects since the finite sample bias is typically substantial. We present a bias-reduced fixed effects estimator that provides better estimates of the individual effects in these models by removing the first-order asymptotic bias. An additional, practical advantage of the estimator is that it provides finite predictions for all individual effects in the sample, including those for which the corresponding dependent variable has identical outcomes in all time periods over time (either all zeros or ones); for these, the maximum likelihood prediction is infinite. We illustrate the approach in simulation experiments and in an application to health care utilization.

In the aftermath of the global financial crisis and ongoing COVID-19 pandemic, investors face challenges in understanding price dynamics across assets. This paper explores the performance of the various type of machine learning algorithms (MLAs) to predict mid-price movement for Bitcoin futures prices. We use high-frequency intraday data to evaluate the relative forecasting performances across various time frequencies, ranging between 5 and 60-min. Our findings show that the average classification accuracy for five out of the six MLAs is consistently above the 50% threshold, indicating that MLAs outperform benchmark models such as ARIMA and random walk in forecasting Bitcoin futures prices. This highlights the importance and relevance of MLAs to produce accurate forecasts for bitcoin futures prices during the COVID-19 turmoil.

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Current economy and financial market globalization is pushing the China stock market open to the world. In recent years, China’s securities market regulators have introduced a variety of foreign policies to attract foreign capital. Foreign ownership can provide financial support, management experience, and technological innovation; But on the other hand, they may also bring short-term speculation and international risk which is not conducive to the company. This paper studies the relationship between foreign ownership and stock price volatility. The study period is from 2011 to 2019, and I use a fixed e↵ects model to conduct the research. Additionally, I also divide the foreign shareholder into institution and individual, use domestic institution and individual as control variables to check whether the e↵ect of di↵erent investors is di↵erent between foreigner shareholders and domestic shareholders. Furthermore, I also divide the sample period into “before stock connect” and “after stock connect” to check whether the e↵ect changes in di↵erent market condition. My result shows that foreign shareholding is significant positively correlated with companies’ stock price volatility. I also study the relationship under di↵erent market conditions and find that “after stock connect” foreign shareholding has a larger positive e↵ects on stock price volatility than previously. Finally I conduct robustness tests to further prove my results. The empirical results with interaction terms indicates that foreign ownership influences firm-level volatility via liquidity.

This paper tests the hypothesis that the tenets of the prospect theory can pre-dict the market aggregate returns. The hypothesis is tested in the 49 markets and uses both the parameters from Tversky and Kahneman (1992) as well as country specific parameters from Rieger et al. (2017). The results indicate that one cannot aggregate the results from the cross-section of stocks to the market level. Only few countries show the sign of the coeÿcient as predicted in previous works. Statistical significance is also rarely seen. The optimisation tests for the prospect theory value neither support nor reject the hypothesis stated previously. Various factors can play a role in determining the sign and significance of the coeÿcient, starting from the number of companies in the market index (concentration) and ending with the market capitalisation of the constituents (influence on the market index level). The only market where the premises of the prospect theory show negative coeÿcients and statistical significance is the small cap market. This paper contributes to the literature of behavioural factors influencing market returns and gives important impli-cations for future research in the area.